46 CFR 57.06-2 - Production test plate interval of testing.

Code of Federal Regulations, 2014 CFR

2014-10-01

... WELDING AND BRAZING Production Tests § 57.06-2 Production test plate interval of testing. (a) At least one... follows: (1) When the extent of welding on a single vessel exceeds 50 lineal feet of either or both... joint. (2) When the extent of welding on vessels welded in succession exceeds 50 lineal feet of either...

46 CFR 57.06-2 - Production test plate interval of testing.

Code of Federal Regulations, 2012 CFR

2012-10-01

... WELDING AND BRAZING Production Tests § 57.06-2 Production test plate interval of testing. (a) At least one... follows: (1) When the extent of welding on a single vessel exceeds 50 lineal feet of either or both... joint. (2) When the extent of welding on vessels welded in succession exceeds 50 lineal feet of either...

46 CFR 57.06-2 - Production test plate interval of testing.

Code of Federal Regulations, 2011 CFR

2011-10-01

... WELDING AND BRAZING Production Tests § 57.06-2 Production test plate interval of testing. (a) At least one... follows: (1) When the extent of welding on a single vessel exceeds 50 lineal feet of either or both... joint. (2) When the extent of welding on vessels welded in succession exceeds 50 lineal feet of either...

46 CFR 57.06-2 - Production test plate interval of testing.

Code of Federal Regulations, 2013 CFR

2013-10-01

... WELDING AND BRAZING Production Tests § 57.06-2 Production test plate interval of testing. (a) At least one... follows: (1) When the extent of welding on a single vessel exceeds 50 lineal feet of either or both... joint. (2) When the extent of welding on vessels welded in succession exceeds 50 lineal feet of either...

76 FR 80388 - Notice of Intent to Repatriate Cultural Items: U.S. Department of the Interior, National Park...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-12-23

... cultural items meet the definition of sacred objects and repatriation to the lineal descendant stated below... descendants of the individual who owned these sacred objects and who wish to claim the items should contact... they are lineal descendants of the individual who owned these sacred objects and who wish to claim the...

TEPC Response Functions

NASA Technical Reports Server (NTRS)

Shinn, J. L.; Wilson, J. W.

2003-01-01

The tissue equivalent proportional counter had the purpose of providing the energy absorbed from a radiation field and an estimate of the corresponding linear energy transfer (LET) for evaluation of radiation quality to convert to dose equivalent. It was the recognition of the limitations in estimating LET which lead to a new approach to dosimetry, microdosimetry, and the corresponding emphasis on energy deposit in a small tissue volume as the driver of biological response with the defined quantity of lineal energy. In many circumstances, the average of the lineal energy and LET are closely related and has provided a basis for estimating dose equivalent. Still in many cases the lineal is poorly related to LET and brings into question the usefulness as a general purpose device. These relationships are examined in this paper.

hamlet, a binary genetic switch between single- and multiple- dendrite neuron morphology.

PubMed

Moore, Adrian W; Jan, Lily Yeh; Jan, Yuh Nung

2002-08-23

The dendritic morphology of neurons determines the number and type of inputs they receive. In the Drosophila peripheral nervous system (PNS), the external sensory (ES) neurons have a single nonbranched dendrite, whereas the lineally related multidendritic (MD) neurons have extensively branched dendritic arbors. We report that hamlet is a binary genetic switch between these contrasting morphological types. In hamlet mutants, ES neurons are converted to an MD fate, whereas ectopic hamlet expression in MD precursors results in transformation of MD neurons into ES neurons. Moreover, hamlet expression induced in MD neurons undergoing dendrite outgrowth drastically reduces arbor branching.

Predictive model of third molar eruption after second molar extraction.

PubMed

De-la-Rosa-Gay, Cristina; Valmaseda-Castellón, Eduard; Gay-Escoda, Cosme

2010-03-01

Extraction of second permanent molars is an option for providing space in orthodontic treatment. Although many articles have described its impact on the outcome, there are few data on the prognosis of the eruption of the adjacent third molars. The aims of this investigation were to provide predictive models of eruption of third molars after second permanent molar extraction and to validate them. A total of 48 patients (ages, 11-23 years) who had 128 second permanent molars (54 maxillary, 74 mandibular) extracted during orthodontic treatment were followed until eruption of the third molars was complete. A lineal regression model predicted the final angle of the third molars with the permanent first molar by using the variables of initial angle, jaw, and the developmental stage of the third molar. A logistic regression model predicted the probability of correct eruption by using the variables of initial angle, jaw, sex, age, and the developmental stage of the third molar. 2010 American Association of Orthodontists. Published by Mosby, Inc. All rights reserved.

[Caesarean sections in Mexico: tendencies, levels and associated factors].

PubMed

Puentes-Rosas, Esteban; Gómez-Dantés, Octavio; Garrido-Latorre, Francisco

2004-01-01

To describe the rate of caesarean sections in Mexico in the last 10 years and evaluate its relationship with several socioeconomic variables, type of health care services, and specialists' availability. The Ministry of Health's register of births was used as source of information. The dependent variable was the type of delivery (vaginal or caesarean). The independent variables were: gross domestic product, human development index, illiteracy percentage among women, social exclusion index and, gynecology and obstetrics specialists supply. Correlations between variables were evaluated using Pearson's parametric test and Spearman range test. A lineal multiple regression was used to model the national caesarean data of 1999. National caesarean percentage increased in the last 10 years at an annual rate of 1%. It was considerably higher in social security institutions and the private sector. Caesareans percentages in 1999 were slightly above 35%. The highest values were those of the private sector with 53%, followed by social security institutions, with 38.2%. The variables more strongly associated with C sections were GDP, specialists' availability and human development index. It seems reasonable to advocate for a widespread descent in caesarean sections in Mexico. Important declines in certain contexts have been witnessed by implementing measures such as a second opinion before any C-section, a precise definition of the reasons for using it, and the monitoring of individual caesarean percentage among hospital obstetricians.

Usefulness of Patients-Reported Outcomes in Rheumatoid Arthritis Focus Group

PubMed Central

Amaya-Amaya, Jenny; Botello-Corzo, Diana; Calixto, Omar-Javier; Calderón-Rojas, Rolando; Domínguez, Aura-Maria; Cruz-Tapias, Paola; Montoya-Ortiz, Gladis; Mantilla, Ruben-Dario; Anaya, Juan-Manuel; Rojas-Villarraga, Adriana

2012-01-01

Objective. Patient-reported outcomes (PROs) have become an essential part of the assessment of patients with rheumatoid arthritis (RA). We aimed to evaluate the agreement and correlation between PROs and the physician's measurements. Methods. This was a cross-sectional analytical study in which 135 patients with RA were clinically evaluated during two different sessions of focus group interviews. Rheumatologist recorded 28 swollen (SJCs) and tender joint counts (TJCs). The patients filled out the PROs instruments (MDHAQ, RADAI, RAPID3, 4, and 5 and self-report articular index (SAI) diagram for pain and joint swelling). DAS28 was calculated (C-reactive protein). An adjusted multiple lineal regression model was done (DAS28 as dependent variable). Results. Highly significant agreements were found between SJC and TJC registered by the physician and patient. There was moderate correlation between DAS28 with patient SJC (r = 0.52), patient TJC (r = 0.55), RADAI (r = 0.56), RAPID3 (r = 0.52), RAPID4 (r = 0.56), RAPID5 (r = 0.66), and VAS-Global (r = 0.51). Likewise, we found moderate to high correlations between CDAI and SDAI with all variable measurements done by the patients. The resulting predictive equation was DAS28(CRP) = 2.02 + 0.037 × RAPID4 + 0.042× patient SJC. Conclusion. PROs applied in focus groups interview are a useful tool for managing patients with RA regardless of gender, educational level, and duration of disease. PMID:23097701

Spectroscopic and DFT study of solvent effects on the electronic absorption spectra of sulfamethoxazole in neat and binary solvent mixtures

NASA Astrophysics Data System (ADS)

Almandoz, M. C.; Sancho, M. I.; Blanco, S. E.

2014-01-01

The solvatochromic behavior of sulfamethoxazole (SMX) was investigated using UV-vis spectroscopy and DFT methods in neat and binary solvent mixtures. The spectral shifts of this solute were correlated with the Kamlet and Taft parameters (α, β and π*). Multiple lineal regression analysis indicates that both specific hydrogen-bond interaction and non specific dipolar interaction play an important role in the position of the absorption maxima in neat solvents. The simulated absorption spectra using TD-DFT methods were in good agreement with the experimental ones. Binary mixtures consist of cyclohexane (Cy)-ethanol (EtOH), acetonitrile (ACN)-dimethylsulfoxide (DMSO), ACN-dimethylformamide (DMF), and aqueous mixtures containing as co-solvents DMSO, ACN, EtOH and MeOH. Index of preferential solvation was calculated as a function of solvent composition and non-ideal characteristics are observed in all binary mixtures. In ACN-DMSO and ACN-DMF mixtures, the results show that the solvents with higher polarity and hydrogen bond donor ability interact preferentially with the solute. In binary mixtures containing water, the SMX molecules are solvated by the organic co-solvent (DMSO or EtOH) over the whole composition range. Synergistic effect is observed in the case of ACN-H2O and MeOH-H2O, indicating that at certain concentrations solvents interact to form association complexes, which should be more polar than the individual solvents of the mixture.

SU-E-T-26: A Study On the Influence of Photonuclear Reactions On the Biological Effectiveness of Therapeutic High Energy X-Ray Beam

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wakita, A; National Cancer Center Hospital, Chuo-ku, Tokyo; Matsufuji, N

2014-06-01

Purpose: Photons from a modern high-energy therapeutic linear accelerator used in X-ray radiotherapy causes photonuclear reactions in an accelerator or patient's body. The aim of this study is to evaluate the biological effectiveness including these particles by Microdosimetric Kinetic Model (MKM) based on microdosimetry. Methods: A linear accelerator operating at 15 MV was used. CR-39 was used to obtain LET spectra of secondary ions selectively, as CR-39 is regarded insensitive to photons. CR-39 was put on the central axis of the X-ray beam at depths of 0, 5 and 10 cm in plastic phantom at a source to detector distancemore » of 100 cm. Pits formed by the traversal of ions were etched then analyzed to obtain restricted LET distribution. Frequency-mean and dose-mean lineal energy was evaluated from the relationship between the restricted LET and the lineal energy required to evaluate the biological effectiveness by MKM. The relationship was calculated by Monte Carlo simulations with GEANT4. Results: Restricted LET distributions of secondary particles showed broad distributions that decreases exponentially with increasing LET. Frequency-mean and dose-mean lineal energy were determined uniquely within the scope of the energies of secondary particles generated from photons of 15 MeV. The frequency-mean lineal energies at the depth of 0, 5 and 10 cm were 15.1, 16.0 and 19.7 keV/μm respectively, and the dose-mean lineal energies were 18.6, 20.5 and 19.6 keV/μm, respectively. RBE of secondary particles for HSG cell evaluated by MKM was about 2.0 at all depths, and RBE of all particles including photons was evaluated 1.0. Conclusion: We investigated the biological effectiveness of secondary particles by photonuclear reactions. The method to evaluate RBE by MKM was established with measurements and simulations. However, the influence of these secondary ions on RBE was found negligible in the entire biological effectiveness of the high-energy X-ray. This study has been supported by JSPS KAKENHI Grant Number 25861144.« less

Fission-fusion and lineal effect: aspects of the population structure of the Semai Senoi of Malaysia.

PubMed

Fix, A G

1975-09-01

Analysis of histories and genealogies from seven relatively unacculturated, swidden-farming Semai settlements shows that the composition of local groups fluctuates through time. This instability is similar to a pattern which Neel and his colleagues have suggested is typical of primitive society, the fission-fusion model. In addition, the individuals comprising Semai fission groups are kinsmen which implies that the number of independent genomes represented is markedly less than the number of individual migrants (the lineal effect). Fission groups may form new villages or fuse with an established settlement. In either case, the genetic effects of such migration are more pronounced than would be expected on the basis of founder effect or random migration. Despite several conspicuous differences in social organization between the Semai and the South American Indians (e.g., bilateral vs. unilineal descent) whose population structure provided the empirical basis for the fission-fusion, lineal effect model, the basic similarities are striking. The Semai case thus lends support to the proposition that this pattern may be of some generality in technologically primitive populations.

Glyphosate sorption to soils of Argentina. Estimation of affinity coeficient by pedotransfer function

NASA Astrophysics Data System (ADS)

De Geronimo, Eduardo; Aparicio, Virginia; Costa, José Luis

2017-04-01

Argentine agricultural production is fundamentally based on a technological package that combines direct seeding and glyphosate with transgenic crops (soybean, maize and cotton). Therefore, glyphosate is the most employed herbicide in the country, where 180 to 200 million liters are applied every year. Glyphosate is strongly sorbed to soil by binding to clay minerals, layer silicates, metal oxides, non-crystalline materials or organic matter. Sorption of glyphosate is a reversible process that regulates the half-life and mobility of the herbicide and it is therefore related to the risk of contaminating courses of surface and groundwater. However, this behavior may vary depending on the characteristics of the soil on which it is applied. In addition, pH is a determining factor since it modifies the net charge in the molecule and, with it, the force of the electrostatic interaction between the glyphosate and the components of the soil. For a reliable risk assessment of groundwater contamination from pesticides precise predictions of sorption coefficients are needed. The aim of this work is to study the affinity of glyphosate to different soils of Argentina and create a model to estimate the glyphosate Freundlich sorption coefficient (Kf) from easily measurable soil properties. Adsorption of glyphosate was investigated on 12 different agricultural soils of Argentina using batch equilibration technique and fit to Freundlich sorption model. The correlation coefficients and the effects of soil characteristic factors on glyphosate adsorption parameter were analyzed through principal component and multiple lineal regression analysis. Results indicate that pH and clay contents were found to be the most significant soil factors which affect the glyphosate adsorption process. The Freundlich (Kf) pedotransfer function obtained by stepwise regression analysis was Kf = 735.2*Clay - 104.2*pH + 0.7*Polsen - 3.8*Alin. A 97.9% of the variation of glyphosate sorption coefficient could be attributed to the variation of the soil clay contents, pH, Polsen and Alin.

[Intake of sugar-sweetened non-alcoholic beverages and body mass index: A national sample of Chilean school children].

PubMed

Araneda, Jacqueline; Bustos, Patricia; Cerecera, Francisco; Amigo, Hugo

2015-01-01

To estimate the association between the intake of sugar-sweetened non-alcoholic beverages and body mass index (BMI) in Chilean school children. Food consumption frequency data were analyzed for school children aged 6 to 18. The association between consumption of sugar-sweetened beverages and BMI was estimated by multivariate lineal regression models. Sugar-sweetened beverages are consumed on a daily basis by 92% (95%CI:90-94) of subjects with daily intake medians of 424 mL (p25-p75:212-707). Every extra daily portion of sugar-sweetened beverages consumed by school children aged 6 to 13 is associated with 0.13 BMI z-scores (95%CI:0.04-0.2;p=0.01). School children consume sugar-sweetened beverages daily with intake medians close to 0.5L. There is an association between sugar-sweetened beverage consumption and higher BMI in Chilean school children.

Assessing trail conditions in protected areas: Application of a problem-assessment method in Great Smoky Mountains National Park, USA

USGS Publications Warehouse

Leung, Y.-F.; Marion, J.

1999-01-01

The degradation of trail resources associated with expanding recreation and tourism visitation is a growing management problem in protected areas worldwide. In order to make judicious trail and visitor management decisions, protected area managers need objective and timely information on trail resource conditions. This paper introduces a trail survey method that efficiently characterizes the lineal extent of common trail problems. The method was applied to a large sample of trails within Great Smoky Mountains National Park, a highuse protected area in the USA. The Trail ProblemAssessment Method (TPAM) employs a continuous search for multiple indicators of predefined tread problems, yielding census data documenting the location, occurrence and extent of each problem. The present application employed 23 different indicators in three categories to gather inventory, resource condition, and design and maintenance data of each surveyed trail. Seventy-two backcountry hiking trails (528 km), or 35% of the Park's total trail length, were surveyed. Soil erosion and wet soil were found to be the two most common impacts on a lineal extent basis. Trails with serious tread problems were well distributed throughout the Park, although wet muddy treads tended to be concentrated in areas where horse use was high. The effectiveness of maintenance features installed to divert water from trail treads was also evaluated. Water bars were found to be more effective than drainage dips. The TPAM was able to provide Park managers with objective and quantitative information for use in trail planning, management and maintenance decisions, and is applicable to other protected areas elsewhere with different environmental and impact characteristics.

78 FR 72711 - Notice of Intent To Repatriate Cultural Items: U.S. Department of the Interior, Bureau of Land...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-12-03

...The U.S. Department of the Interior, Bureau of Land Management (BLM), Alaska State Office, in consultation with the appropriate Indian tribes or Native Hawaiian organizations, has determined that the items listed in this notice meet the definition of unassociated funerary objects. Lineal descendants or representatives of any Indian tribe or Native Hawaiian organization not identified in this notice that wish to claim these items should submit a written request to the BLM Alaska State Office. If no additional claimants come forward, transfer of control of the items to the lineal descendants, Indian tribes, or Native Hawaiian organizations stated in this notice may proceed.

Measurements on the shuttle of the LET spectra of galactic cosmic radiation and comparison with the radiation transport model

NASA Technical Reports Server (NTRS)

Badhwar, G. D.; Cucinotta, F. A.; Braby, L. A.; Konradi, A.; Wilson, J. W. (Principal Investigator)

1994-01-01

A new class of tissue-equivalent proportional counters has been flown on two space shuttle flights. These detectors and their associated electronics cover a lineal energy range from 0.4 to 1250 keV/microns with a multichannel analyzer resolution of 0.1 keV/microns from 0.4 to 20 keV/microns and 5 keV/microns from 20 to 1250 keV/microns. These detectors provide the most complete dynamic range and highest resolution of any technique currently in use. On one mission, one detector was mounted in the Shuttle payload bay and another older model in the mid-deck, thus providing information on the depth dependence of the lineal energy spectrum. A detailed comparison of the observed lineal energy and calculated LET spectra for galactic cosmic radiation shows that, although the radiation transport models provide a rather accurate description of the dose (+/- 15%) and equivalent dose (+/- 15%), the calculations significantly underestimate the frequency of events below about 100 keV/microns. This difference cannot be explained by the inclusion of the contribution of splash protons. The contribution of the secondary pions, kaons and electrons produced in the Shuttle shielding, if included in the radiation transport model, may explain these differences. There are also significant differences between the model predictions and observations above 140 keV/microns, particularly for 28.5 degrees inclination orbit.

Dichlorodiphenyldichloroethylene Exposure during the First Trimester of Pregnancy Alters the Anal Position in Male Infants

PubMed Central

Torres-Sanchez, Luisa; Zepeda, Monica; Cebrián, Mariano E.; Belkind-Gerson, Jaime; Garcia-Hernandez, Rosa M.; Belkind-Valdovinos, Uri; López-Carrillo, Lizbeth

2016-01-01

Anogenital distance (AGD) at birth is regarded as a useful measurement that reflects the prenatal androgenic status in rodents. However, the impact of xenoantiandrogens on human development is largely unknown. The aim of this study was to evaluate the potential antiandrogenic impact of prenatal DDT metabolites (p,p′-DDE and p,p′-DDT) exposure on infant AGD, using a non-age–dependent anal position index (API). As part of an ongoing perinatal cohort study on the effects of organochlorine pesticides in children’s neurodevelopment, we conducted a cross-sectional study in 71 infants (37 males and 34 females). Maternal serum levels of DDT metabolites (p,p′-DDE and p,p′-DDT) before and during each trimester of pregnancy were determined by electron capture gas–liquid chromatography. During postnatal home visits at 3, 6, and 12 or 18 months of age, the children’s weight and API were evaluated. Multiple lineal regression models were used to estimate the potential endocrine disruptor activity of prenatal p,p′-DDE exposure. Boys had significantly higher API values than girls (0.6 versus 0.5; P < 0.001). Only among boys, a doubling increase of maternal p,p′-DDE serum levels during the first trimester of pregnancy, were associated with a significant reduction of API (β = −0.02; P = 0.02). No effect of p,p′-DDT on AGD was observed. Evidence of the effect of prenatal p,p′-DDE on external genital differentiation is scarce and not consistent in the literature. Further studies are needed to confirm a hormonal disruptive effect on the development of external genitalia, due not only to p,p′-DDE but also due to other antiandrogenic persistent compounds. PMID:18991914

Medial extrusion of the posterior segment of medial meniscus is a sensitive sign for posterior horn tears.

PubMed

Ohishi, Tsuyoshi; Suzuki, Daisuke; Yamamoto, Kazufumi; Banno, Tomohiro; Shimizu, Yuta; Matsuyama, Yukihiro

2014-01-01

To evaluate medial extrusion of the posterior segment of the medial meniscus in posterior horn tears. This study enrolled 72 patients without medial meniscal tears (group N), 72 patients with medial meniscal tears without posterior horn tears (group PH-), 44 patients with posterior horn tears of the medial meniscus (group PH+). All meniscal tears were confirmed by arthroscopy. Medial extrusion of the middle segment and the posterior segment was measured on coronal MRIs. Extrusions of both middle and posterior segments in groups PH- and PH+ (middle segment; 2.94±1.51 mm for group PH- and 3.75±1.69 mm for group PH+, posterior segment; 1.85±1.82 mm for group PH- and 4.59±2.74 mm for group PH+) were significantly larger than those in group N (middle segment; 2.04±1.20, posterior segment; 1.21±1.86). Both indicators of extrusion in group PH+ were larger than those in group PH-. In the early OA category, neither middle nor posterior segment in group PH- extruded more than in group N. However, only the posterior segment in group PH+ extruded significantly more than in group N. Multiple lineal regression analyses revealed that posterior segment extrusion was strongly correlated with the posterior horn tears (p<0.001) among groups PH- and PH+. The newly presented indicator for extrusion of the posterior segment of the medial meniscus is associated with posterior horn tears in comparison with the extrusion of the middle segment, especially in the early stages of osteoarthritis. Level II--Diagnostic Study. Copyright © 2013 Elsevier B.V. All rights reserved.

Comparison between thromboelastography and conventional coagulation test: Should we abandon conventional coagulation tests in polytrauma patients?

PubMed

Tur Martínez, Jaume; Petrone, Patrizio; Axelrad, Alexander; Marini, Corrado P

2018-05-12

TEG provides an in-vivo assessment of viscoelastic clot strength in whole blood compared with CCT, which may not reflect the influence of platelets. The aim of this study was to compare TEG vs. CCT in trauma patients stratified by mechanism of injury (MOI) and pre-existing coagulation status. A retrospective, observational study of 230 polytrauma patients admitted to a University Hospital Level 1 Trauma Center, with TEG and CCT on admission stratified by MOI: multiple trauma (MT), isolated traumatic brain injury (TBI) or MT+TBI. Statistical analysis included correlation between TEG and CCT in all groups and a subgroup analysis of anticoagulated patients. Data were analyzed with ANOVA, Spearman and lineal regression when appropriate. Statistical significance was accepted at P<0.05. TEG was normal in 28.7%, hypercoagulable in 68.3%, hypocoagulable in 7%. There was no difference in TEG status among the groups. The coagulation status was not affected by age, ISS or shock. The CCT were abnormal in 63.6% of patients with normal TEG. Normal or hypercoagulable-TEG was found in 21/23 patients on Coumadin who had elevated INR and in 10/11 patients on NOAC. An analysis of the 23 patients on Coumadin stratified by INR showed a normal or hypercoagulable-TEG in 21/23 patients. Only 2 patients had a hypocoagulable-TEG. Mortality was 5.2% (58.3% severe TBI). TEG is more useful than CCT in polytrauma patients, including patients on anticoagulants. TBI could increase the incidence of hypercoagulability in trauma. CCT are not useful from the standpoint of treatment. Copyright © 2018 AEC. Publicado por Elsevier España, S.L.U. All rights reserved.

Inbreeding coefficients and degree of consanguineous marriages in Spain: a review.

PubMed

Fuster, Vicente; Colantonio, Sonia Edith

2003-01-01

The contribution of consanguineous marriages corresponding to uncle-niece or aunt-nephew (C12), first cousin (C22), first cousin once removed (C23), and second cousin (C33) to the inbreeding coefficient (alpha) was analyzed from a sample of Spanish areas and periods. Multiple regressions were performed taking as independent variables the different degrees of consanguinity previously selected (C12, C22, C23, and C33) and as dependent variable the inbreeding coefficient (alpha). According to the results obtained for any degree and period, rural frequencies always surpass urban. However, the pattern is similar in both areas. In the period where consanguinity was more elevated (1890-1929) the C22/C33 ratio increased. Its variation is not due to C22 and C33 changes in the same way. In rural areas, this ratio surpasses the expected value by a factor of 2-3, but in urban areas it was 7-10 times larger, in some cases due to migration. While in rural Spain the C33 frequency was approximately 1.5 times C22, in cities C22 was 1.5 times C33. The best fit among the various types of consanguineous matings and alpha involves a lineal relationship. Regardless of the number of variables contributing significantly to alpha, C22 matings are always present. Moreover, their standardized (beta) coefficients are the highest. The above indicates that this consanguineous relationship conditions the inbreeding coefficient the most. In the period of greater consanguinity, close relationships, uncle-niece C12, and first cousin once removed (C23) make a significant contribution to alpha. In rural Spain second cousins (C33) always significantly determined alpha; however, in cities the inbreeding variation was mainly due to C12 and C23. Copyright 2003 Wiley-Liss, Inc.

Assessing post-fire ground cover in Mediterranean shrublands with field spectrometry and digital photography

NASA Astrophysics Data System (ADS)

Montorio Llovería, Raquel; Pérez-Cabello, Fernando; García-Martín, Alberto

2016-09-01

Fire severity can be assessed by identifying and quantifying the fractional abundance of post-fire ground cover types, an approach with great capacity to predict ecosystem response. Focused on shrubland formations of Mediterranean-type ecosystems, three burned areas (Ibieca and Zuera wildfires and Peñaflor experimental fire) were sampled in the summers of 2006 and 2007. Two different ground measurements were made for each of the 356 plots: (i) 3-band high spatial resolution photography (HSRP) and (ii) the hemispherical-conical reflectance factor (HCRF) in the visible to near-infrared spectral range (VNIR, 400-900 nm). Stepwise multiple lineal regression (SMLR) models were fitted to spectral variables (HCRF, first derivative spectra or FDS, and four absorption indices) to estimate the fractional cover of seven post-fire ground cover types (vegetation and soil - unburned and charred components - and ash - char and ash, individually and as a combined category). Models were developed and validated at the Peñaflor site (training, n = 217; validation, n = 88) and applied to the samples from the Ibieca and Zuera sites (n = 51). The best results were observed for the abundance estimations of green vegetation (Radj.20.70-0.90), unburned soil (Radj.20.40-0.75), and the combination of ashes (Radj.20.65-0.80). In comparison of spectral data, FDS outperforms reflectance or absorption data because of its higher accuracy levels and, importantly, its greater capacity to yield generalizable models. Future efforts should be made to improve the estimation of intermediate severity levels and upscaling the developed models. In the context of fire severity assessment, our study demonstrates the potential of hyperspectral data to estimate in a quick and objective manner post-fire ground cover fractions and thus provide valuable information to guide management responses.

38 CFR 38.633 - Group memorial monuments.

Code of Federal Regulations, 2013 CFR

2013-07-01

... aunts; nephews or nieces; cousins; and/or other lineal descendent. (4) Documentary evidence—Official... any governmental body, e.g., the American Battle Monuments Commission, to commemorate the dead from a...

38 CFR 38.633 - Group memorial monuments.

Code of Federal Regulations, 2012 CFR

2012-07-01

... aunts; nephews or nieces; cousins; and/or other lineal descendent. (4) Documentary evidence—Official... any governmental body, e.g., the American Battle Monuments Commission, to commemorate the dead from a...

38 CFR 38.633 - Group memorial monuments.

Code of Federal Regulations, 2011 CFR

2011-07-01

... aunts; nephews or nieces; cousins; and/or other lineal descendent. (4) Documentary evidence—Official... any governmental body, e.g., the American Battle Monuments Commission, to commemorate the dead from a...

38 CFR 38.633 - Group memorial monuments.

Code of Federal Regulations, 2010 CFR

2010-07-01

... aunts; nephews or nieces; cousins; and/or other lineal descendent. (4) Documentary evidence—Official... any governmental body, e.g., the American Battle Monuments Commission, to commemorate the dead from a...

38 CFR 38.633 - Group memorial monuments.

Code of Federal Regulations, 2014 CFR

2014-07-01

... aunts; nephews or nieces; cousins; and/or other lineal descendent. (4) Documentary evidence—Official... any governmental body, e.g., the American Battle Monuments Commission, to commemorate the dead from a...

Association of Alanine Aminotransferase Levels (ALT) with the Hepatic Insulin Resistance Index (HIRI): a cross-sectional study.

PubMed

Gómez-Sámano, Miguel Angel; Cuevas-Ramos, Daniel; Mehta, Roopa; Brau-Figueroa, Hasan; Meza-Arana, Clara Elena; Gulias-Herrero, Alfonso

2012-09-04

The association between serum alanine aminotransferase (ALT) levels and hepatic insulin resistance (IR) has been evaluated with the hyperinsulinemic-euglycemic clamp. However, there is no information about the association of ALT with the Hepatic Insulin Resistance Index (HIRI). The aim of this study was to evaluate the association between serum ALT levels and HIRI in subjects with differing degrees of impaired glucose metabolism. This cross-sectional study included subjects that had an indication for testing for type 2 diabetes mellitus (T2DM) with an oral glucose tolerance test (OGTT). Clinical and biochemical evaluations were carried out including serum ALT level quantification. HIRI was calculated for each participant. Correlation analyses and lineal regression models were used to evaluate the association between ALT levels and HIRI. A total of 324 subjects (37.6% male) were included. The mean age was 40.4 ± 14.3 years and the mean body mass index (BMI) was 32.0 ± 7.3 kg/m2. Individuals were divided into 1 of 5 groups: without metabolic abnormalities (n = 113, 34.8%); with the metabolic syndrome (MetS, n = 179, 55.2%), impaired fasting glucose (IFG, n = 85, 26.2%); impaired glucose tolerance (IGT, n = 91, 28.0%), and T2DM (n = 23, 7.0%). The ALT (p < 0.001) and HOMA2-IR (p < 0.001) values progressively increased with HIRI quartiles, while ISI-Matsuda (p < 0.001) progressively decreased. After adjustment for sex, age, and BMI, we identified a significant correlation between HIRI and ALT in persons with the MetS (r = 0.22, p = 0.003), IFG (r = 0.33, p < 0.001), IGT (r = 0.37, p < 0.001), and T2DM (r = 0.72, p < 0.001). Lineal regression analysis adjusting for age, HDL-C, TG and waist circumference (WC) showed an independent association between ALT and HIRI in subjects with the MetS (beta = 0.07, p = 0.01), IFG (beta = 0.10, p = 0.02), IGT (beta = 0.09, p = 0.007), and T2DM (beta = 0.31, p = 0.003). This association was not identified in subjects without metabolic abnormalities. ALT levels are independently associated with HIRI in subjects with the MetS, IFG, IGT, and T2DM. The ALT value in these subjects may be an indirect parameter to evaluate hepatic IR.

A Technique of Fuzzy C-Mean in Multiple Linear Regression Model toward Paddy Yield

NASA Astrophysics Data System (ADS)

Syazwan Wahab, Nur; Saifullah Rusiman, Mohd; Mohamad, Mahathir; Amira Azmi, Nur; Che Him, Norziha; Ghazali Kamardan, M.; Ali, Maselan

2018-04-01

In this paper, we propose a hybrid model which is a combination of multiple linear regression model and fuzzy c-means method. This research involved a relationship between 20 variates of the top soil that are analyzed prior to planting of paddy yields at standard fertilizer rates. Data used were from the multi-location trials for rice carried out by MARDI at major paddy granary in Peninsular Malaysia during the period from 2009 to 2012. Missing observations were estimated using mean estimation techniques. The data were analyzed using multiple linear regression model and a combination of multiple linear regression model and fuzzy c-means method. Analysis of normality and multicollinearity indicate that the data is normally scattered without multicollinearity among independent variables. Analysis of fuzzy c-means cluster the yield of paddy into two clusters before the multiple linear regression model can be used. The comparison between two method indicate that the hybrid of multiple linear regression model and fuzzy c-means method outperform the multiple linear regression model with lower value of mean square error.

43 CFR 10.1 - Purpose, applicability, and information collection.

Code of Federal Regulations, 2011 CFR

2011-10-01

... regulations develop a systematic process for determining the rights of lineal descendants and Indian tribes.... Any final determination making the Act or this part inapplicable is subject to review under section 15...

[Evaluation of continuous education: from the satisfaction to the impact. With regard to a formative programme in minor surgery in a health area].

PubMed

Martín Fernández, J; Martínez Marcos, M; Ferrándiz Santos, J

2001-04-30

To compare the evaluation of reaction of an activity of continuous education (CE) in minor surgery (MS), with the impact in the realization of MS in a health area. Observational cross-sectional study. Setting. 27 centers in a health area that offer MS between their services. The valuation was studied in a scale from 1 to 10, of 9 theoretical-practical activities of CE in MS and the consideration of its utility. The number of activities of MS (NMS) carried out in all the units, was picked up during one year, and a model of lineal regression was built. The independent variable was the NMS, and the explanatory ones the assistance pressure (AP), the postgraduate formation (PF), the staff of the unit, the equipment (E), and the carried out CE. The valuation of the CE had a median of 8 (with interquartile range 1), 85.1% of the people who realized CE in MS said that this would be of utility. However in the explanatory regression model the PF was the only significant variable (beta = 6.7; 95% CI, 0.12-12.22). Neither the CE, nor the AP, nor the E, explained the variability among the NMS. The CE in MS with conventional methodology has a very positive reaction evaluation, but its impact in the later realization of MS is not significant.

The effect of Tomm's therapeutic questioning styles on therapeutic alliance: a clinical analog study.

PubMed

Dozier, R M; Hicks, M W; Cornille, T A; Peterson, G W

1998-01-01

Tomm (1988) suggests that circular and reflexive questions tend to elicit feelings of freedom/acceptance whereas lineal and strategic questions usually trigger feelings of judgment/constraint. Employing an analog methodology, each of Tomm's four questioning styles was portrayed in the form of a 5-minute videotaped intake scenario. Forty family triads with a mother, father, and adolescent son were randomly assigned to one of four experimental conditions with ten families each. Each condition involved viewing one of four questioning style scenarios. All participants (N = 120 individuals--40 mothers, 40 fathers, and 40 adolescent sons) completed the Family Therapy Alliance Scale (FTAS; Pinsof & Catherall, 1986) and a validity-check instrument. The results indicated that circular and reflexive questioning styles elicited significantly higher (p < 0.001) alliance scores on the FTAS than did either lineal or strategic questions. Implications for the use of different types of questions in family therapy are discussed.

Identification and functional characterization of muscle satellite cells in Drosophila

PubMed Central

Reichert, Heinrich

2017-01-01

Work on genetic model systems such as Drosophila and mouse has shown that the fundamental mechanisms of myogenesis are remarkably similar in vertebrates and invertebrates. Strikingly, however, satellite cells, the adult muscle stem cells that are essential for the regeneration of damaged muscles in vertebrates, have not been reported in invertebrates. In this study, we show that lineal descendants of muscle stem cells are present in adult muscle of Drosophila as small, unfused cells observed at the surface and in close proximity to the mature muscle fibers. Normally quiescent, following muscle fiber injury, we show that these cells express Zfh1 and engage in Notch-Delta-dependent proliferative activity and generate lineal descendant populations, which fuse with the injured muscle fiber. In view of strikingly similar morphological and functional features, we consider these novel cells to be the Drosophila equivalent of vertebrate muscle satellite cells. PMID:29072161

Performance analysis of the lineal model for estimating the maximum power of a HCPV module in different climate conditions

NASA Astrophysics Data System (ADS)

Fernández, Eduardo F.; Almonacid, Florencia; Sarmah, Nabin; Mallick, Tapas; Sanchez, Iñigo; Cuadra, Juan M.; Soria-Moya, Alberto; Pérez-Higueras, Pedro

2014-09-01

A model based on easily obtained atmospheric parameters and on a simple lineal mathematical expression has been developed at the Centre of Advanced Studies in Energy and Environment in southern Spain. The model predicts the maximum power of a HCPV module as a function of direct normal irradiance, air temperature and air mass. Presently, the proposed model has only been validated in southern Spain and its performance in locations with different atmospheric conditions still remains unknown. In order to address this issue, several HCPV modules have been measured in two different locations with different climate conditions than the south of Spain: the Environment and Sustainability Institute in southern UK and the National Renewable Energy Center in northern Spain. Results show that the model has an adequate match between actual and estimated data with a RMSE lower than 3.9% at locations with different climate conditions.

Multiple Correlation versus Multiple Regression.

ERIC Educational Resources Information Center

Huberty, Carl J.

2003-01-01

Describes differences between multiple correlation analysis (MCA) and multiple regression analysis (MRA), showing how these approaches involve different research questions and study designs, different inferential approaches, different analysis strategies, and different reported information. (SLD)

The Detection and Interpretation of Interaction Effects between Continuous Variables in Multiple Regression.

ERIC Educational Resources Information Center

Jaccard, James; And Others

1990-01-01

Issues in the detection and interpretation of interaction effects between quantitative variables in multiple regression analysis are discussed. Recent discussions associated with problems of multicollinearity are reviewed in the context of the conditional nature of multiple regression with product terms. (TJH)

Monitoring lava-dome growth during the 2004-2008 Mount St. Helens, Washington, eruption using oblique terrestrial photography

USGS Publications Warehouse

Major, J.J.; Dzurisin, D.; Schilling, S.P.; Poland, Michael P.

2009-01-01

We present an analysis of lava dome growth during the 2004–2008 eruption of Mount St. Helens using oblique terrestrial images from a network of remotely placed cameras. This underutilized monitoring tool augmented more traditional monitoring techniques, and was used to provide a robust assessment of the nature, pace, and state of the eruption and to quantify the kinematics of dome growth. Eruption monitoring using terrestrial photography began with a single camera deployed at the mouth of the volcano's crater during the first year of activity. Analysis of those images indicates that the average lineal extrusion rate decayed approximately logarithmically from about 8 m/d to about 2 m/d (± 2 m/d) from November 2004 through December 2005, and suggests that the extrusion rate fluctuated on time scales of days to weeks. From May 2006 through September 2007, imagery from multiple cameras deployed around the volcano allowed determination of 3-dimensional motion across the dome complex. Analysis of the multi-camera imagery shows spatially differential, but remarkably steady to gradually slowing, motion, from about 1–2 m/d from May through October 2006, to about 0.2–1.0 m/d from May through September 2007. In contrast to the fluctuations in lineal extrusion rate documented during the first year of eruption, dome motion from May 2006 through September 2007 was monotonic (± 0.10 m/d) to gradually slowing on time scales of weeks to months. The ability to measure spatial and temporal rates of motion of the effusing lava dome from oblique terrestrial photographs provided a significant, and sometimes the sole, means of identifying and quantifying dome growth during the eruption, and it demonstrates the utility of using frequent, long-term terrestrial photography to monitor and study volcanic eruptions.

Beyond Multiple Regression: Using Commonality Analysis to Better Understand R[superscript 2] Results

ERIC Educational Resources Information Center

Warne, Russell T.

2011-01-01

Multiple regression is one of the most common statistical methods used in quantitative educational research. Despite the versatility and easy interpretability of multiple regression, it has some shortcomings in the detection of suppressor variables and for somewhat arbitrarily assigning values to the structure coefficients of correlated…

Multiplication factor versus regression analysis in stature estimation from hand and foot dimensions.

PubMed

Krishan, Kewal; Kanchan, Tanuj; Sharma, Abhilasha

2012-05-01

Estimation of stature is an important parameter in identification of human remains in forensic examinations. The present study is aimed to compare the reliability and accuracy of stature estimation and to demonstrate the variability in estimated stature and actual stature using multiplication factor and regression analysis methods. The study is based on a sample of 246 subjects (123 males and 123 females) from North India aged between 17 and 20 years. Four anthropometric measurements; hand length, hand breadth, foot length and foot breadth taken on the left side in each subject were included in the study. Stature was measured using standard anthropometric techniques. Multiplication factors were calculated and linear regression models were derived for estimation of stature from hand and foot dimensions. Derived multiplication factors and regression formula were applied to the hand and foot measurements in the study sample. The estimated stature from the multiplication factors and regression analysis was compared with the actual stature to find the error in estimated stature. The results indicate that the range of error in estimation of stature from regression analysis method is less than that of multiplication factor method thus, confirming that the regression analysis method is better than multiplication factor analysis in stature estimation. Copyright © 2012 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

49 CFR 213.309 - Restoration or renewal of track under traffic conditions.

Code of Federal Regulations, 2010 CFR

2010-10-01

... Track Classes 6 and Higher § 213.309 Restoration or renewal of track under traffic conditions. (a... more than 15 lineal feet of track are involved at any one time and the ambient air temperature is not...

Prediction of hearing outcomes by multiple regression analysis in patients with idiopathic sudden sensorineural hearing loss.

PubMed

Suzuki, Hideaki; Tabata, Takahisa; Koizumi, Hiroki; Hohchi, Nobusuke; Takeuchi, Shoko; Kitamura, Takuro; Fujino, Yoshihisa; Ohbuchi, Toyoaki

2014-12-01

This study aimed to create a multiple regression model for predicting hearing outcomes of idiopathic sudden sensorineural hearing loss (ISSNHL). The participants were 205 consecutive patients (205 ears) with ISSNHL (hearing level ≥ 40 dB, interval between onset and treatment ≤ 30 days). They received systemic steroid administration combined with intratympanic steroid injection. Data were examined by simple and multiple regression analyses. Three hearing indices (percentage hearing improvement, hearing gain, and posttreatment hearing level [HLpost]) and 7 prognostic factors (age, days from onset to treatment, initial hearing level, initial hearing level at low frequencies, initial hearing level at high frequencies, presence of vertigo, and contralateral hearing level) were included in the multiple regression analysis as dependent and explanatory variables, respectively. In the simple regression analysis, the percentage hearing improvement, hearing gain, and HLpost showed significant correlation with 2, 5, and 6 of the 7 prognostic factors, respectively. The multiple correlation coefficients were 0.396, 0.503, and 0.714 for the percentage hearing improvement, hearing gain, and HLpost, respectively. Predicted values of HLpost calculated by the multiple regression equation were reliable with 70% probability with a 40-dB-width prediction interval. Prediction of HLpost by the multiple regression model may be useful to estimate the hearing prognosis of ISSNHL. © The Author(s) 2014.

False Positives in Multiple Regression: Unanticipated Consequences of Measurement Error in the Predictor Variables

ERIC Educational Resources Information Center

Shear, Benjamin R.; Zumbo, Bruno D.

2013-01-01

Type I error rates in multiple regression, and hence the chance for false positive research findings, can be drastically inflated when multiple regression models are used to analyze data that contain random measurement error. This article shows the potential for inflated Type I error rates in commonly encountered scenarios and provides new…

Using Robust Standard Errors to Combine Multiple Regression Estimates with Meta-Analysis

ERIC Educational Resources Information Center

Williams, Ryan T.

2012-01-01

Combining multiple regression estimates with meta-analysis has continued to be a difficult task. A variety of methods have been proposed and used to combine multiple regression slope estimates with meta-analysis, however, most of these methods have serious methodological and practical limitations. The purpose of this study was to explore the use…

Use of Multiple Regression and Use-Availability Analyses in Determining Habitat Selection by Gray Squirrels (Sciurus Carolinensis)

Treesearch

John W. Edwards; Susan C. Loeb; David C. Guynn

1994-01-01

Multiple regression and use-availability analyses are two methods for examining habitat selection. Use-availability analysis is commonly used to evaluate macrohabitat selection whereas multiple regression analysis can be used to determine microhabitat selection. We compared these techniques using behavioral observations (n = 5534) and telemetry locations (n = 2089) of...

Building Regression Models: The Importance of Graphics.

ERIC Educational Resources Information Center

Dunn, Richard

1989-01-01

Points out reasons for using graphical methods to teach simple and multiple regression analysis. Argues that a graphically oriented approach has considerable pedagogic advantages in the exposition of simple and multiple regression. Shows that graphical methods may play a central role in the process of building regression models. (Author/LS)

Testing Different Model Building Procedures Using Multiple Regression.

ERIC Educational Resources Information Center

Thayer, Jerome D.

The stepwise regression method of selecting predictors for computer assisted multiple regression analysis was compared with forward, backward, and best subsets regression, using 16 data sets. The results indicated the stepwise method was preferred because of its practical nature, when the models chosen by different selection methods were similar…

Decreasing Multicollinearity: A Method for Models with Multiplicative Functions.

ERIC Educational Resources Information Center

Smith, Kent W.; Sasaki, M. S.

1979-01-01

A method is proposed for overcoming the problem of multicollinearity in multiple regression equations where multiplicative independent terms are entered. The method is not a ridge regression solution. (JKS)

Correlation between adenoma detection rate in colonoscopy- and fecal immunochemical testing-based colorectal cancer screening programs.

PubMed

Cubiella, Joaquín; Castells, Antoni; Andreu, Montserrat; Bujanda, Luis; Carballo, Fernando; Jover, Rodrigo; Lanas, Ángel; Morillas, Juan Diego; Salas, Dolores; Quintero, Enrique

2017-03-01

The adenoma detection rate (ADR) is the main quality indicator of colonoscopy. The ADR recommended in fecal immunochemical testing (FIT)-based colorectal cancer screening programs is unknown. Using the COLONPREV (NCT00906997) study dataset, we performed a post-hoc analysis to determine if there was a correlation between the ADR in primary and work-up colonoscopy, and the equivalent figure to the minimal 20% ADR recommended. Colonoscopy was performed in 5722 individuals: 5059 as primary strategy and 663 after a positive FIT result (OC-Sensor™; cut-off level 15 µg/g of feces). We developed a predictive model based on a multivariable lineal regression analysis including confounding variables. The median ADR was 31% (range, 14%-51%) in the colonoscopy group and 55% (range, 21%-83%) in the FIT group. There was a positive correlation in the ADR between primary and work-up colonoscopy (Pearson's coefficient 0.716; p  < 0.001). ADR in the FIT group was independently related to ADR in the colonoscopy group: regression coefficient for colonoscopy ADR, 0.71 ( p  = 0.009); sex, 0.09 ( p  = 0.09); age, 0.3 ( p  = 0.5); and region 0.00 ( p  = 0.9). The equivalent figure to the 20% ADR was 45% (95% confidence interval, 35%-56%). ADR in primary and work-up colonoscopy of a FIT-positive result are positively and significantly correlated.

Correlation between adenoma detection rate in colonoscopy- and fecal immunochemical testing-based colorectal cancer screening programs

PubMed Central

Castells, Antoni; Andreu, Montserrat; Bujanda, Luis; Carballo, Fernando; Jover, Rodrigo; Lanas, Ángel; Morillas, Juan Diego; Salas, Dolores; Quintero, Enrique

2016-01-01

Background The adenoma detection rate (ADR) is the main quality indicator of colonoscopy. The ADR recommended in fecal immunochemical testing (FIT)-based colorectal cancer screening programs is unknown. Methods Using the COLONPREV (NCT00906997) study dataset, we performed a post-hoc analysis to determine if there was a correlation between the ADR in primary and work-up colonoscopy, and the equivalent figure to the minimal 20% ADR recommended. Colonoscopy was performed in 5722 individuals: 5059 as primary strategy and 663 after a positive FIT result (OC-Sensor™; cut-off level 15 µg/g of feces). We developed a predictive model based on a multivariable lineal regression analysis including confounding variables. Results The median ADR was 31% (range, 14%–51%) in the colonoscopy group and 55% (range, 21%–83%) in the FIT group. There was a positive correlation in the ADR between primary and work-up colonoscopy (Pearson’s coefficient 0.716; p < 0.001). ADR in the FIT group was independently related to ADR in the colonoscopy group: regression coefficient for colonoscopy ADR, 0.71 (p = 0.009); sex, 0.09 (p = 0.09); age, 0.3 (p = 0.5); and region 0.00 (p = 0.9). The equivalent figure to the 20% ADR was 45% (95% confidence interval, 35%–56%). Conclusions ADR in primary and work-up colonoscopy of a FIT-positive result are positively and significantly correlated. PMID:28344793

Interpretation of reflection seismic data acquired for Knight Hawk Coal, LLC.

DOT National Transportation Integrated Search

2011-10-01

The Missouri University of Science and Technology geophysical crew acquired approximately 3000 lineal feet of reflection seismic : data along five separate traverses (1-5) at the PEUG South mine site. The objective was to determine if any of the trav...

Multiple-Instance Regression with Structured Data

NASA Technical Reports Server (NTRS)

Wagstaff, Kiri L.; Lane, Terran; Roper, Alex

2008-01-01

We present a multiple-instance regression algorithm that models internal bag structure to identify the items most relevant to the bag labels. Multiple-instance regression (MIR) operates on a set of bags with real-valued labels, each containing a set of unlabeled items, in which the relevance of each item to its bag label is unknown. The goal is to predict the labels of new bags from their contents. Unlike previous MIR methods, MI-ClusterRegress can operate on bags that are structured in that they contain items drawn from a number of distinct (but unknown) distributions. MI-ClusterRegress simultaneously learns a model of the bag's internal structure, the relevance of each item, and a regression model that accurately predicts labels for new bags. We evaluated this approach on the challenging MIR problem of crop yield prediction from remote sensing data. MI-ClusterRegress provided predictions that were more accurate than those obtained with non-multiple-instance approaches or MIR methods that do not model the bag structure.

Validation of the physical and RBE-weighted dose estimator based on PHITS coupled with a microdosimetric kinetic model for proton therapy.

PubMed

Takada, Kenta; Sato, Tatsuhiko; Kumada, Hiroaki; Koketsu, Junichi; Takei, Hideyuki; Sakurai, Hideyuki; Sakae, Takeji

2018-01-01

The microdosimetric kinetic model (MKM) is widely used for estimating relative biological effectiveness (RBE)-weighted doses for various radiotherapies because it can determine the surviving fraction of irradiated cells based on only the lineal energy distribution, and it is independent of the radiation type and ion species. However, the applicability of the method to proton therapy has not yet been investigated thoroughly. In this study, we validated the RBE-weighted dose calculated by the MKM in tandem with the Monte Carlo code PHITS for proton therapy by considering the complete simulation geometry of the clinical proton beam line. The physical dose, lineal energy distribution, and RBE-weighted dose for a 155 MeV mono-energetic and spread-out Bragg peak (SOBP) beam of 60 mm width were evaluated. In estimating the physical dose, the calculated depth dose distribution by irradiating the mono-energetic beam using PHITS was consistent with the data measured by a diode detector. A maximum difference of 3.1% in the depth distribution was observed for the SOBP beam. In the RBE-weighted dose validation, the calculated lineal energy distributions generally agreed well with the published measurement data. The calculated and measured RBE-weighted doses were in excellent agreement, except at the Bragg peak region of the mono-energetic beam, where the calculation overestimated the measured data by ~15%. This research has provided a computational microdosimetric approach based on a combination of PHITS and MKM for typical clinical proton beams. The developed RBE-estimator function has potential application in the treatment planning system for various radiotherapies. © The Author 2017. Published by Oxford University Press on behalf of The Japan Radiation Research Society and Japanese Society for Radiation Oncology.

Validation of the physical and RBE-weighted dose estimator based on PHITS coupled with a microdosimetric kinetic model for proton therapy

PubMed Central

Sato, Tatsuhiko; Kumada, Hiroaki; Koketsu, Junichi; Takei, Hideyuki; Sakurai, Hideyuki; Sakae, Takeji

2018-01-01

Abstract The microdosimetric kinetic model (MKM) is widely used for estimating relative biological effectiveness (RBE)-weighted doses for various radiotherapies because it can determine the surviving fraction of irradiated cells based on only the lineal energy distribution, and it is independent of the radiation type and ion species. However, the applicability of the method to proton therapy has not yet been investigated thoroughly. In this study, we validated the RBE-weighted dose calculated by the MKM in tandem with the Monte Carlo code PHITS for proton therapy by considering the complete simulation geometry of the clinical proton beam line. The physical dose, lineal energy distribution, and RBE-weighted dose for a 155 MeV mono-energetic and spread-out Bragg peak (SOBP) beam of 60 mm width were evaluated. In estimating the physical dose, the calculated depth dose distribution by irradiating the mono-energetic beam using PHITS was consistent with the data measured by a diode detector. A maximum difference of 3.1% in the depth distribution was observed for the SOBP beam. In the RBE-weighted dose validation, the calculated lineal energy distributions generally agreed well with the published measurement data. The calculated and measured RBE-weighted doses were in excellent agreement, except at the Bragg peak region of the mono-energetic beam, where the calculation overestimated the measured data by ~15%. This research has provided a computational microdosimetric approach based on a combination of PHITS and MKM for typical clinical proton beams. The developed RBE-estimator function has potential application in the treatment planning system for various radiotherapies. PMID:29087492

Measurement and simulation of lineal energy distribution at the CERN high energy facility with a tissue equivalent proportional counter.

PubMed

Rollet, S; Autischer, M; Beck, P; Latocha, M

2007-01-01

The response of a tissue equivalent proportional counter (TEPC) in a mixed radiation field with a neutron energy distribution similar to the radiation field at commercial flight altitudes has been studied. The measurements have been done at the CERN-EU High-Energy Reference Field (CERF) facility where a well-characterised radiation field is available for intercomparison. The TEPC instrument used by the ARC Seibersdorf Research is filled with pure propane gas at low pressure and can be used to determine the lineal energy distribution of the energy deposition in a mass of gas equivalent to a 2 microm diameter volume of unit density tissue, of similar size to the nuclei of biological cells. The linearity of the detector response was checked both in term of dose and dose rate. The effect of dead-time has been corrected. The influence of the detector exposure location and orientation in the radiation field on the dose distribution was also studied as a function of the total dose. The microdosimetric distribution of the absorbed dose as a function of the lineal energy has been obtained and compared with the same distribution simulated with the FLUKA Monte Carlo transport code. The dose equivalent was calculated by folding this distribution with the quality factor as a function of linear energy transfer. The comparison between the measured and simulated distributions show that they are in good agreement. As a result of this study the detector is well characterised, thanks also to the numerical simulations the instrument response is well understood, and it's currently being used onboard the aircrafts to evaluate the dose to aircraft crew caused by cosmic radiation.

Measurement of microdosimetric spectra with a wall-less tissue-equivalent proportional counter for a 290 MeV/u 12C beam.

PubMed

Tsuda, Shuichi; Sato, Tatsuhiko; Takahashi, Fumiaki; Satoh, Daiki; Endo, Akira; Sasaki, Shinichi; Namito, Yoshihito; Iwase, Hiroshi; Ban, Shuichi; Takada, Masashi

2010-09-07

The frequency distribution of the lineal energy, y, of a 290 MeV/u carbon beam was measured to obtain the dose-weighted mean of y and compare it with the linear energy transfer (LET). In the experiment, a wall-less tissue-equivalent proportional counter (TEPC) in a cylindrical volume with a simulated diameter of 0.72 microm was used. The measured frequency distribution of y as well as its dose-mean value agrees within 10% uncertainty with the corresponding data from microdosimetric calculations using the PHITS code. The ratio of the measured dose-mean lineal energy to the LET of the 290 MeV/u carbon beam is 0.73, which is much smaller than the corresponding data obtained by a wall TEPC. This result demonstrates that a wall-less TEPC is necessary to precisely measure the dose-mean of y for energetic heavy ion beams.

A Quantile Regression Approach to Understanding the Relations Between Morphological Awareness, Vocabulary, and Reading Comprehension in Adult Basic Education Students

PubMed Central

Tighe, Elizabeth L.; Schatschneider, Christopher

2015-01-01

The purpose of this study was to investigate the joint and unique contributions of morphological awareness and vocabulary knowledge at five reading comprehension levels in Adult Basic Education (ABE) students. We introduce the statistical technique of multiple quantile regression, which enabled us to assess the predictive utility of morphological awareness and vocabulary knowledge at multiple points (quantiles) along the continuous distribution of reading comprehension. To demonstrate the efficacy of our multiple quantile regression analysis, we compared and contrasted our results with a traditional multiple regression analytic approach. Our results indicated that morphological awareness and vocabulary knowledge accounted for a large portion of the variance (82-95%) in reading comprehension skills across all quantiles. Morphological awareness exhibited the greatest unique predictive ability at lower levels of reading comprehension whereas vocabulary knowledge exhibited the greatest unique predictive ability at higher levels of reading comprehension. These results indicate the utility of using multiple quantile regression to assess trajectories of component skills across multiple levels of reading comprehension. The implications of our findings for ABE programs are discussed. PMID:25351773

ℓ(p)-Norm multikernel learning approach for stock market price forecasting.

PubMed

Shao, Xigao; Wu, Kun; Liao, Bifeng

2012-01-01

Linear multiple kernel learning model has been used for predicting financial time series. However, ℓ(1)-norm multiple support vector regression is rarely observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtures that generalize well, we adopt ℓ(p)-norm multiple kernel support vector regression (1 ≤ p < ∞) as a stock price prediction model. The optimization problem is decomposed into smaller subproblems, and the interleaved optimization strategy is employed to solve the regression model. The model is evaluated on forecasting the daily stock closing prices of Shanghai Stock Index in China. Experimental results show that our proposed model performs better than ℓ(1)-norm multiple support vector regression model.

26 CFR 1.6046-1 - Returns as to organization or reorganization of foreign corporations and as to acquisitions of...

Code of Federal Regulations, 2014 CFR

2014-04-01

... as in Example 5 except that, in addition, on November 1, 2015, D donates 4% of the outstanding stock... (whether by the whole or half blood), his spouse, his ancestors, and his lineal descendants. However, when...

26 CFR 1.6046-1 - Returns as to organization or reorganization of foreign corporations and as to acquisitions of...

Code of Federal Regulations, 2012 CFR

2012-04-01

... in Example (5) except that, in addition, on November 1, 1970, D donates 2 percent of the outstanding... brothers and sisters (whether by the whole or half blood), his spouse, his ancestors, and his lineal...

26 CFR 1.6046-1 - Returns as to organization or reorganization of foreign corporations and as to acquisitions of...

Code of Federal Regulations, 2013 CFR

2013-04-01

... in Example (5) except that, in addition, on November 1, 1970, D donates 2 percent of the outstanding... brothers and sisters (whether by the whole or half blood), his spouse, his ancestors, and his lineal...

26 CFR 1.6046-1 - Returns as to organization or reorganization of foreign corporations and as to acquisitions of...

Code of Federal Regulations, 2011 CFR

2011-04-01

... in Example (5) except that, in addition, on November 1, 1970, D donates 2 percent of the outstanding... brothers and sisters (whether by the whole or half blood), his spouse, his ancestors, and his lineal...

Advanced statistics: linear regression, part II: multiple linear regression.

PubMed

Marill, Keith A

2004-01-01

The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.

Modeling Polytomous Item Responses Using Simultaneously Estimated Multinomial Logistic Regression Models

ERIC Educational Resources Information Center

Anderson, Carolyn J.; Verkuilen, Jay; Peyton, Buddy L.

2010-01-01

Survey items with multiple response categories and multiple-choice test questions are ubiquitous in psychological and educational research. We illustrate the use of log-multiplicative association (LMA) models that are extensions of the well-known multinomial logistic regression model for multiple dependent outcome variables to reanalyze a set of…

Isolating and Examining Sources of Suppression and Multicollinearity in Multiple Linear Regression

ERIC Educational Resources Information Center

Beckstead, Jason W.

2012-01-01

The presence of suppression (and multicollinearity) in multiple regression analysis complicates interpretation of predictor-criterion relationships. The mathematical conditions that produce suppression in regression analysis have received considerable attention in the methodological literature but until now nothing in the way of an analytic…

General Nature of Multicollinearity in Multiple Regression Analysis.

ERIC Educational Resources Information Center

Liu, Richard

1981-01-01

Discusses multiple regression, a very popular statistical technique in the field of education. One of the basic assumptions in regression analysis requires that independent variables in the equation should not be highly correlated. The problem of multicollinearity and some of the solutions to it are discussed. (Author)

ℓ p-Norm Multikernel Learning Approach for Stock Market Price Forecasting

PubMed Central

Shao, Xigao; Wu, Kun; Liao, Bifeng

2012-01-01

Linear multiple kernel learning model has been used for predicting financial time series. However, ℓ 1-norm multiple support vector regression is rarely observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtures that generalize well, we adopt ℓ p-norm multiple kernel support vector regression (1 ≤ p < ∞) as a stock price prediction model. The optimization problem is decomposed into smaller subproblems, and the interleaved optimization strategy is employed to solve the regression model. The model is evaluated on forecasting the daily stock closing prices of Shanghai Stock Index in China. Experimental results show that our proposed model performs better than ℓ 1-norm multiple support vector regression model. PMID:23365561

Sample size determination for logistic regression on a logit-normal distribution.

PubMed

Kim, Seongho; Heath, Elisabeth; Heilbrun, Lance

2017-06-01

Although the sample size for simple logistic regression can be readily determined using currently available methods, the sample size calculation for multiple logistic regression requires some additional information, such as the coefficient of determination ([Formula: see text]) of a covariate of interest with other covariates, which is often unavailable in practice. The response variable of logistic regression follows a logit-normal distribution which can be generated from a logistic transformation of a normal distribution. Using this property of logistic regression, we propose new methods of determining the sample size for simple and multiple logistic regressions using a normal transformation of outcome measures. Simulation studies and a motivating example show several advantages of the proposed methods over the existing methods: (i) no need for [Formula: see text] for multiple logistic regression, (ii) available interim or group-sequential designs, and (iii) much smaller required sample size.

25 CFR 163.22 - Payment for forest products.

Code of Federal Regulations, 2010 CFR

2010-04-01

...) Terms and conditions for payment of forest products under lump sum (predetermined volume) sales shall be... Forest Management and Operations § 163.22 Payment for forest products. (a) The basis of volume determination for forest products sold shall be the Scribner Decimal C log rules, cubic volume, lineal...

78 FR 44593 - Notice of Inventory Completion: Washington State Parks and Recreation Commission, Olympia, WA

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-24

... tribes or Native Hawaiian organizations, and has determined that there is a cultural affiliation between the human remains and present-day Indian tribes or Native Hawaiian organizations. Lineal descendants or representatives of any Indian tribe or Native Hawaiian organization not identified in this notice...

43 CFR 10.14 - Lineal descent and cultural affiliation.

Code of Federal Regulations, 2014 CFR

2014-10-01

... shared group identity that may be reasonably traced historically or prehistorically between a present-day... evidence sufficient to: (i) Establish the identity and cultural characteristics of the earlier group, (ii...) Evidence of the existence of a shared group identity that can be reasonably traced between the present-day...

43 CFR 10.14 - Lineal descent and cultural affiliation.

Code of Federal Regulations, 2012 CFR

2012-10-01

... shared group identity that may be reasonably traced historically or prehistorically between a present-day... evidence sufficient to: (i) Establish the identity and cultural characteristics of the earlier group, (ii...) Evidence of the existence of a shared group identity that can be reasonably traced between the present-day...

43 CFR 10.14 - Lineal descent and cultural affiliation.

Code of Federal Regulations, 2013 CFR

2013-10-01

... shared group identity that may be reasonably traced historically or prehistorically between a present-day... evidence sufficient to: (i) Establish the identity and cultural characteristics of the earlier group, (ii...) Evidence of the existence of a shared group identity that can be reasonably traced between the present-day...

78 FR 50109 - Notice of Intent To Repatriate Cultural Items: Thomas Burke Memorial Washington State Museum...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-08-16

... items listed in this notice meet the definition of unassociated funerary objects. Lineal descendants or... the control of the Burke Museum, Seattle, WA, that meet the definition of unassociated funerary... with Native American Coast Salish historic material culture. Linguistically, Native American speakers...

Incorporating Digital E-Books into Educational Curriculum

ERIC Educational Resources Information Center

Turner, Freda

2005-01-01

The first books were probably the Egyptian scrolls of papyrus that provided lineal content to readers. Today (2005) the Internet technology presents the Internet lifestyle that has introduced electronic or e-books that can enrich learning experiences. E-books have an advantage over traditional books in that they offer hypertext linking, search…

A Quantile Regression Approach to Understanding the Relations Among Morphological Awareness, Vocabulary, and Reading Comprehension in Adult Basic Education Students.

PubMed

Tighe, Elizabeth L; Schatschneider, Christopher

2016-07-01

The purpose of this study was to investigate the joint and unique contributions of morphological awareness and vocabulary knowledge at five reading comprehension levels in adult basic education (ABE) students. We introduce the statistical technique of multiple quantile regression, which enabled us to assess the predictive utility of morphological awareness and vocabulary knowledge at multiple points (quantiles) along the continuous distribution of reading comprehension. To demonstrate the efficacy of our multiple quantile regression analysis, we compared and contrasted our results with a traditional multiple regression analytic approach. Our results indicated that morphological awareness and vocabulary knowledge accounted for a large portion of the variance (82%-95%) in reading comprehension skills across all quantiles. Morphological awareness exhibited the greatest unique predictive ability at lower levels of reading comprehension whereas vocabulary knowledge exhibited the greatest unique predictive ability at higher levels of reading comprehension. These results indicate the utility of using multiple quantile regression to assess trajectories of component skills across multiple levels of reading comprehension. The implications of our findings for ABE programs are discussed. © Hammill Institute on Disabilities 2014.

Stepwise versus Hierarchical Regression: Pros and Cons

ERIC Educational Resources Information Center

Lewis, Mitzi

2007-01-01

Multiple regression is commonly used in social and behavioral data analysis. In multiple regression contexts, researchers are very often interested in determining the "best" predictors in the analysis. This focus may stem from a need to identify those predictors that are supportive of theory. Alternatively, the researcher may simply be interested…

A Method of Calculating Functional Independence Measure at Discharge from Functional Independence Measure Effectiveness Predicted by Multiple Regression Analysis Has a High Degree of Predictive Accuracy.

PubMed

Tokunaga, Makoto; Watanabe, Susumu; Sonoda, Shigeru

2017-09-01

Multiple linear regression analysis is often used to predict the outcome of stroke rehabilitation. However, the predictive accuracy may not be satisfactory. The objective of this study was to elucidate the predictive accuracy of a method of calculating motor Functional Independence Measure (mFIM) at discharge from mFIM effectiveness predicted by multiple regression analysis. The subjects were 505 patients with stroke who were hospitalized in a convalescent rehabilitation hospital. The formula "mFIM at discharge = mFIM effectiveness × (91 points - mFIM at admission) + mFIM at admission" was used. By including the predicted mFIM effectiveness obtained through multiple regression analysis in this formula, we obtained the predicted mFIM at discharge (A). We also used multiple regression analysis to directly predict mFIM at discharge (B). The correlation between the predicted and the measured values of mFIM at discharge was compared between A and B. The correlation coefficients were .916 for A and .878 for B. Calculating mFIM at discharge from mFIM effectiveness predicted by multiple regression analysis had a higher degree of predictive accuracy of mFIM at discharge than that directly predicted. Copyright © 2017 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Use of Empirical Estimates of Shrinkage in Multiple Regression: A Caution.

ERIC Educational Resources Information Center

Kromrey, Jeffrey D.; Hines, Constance V.

1995-01-01

The accuracy of four empirical techniques to estimate shrinkage in multiple regression was studied through Monte Carlo simulation. None of the techniques provided unbiased estimates of the population squared multiple correlation coefficient, but the normalized jackknife and bootstrap techniques demonstrated marginally acceptable performance with…

Enhance-Synergism and Suppression Effects in Multiple Regression

ERIC Educational Resources Information Center

Lipovetsky, Stan; Conklin, W. Michael

2004-01-01

Relations between pairwise correlations and the coefficient of multiple determination in regression analysis are considered. The conditions for the occurrence of enhance-synergism and suppression effects when multiple determination becomes bigger than the total of squared correlations of the dependent variable with the regressors are discussed. It…

The association of AKNA gene polymorphisms with knee osteoarthritis suggests the relevance of this immune response regulator in the disease genetic susceptibility.

PubMed

Martínez-Nava, Gabriela Angélica; Fernández-Torres, Javier; Martínez-Flores, Karina; Zamudio-Cuevas, Yessica; Clavijo-Cornejo, Denise; Espinosa-Morales, Rolando; Lozada, Carlos A; Gutierrez, Marwin; Granados, Julio; Pineda, Carlos; Madrid-Marina, Vicente; López-Reyes, Alberto

2018-04-01

Recent studies have identified AKNA as a potential susceptibility gene for several inflammatory diseases. Here, we aimed to assess the potential association of AKNA polymorphisms with knee osteoarthritis (KOA) susceptibility in a Mexican population, following STREGA recommendations. From a DNA bank of 181 KOA patients and 140 healthy controls, two AKNA SNPs were genotyped using TaqMan probes. The association between KOA susceptibility and AKNA polymorphisms genotypes was evaluated by multivariated logistic regression analysis. Information regarding patients' inflammatory biomarkers levels was obtained and their association with AKNA polymorphisms genotypes was assessed by lineal regression. We found a positive association with the recessive inheritance model of both AKNA polymorphisms (A/A genotype for both) and KOA susceptibility adjusting by age, body mass index (BMI), gender and place of birth (OR = 2.48, 95% CI 1.09-5.65 for rs10817595 polymorphism; and OR = 4.96; 95% CI 2.421-10.2 for rs3748176 polymorphism). Additionally these associations were also seen after stratifying patients by KOA severity and age. Furthermore the total leukocyte count was positively associated with rs10817595 AKNA polymorphism (β = 1.39; 95% CI 0.44-2.34) adjusting by age, BMI, gender, place of birth and disease severity. We suggest that regulatory and coding polymorphisms of the inflammatory modulator gene AKNA can influence the development of KOA. Further structural and functional studies might reveal the role of AKNA in OA and other rheumatic diseases.

An Effect Size for Regression Predictors in Meta-Analysis

ERIC Educational Resources Information Center

Aloe, Ariel M.; Becker, Betsy Jane

2012-01-01

A new effect size representing the predictive power of an independent variable from a multiple regression model is presented. The index, denoted as r[subscript sp], is the semipartial correlation of the predictor with the outcome of interest. This effect size can be computed when multiple predictor variables are included in the regression model…

Regression Analysis: Legal Applications in Institutional Research

ERIC Educational Resources Information Center

Frizell, Julie A.; Shippen, Benjamin S., Jr.; Luna, Andrew L.

2008-01-01

This article reviews multiple regression analysis, describes how its results should be interpreted, and instructs institutional researchers on how to conduct such analyses using an example focused on faculty pay equity between men and women. The use of multiple regression analysis will be presented as a method with which to compare salaries of…

RAWS II: A MULTIPLE REGRESSION ANALYSIS PROGRAM,

DTIC Science & Technology

This memorandum gives instructions for the use and operation of a revised version of RAWS, a multiple regression analysis program. The program...of preprocessed data, the directed retention of variable, listing of the matrix of the normal equations and its inverse, and the bypassing of the regression analysis to provide the input variable statistics only. (Author)

Incremental Net Effects in Multiple Regression

ERIC Educational Resources Information Center

Lipovetsky, Stan; Conklin, Michael

2005-01-01

A regular problem in regression analysis is estimating the comparative importance of the predictors in the model. This work considers the 'net effects', or shares of the predictors in the coefficient of the multiple determination, which is a widely used characteristic of the quality of a regression model. Estimation of the net effects can be a…

Floating Data and the Problem with Illustrating Multiple Regression.

ERIC Educational Resources Information Center

Sachau, Daniel A.

2000-01-01

Discusses how to introduce basic concepts of multiple regression by creating a large-scale, three-dimensional regression model using the classroom walls and floor. Addresses teaching points that should be covered and reveals student reaction to the model. Finds that the greatest benefit of the model is the low fear, walk-through, nonmathematical…

Using Multiple and Logistic Regression to Estimate the Median WillCost and Probability of Cost and Schedule Overrun for Program Managers

DTIC Science & Technology

2017-03-23

PUBLIC RELEASE; DISTRIBUTION UNLIMITED Using Multiple and Logistic Regression to Estimate the Median Will- Cost and Probability of Cost and... Cost and Probability of Cost and Schedule Overrun for Program Managers Ryan C. Trudelle Follow this and additional works at: https://scholar.afit.edu...afit.edu. Recommended Citation Trudelle, Ryan C., "Using Multiple and Logistic Regression to Estimate the Median Will- Cost and Probability of Cost and

78 FR 21403 - Notice of Inventory Completion: San Francisco State University NAGPRA Program, San Francisco, CA

Federal Register 2010, 2011, 2012, 2013, 2014

2013-04-10

... consultation with the appropriate Indian tribes or Native Hawaiian organizations, and has determined that there... Indian tribes or Native Hawaiian organizations. Lineal descendants or representatives of any Indian tribe or Native Hawaiian organization not identified in this notice that wish to request transfer of...

78 FR 59956 - Notice of Inventory Completion: Thomas Burke Memorial Washington State Museum, University of...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-09-30

... organizations, and have determined that there is a cultural affiliation between the human remains and associated funerary objects and present-day Indian tribes or Native Hawaiian organizations. Lineal descendants or representatives of any Indian tribe or Native Hawaiian organization not identified in this notice that wish to...

78 FR 59963 - Notice of Inventory Completion: Thomas Burke Memorial Washington State Museum, University of...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-09-30

... organizations, and have determined that there is a cultural affiliation between the human remains and associated funerary objects and present-day Indian tribes or Native Hawaiian organizations. Lineal descendants or representatives of any Indian tribe or Native Hawaiian organization not identified in this notice that wish to...

78 FR 59954 - Notice of Inventory Completion: Michigan State Police, Mount Pleasant Post, Mount Pleasant, MI

Federal Register 2010, 2011, 2012, 2013, 2014

2013-09-30

... tribes or Native Hawaiian organizations, and has determined that there is a cultural affiliation between the human remains and present- day Indian tribes or Native Hawaiian organizations. Lineal descendants or representatives of any Indian tribe or Native Hawaiian organization not identified in this notice...

Physical Activity, Physical Fitness and Academic Achievement in Adolescents: A Self-Organizing Maps Approach

ERIC Educational Resources Information Center

Pellicer-Chenoll, Maite; Garcia-Massó, Xavier; Morales, Jose; Serra-Añó, Pilar; Solana-Tramunt, Mònica; González, Luis-Millán; Toca-Herrera, José-Luis

2015-01-01

The relationship among physical activity, physical fitness and academic achievement in adolescents has been widely studied; however, controversy concerning this topic persists. The methods used thus far to analyse the relationship between these variables have included mostly traditional lineal analysis according to the available literature. The…

78 FR 36243 - Notice of Intent To Repatriate Cultural Items: University of Oregon Museum of Natural and...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-06-17

... Cultural History, Eugene, OR AGENCY: National Park Service, Interior. ACTION: Notice. SUMMARY: The University of Oregon Museum of Natural and Cultural History, in consultation with the appropriate Indian... History. If no additional claimants come forward, transfer of control of the cultural items to the lineal...

Terminus Amnesia: Cherokee Freedmen, Citizenship, and Education

ERIC Educational Resources Information Center

Chin, Jeremiah; Bustamante, Nicholas; Solyom, Jessica Ann; Brayboy, Bryan McKinley Jones

2016-01-01

In 2007, the Cherokee Nation of Oklahoma amended its constitution to limit membership to only those who can trace lineal descent to an individual listed as "Cherokee by Blood" on the final Dawes Rolls. This exercise of sovereignty paradoxically ties the Dawes Rolls, the colonial instruments used to divide the lands and peoples of the…

78 FR 34130 - Notice of Intent to Repatriate Cultural Items: The Field Museum, Chicago, IL

Federal Register 2010, 2011, 2012, 2013, 2014

2013-06-06

... notice meet the definition of unassociated funerary objects. Lineal descendants or representatives of any... items in the possession of the Field Museum, Chicago, IL, that meet the definition of unassociated..., mortuary practices, ceramic types, and other items of material culture at this ruin are consistent with the...

26 CFR 26.2612-1 - Definitions.

Code of Federal Regulations, 2012 CFR

2012-04-01

... person by reason of a termination occurring on the death of a lineal descendant of the transferor, the... portion of GST tax (including penalties and interest thereon) imposed on a distributee is paid from the...'s child, C, for life and, on C's death, the trust principal is to be paid to T's descendants. Under...

26 CFR 26.2612-1 - Definitions.

Code of Federal Regulations, 2010 CFR

2010-04-01

... person by reason of a termination occurring on the death of a lineal descendant of the transferor, the... portion of GST tax (including penalties and interest thereon) imposed on a distributee is paid from the...'s child, C, for life and, on C's death, the trust principal is to be paid to T's descendants. Under...

26 CFR 26.2612-1 - Definitions.

Code of Federal Regulations, 2011 CFR

2011-04-01

... person by reason of a termination occurring on the death of a lineal descendant of the transferor, the... portion of GST tax (including penalties and interest thereon) imposed on a distributee is paid from the...'s child, C, for life and, on C's death, the trust principal is to be paid to T's descendants. Under...

Radial dependence of lineal energy distribution of 290-MeV/u carbon and 500-MeV/u iron ion beams using a wall-less tissue-equivalent proportional counter

PubMed Central

Tsuda, Shuichi; Sato, Tatsuhiko; Watanabe, Ritsuko; Takada, Masashi

2015-01-01

Using a wall-less tissue-equivalent proportional counter for a 0.72-μm site in tissue, we measured the radial dependence of the lineal energy distribution, yf(y), of 290-MeV/u carbon ions and 500-MeV/u iron ion beams. The measured yf(y) distributions and the dose-mean of y, y¯D, were compared with calculations performed with the track structure simulation code TRACION and the microdosimetric function of the Particle and Heavy Ion Transport code System (PHITS). The values of the measured y¯D were consistent with calculated results within an error of 2%, but differences in the shape of yf(y) were observed for iron ion irradiation. This result indicates that further improvement of the calculation model for yf(y) distribution in PHITS is needed for the analytical function that describes energy deposition by delta rays, particularly for primary ions having linear energy transfer in excess of a few hundred keV μm−1. PMID:25210053

Systematic measurement of lineal energy distributions for proton, He and Si ion beams over a wide energy range using a wall-less tissue equivalent proportional counter.

PubMed

Tsuda, Shuichi; Sato, Tatsuhiko; Takahashi, Fumiaki; Satoh, Daiki; Sasaki, Shinichi; Namito, Yoshihito; Iwase, Hiroshi; Ban, Shuichi; Takada, Masashi

2012-01-01

The frequency distributions of the lineal energy, y, of 160 MeV proton, 150 MeV/u helium, and 490 MeV/u silicon ion beams were measured using a wall-less tissue equivalent proportional counter (TEPC) with a site size of 0.72 µm. The measured frequency distributions of y as well as the dose-mean values, y(D), agree with the corresponding data calculated using the microdosimetric function of the particle and heavy ion transport code system PHITS. The values of y(D) increase in the range of LET below ~10 keV µm(-1) because of discrete energy deposition by delta rays, while the relation is reversed above ~10 keV µm(-1) as the amount of energy escaping via delta rays increases. These results indicate that care should be taken with the difference between y(D) and LET when estimating the ionization density that usually relates to relative biological effectiveness (RBE) of energetic heavy ions.

A study of the differential effects of Tomm's questioning styles on therapeutic alliance.

PubMed

Ryan, D; Carr, A

2001-01-01

To replicate and extend Dozier's (1992) test of Tomm's hypothesis about the differential effects of questioning styles on therapeutic alliance, an analogue study was conducted. Twenty-eight family triads, each including a son and his parents, viewed four videotaped, simulated family therapy scenarios in which Tomm's four questioning styles were separately portrayed. Participants were asked to identify with the client whose role corresponded to theirs (that is, father, mother, or son) and, on the basis of this, to rate the client's alliance with the therapist. They were also asked to rate the overall alliance between the family and the therapist. Finally, having viewed all four scenarios, they were invited to rate comparatively the quality of the therapeutic alliance across the four questioning styles. Compared with strategic and lineal questioning styles, circular and reflexive questions led to higher ratings of therapeutic alliance on all three measures. The results of this study support Tomm's hypothesis that questioning styles based on circular assumptions lead to a better therapeutic alliance at an individual and systemic level than do questions based on lineal assumptions.

En1 directs superior olivary complex neuron positioning, survival, and expression of FoxP1.

PubMed

Altieri, Stefanie C; Jalabi, Walid; Zhao, Tianna; Romito-DiGiacomo, Rita R; Maricich, Stephen M

2015-12-01

Little is known about the genetic pathways and transcription factors that control development and maturation of central auditory neurons. En1, a gene expressed by a subset of developing and mature superior olivary complex (SOC) cells, encodes a homeodomain transcription factor important for neuronal development in the midbrain, cerebellum, hindbrain and spinal cord. Using genetic fate-mapping techniques, we show that all En1-lineal cells in the SOC are neurons and that these neurons are glycinergic, cholinergic and GABAergic in neurotransmitter phenotype. En1 deletion does not interfere with specification or neural fate of these cells, but does cause aberrant positioning and subsequent death of all En1-lineal SOC neurons by early postnatal ages. En1-null cells also fail to express the transcription factor FoxP1, suggesting that FoxP1 lies downstream of En1. Our data define important roles for En1 in the development and maturation of a diverse group of brainstem auditory neurons. Copyright © 2015 Elsevier Inc. All rights reserved.

Microdosimetry of the full slowing down of protons using Monte Carlo track structure simulations.

PubMed

Liamsuwan, T; Uehara, S; Nikjoo, H

2015-09-01

The article investigates two approaches in microdosimetric calculations based on Monte Carlo track structure (MCTS) simulations of a 160-MeV proton beam. In the first approach, microdosimetric parameters of the proton beam were obtained using the weighted sum of proton energy distributions and microdosimetric parameters of proton track segments (TSMs). In the second approach, phase spaces of energy depositions obtained using MCTS simulations in the full slowing down (FSD) mode were used for the microdosimetric calculations. Targets of interest were water cylinders of 2.3-100 nm in diameters and heights. Frequency-averaged lineal energies ([Formula: see text]) obtained using both approaches agreed within the statistical uncertainties. Discrepancies beyond this level were observed for dose-averaged lineal energies ([Formula: see text]) towards the Bragg peak region due to the small number of proton energies used in the TSM approach and different energy deposition patterns in the TSM and FSD of protons. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Tools to Support Interpreting Multiple Regression in the Face of Multicollinearity

PubMed Central

Kraha, Amanda; Turner, Heather; Nimon, Kim; Zientek, Linda Reichwein; Henson, Robin K.

2012-01-01

While multicollinearity may increase the difficulty of interpreting multiple regression (MR) results, it should not cause undue problems for the knowledgeable researcher. In the current paper, we argue that rather than using one technique to investigate regression results, researchers should consider multiple indices to understand the contributions that predictors make not only to a regression model, but to each other as well. Some of the techniques to interpret MR effects include, but are not limited to, correlation coefficients, beta weights, structure coefficients, all possible subsets regression, commonality coefficients, dominance weights, and relative importance weights. This article will review a set of techniques to interpret MR effects, identify the elements of the data on which the methods focus, and identify statistical software to support such analyses. PMID:22457655

Tools to support interpreting multiple regression in the face of multicollinearity.

PubMed

Kraha, Amanda; Turner, Heather; Nimon, Kim; Zientek, Linda Reichwein; Henson, Robin K

2012-01-01

While multicollinearity may increase the difficulty of interpreting multiple regression (MR) results, it should not cause undue problems for the knowledgeable researcher. In the current paper, we argue that rather than using one technique to investigate regression results, researchers should consider multiple indices to understand the contributions that predictors make not only to a regression model, but to each other as well. Some of the techniques to interpret MR effects include, but are not limited to, correlation coefficients, beta weights, structure coefficients, all possible subsets regression, commonality coefficients, dominance weights, and relative importance weights. This article will review a set of techniques to interpret MR effects, identify the elements of the data on which the methods focus, and identify statistical software to support such analyses.

The comparison between several robust ridge regression estimators in the presence of multicollinearity and multiple outliers

NASA Astrophysics Data System (ADS)

Zahari, Siti Meriam; Ramli, Norazan Mohamed; Moktar, Balkiah; Zainol, Mohammad Said

2014-09-01

In the presence of multicollinearity and multiple outliers, statistical inference of linear regression model using ordinary least squares (OLS) estimators would be severely affected and produces misleading results. To overcome this, many approaches have been investigated. These include robust methods which were reported to be less sensitive to the presence of outliers. In addition, ridge regression technique was employed to tackle multicollinearity problem. In order to mitigate both problems, a combination of ridge regression and robust methods was discussed in this study. The superiority of this approach was examined when simultaneous presence of multicollinearity and multiple outliers occurred in multiple linear regression. This study aimed to look at the performance of several well-known robust estimators; M, MM, RIDGE and robust ridge regression estimators, namely Weighted Ridge M-estimator (WRM), Weighted Ridge MM (WRMM), Ridge MM (RMM), in such a situation. Results of the study showed that in the presence of simultaneous multicollinearity and multiple outliers (in both x and y-direction), the RMM and RIDGE are more or less similar in terms of superiority over the other estimators, regardless of the number of observation, level of collinearity and percentage of outliers used. However, when outliers occurred in only single direction (y-direction), the WRMM estimator is the most superior among the robust ridge regression estimators, by producing the least variance. In conclusion, the robust ridge regression is the best alternative as compared to robust and conventional least squares estimators when dealing with simultaneous presence of multicollinearity and outliers.

An improved multiple linear regression and data analysis computer program package

NASA Technical Reports Server (NTRS)

Sidik, S. M.

1972-01-01

NEWRAP, an improved version of a previous multiple linear regression program called RAPIER, CREDUC, and CRSPLT, allows for a complete regression analysis including cross plots of the independent and dependent variables, correlation coefficients, regression coefficients, analysis of variance tables, t-statistics and their probability levels, rejection of independent variables, plots of residuals against the independent and dependent variables, and a canonical reduction of quadratic response functions useful in optimum seeking experimentation. A major improvement over RAPIER is that all regression calculations are done in double precision arithmetic.

Variables Associated with Communicative Participation in People with Multiple Sclerosis: A Regression Analysis

ERIC Educational Resources Information Center

Baylor, Carolyn; Yorkston, Kathryn; Bamer, Alyssa; Britton, Deanna; Amtmann, Dagmar

2010-01-01

Purpose: To explore variables associated with self-reported communicative participation in a sample (n = 498) of community-dwelling adults with multiple sclerosis (MS). Method: A battery of questionnaires was administered online or on paper per participant preference. Data were analyzed using multiple linear backward stepwise regression. The…

Predictors of postoperative outcomes of cubital tunnel syndrome treatments using multiple logistic regression analysis.

PubMed

Suzuki, Taku; Iwamoto, Takuji; Shizu, Kanae; Suzuki, Katsuji; Yamada, Harumoto; Sato, Kazuki

2017-05-01

This retrospective study was designed to investigate prognostic factors for postoperative outcomes for cubital tunnel syndrome (CubTS) using multiple logistic regression analysis with a large number of patients. Eighty-three patients with CubTS who underwent surgeries were enrolled. The following potential prognostic factors for disease severity were selected according to previous reports: sex, age, type of surgery, disease duration, body mass index, cervical lesion, presence of diabetes mellitus, Workers' Compensation status, preoperative severity, and preoperative electrodiagnostic testing. Postoperative severity of disease was assessed 2 years after surgery by Messina's criteria which is an outcome measure specifically for CubTS. Bivariate analysis was performed to select candidate prognostic factors for multiple linear regression analyses. Multiple logistic regression analysis was conducted to identify the association between postoperative severity and selected prognostic factors. Both bivariate and multiple linear regression analysis revealed only preoperative severity as an independent risk factor for poor prognosis, while other factors did not show any significant association. Although conflicting results exist regarding prognosis of CubTS, this study supports evidence from previous studies and concludes early surgical intervention portends the most favorable prognosis. Copyright © 2017 The Japanese Orthopaedic Association. Published by Elsevier B.V. All rights reserved.

The Geometry of Enhancement in Multiple Regression

ERIC Educational Resources Information Center

Waller, Niels G.

2011-01-01

In linear multiple regression, "enhancement" is said to occur when R[superscript 2] = b[prime]r greater than r[prime]r, where b is a p x 1 vector of standardized regression coefficients and r is a p x 1 vector of correlations between a criterion y and a set of standardized regressors, x. When p = 1 then b [is congruent to] r and…

Advanced statistics: linear regression, part I: simple linear regression.

PubMed

Marill, Keith A

2004-01-01

Simple linear regression is a mathematical technique used to model the relationship between a single independent predictor variable and a single dependent outcome variable. In this, the first of a two-part series exploring concepts in linear regression analysis, the four fundamental assumptions and the mechanics of simple linear regression are reviewed. The most common technique used to derive the regression line, the method of least squares, is described. The reader will be acquainted with other important concepts in simple linear regression, including: variable transformations, dummy variables, relationship to inference testing, and leverage. Simplified clinical examples with small datasets and graphic models are used to illustrate the points. This will provide a foundation for the second article in this series: a discussion of multiple linear regression, in which there are multiple predictor variables.

78 FR 72710 - Notice of Intent To Repatriate Cultural Items: Denver Art Museum, Denver, CO

Federal Register 2010, 2011, 2012, 2013, 2014

2013-12-03

... this notice meet the definition of sacred objects and objects of cultural patrimony. Lineal descendants... definition of sacred objects and objects of cultural patrimony under 25 U.S.C. 3001. This notice is published... objects of cultural patrimony and sacred objects. The review of available documentation, in addition to...

Stream protection with small cable yarding systems

Treesearch

Penn A. Peters; Chris B. LeDoux

1984-01-01

Small cable yarder systems that can be purchased and operated by independent logging contractors have less potential negative impact on water quality than ground-based systems operating on steep terrain because they do not require such an intense road system. Stream protection costs were estimated at $3.78 per lineal foot of stream when a typical small yarder (Koller K...

Noninvasive spectral imaging of skin chromophores based on multiple regression analysis aided by Monte Carlo simulation

NASA Astrophysics Data System (ADS)

Nishidate, Izumi; Wiswadarma, Aditya; Hase, Yota; Tanaka, Noriyuki; Maeda, Takaaki; Niizeki, Kyuichi; Aizu, Yoshihisa

2011-08-01

In order to visualize melanin and blood concentrations and oxygen saturation in human skin tissue, a simple imaging technique based on multispectral diffuse reflectance images acquired at six wavelengths (500, 520, 540, 560, 580 and 600nm) was developed. The technique utilizes multiple regression analysis aided by Monte Carlo simulation for diffuse reflectance spectra. Using the absorbance spectrum as a response variable and the extinction coefficients of melanin, oxygenated hemoglobin, and deoxygenated hemoglobin as predictor variables, multiple regression analysis provides regression coefficients. Concentrations of melanin and total blood are then determined from the regression coefficients using conversion vectors that are deduced numerically in advance, while oxygen saturation is obtained directly from the regression coefficients. Experiments with a tissue-like agar gel phantom validated the method. In vivo experiments with human skin of the human hand during upper limb occlusion and of the inner forearm exposed to UV irradiation demonstrated the ability of the method to evaluate physiological reactions of human skin tissue.

Using the Coefficient of Determination "R"[superscript 2] to Test the Significance of Multiple Linear Regression

ERIC Educational Resources Information Center

Quinino, Roberto C.; Reis, Edna A.; Bessegato, Lupercio F.

2013-01-01

This article proposes the use of the coefficient of determination as a statistic for hypothesis testing in multiple linear regression based on distributions acquired by beta sampling. (Contains 3 figures.)

Identifying maternal and infant factors associated with newborn size in rural Bangladesh by partial least squares (PLS) regression analysis

PubMed Central

Rahman, Md. Jahanur; Shamim, Abu Ahmed; Klemm, Rolf D. W.; Labrique, Alain B.; Rashid, Mahbubur; Christian, Parul; West, Keith P.

2017-01-01

Birth weight, length and circumferences of the head, chest and arm are key measures of newborn size and health in developing countries. We assessed maternal socio-demographic factors associated with multiple measures of newborn size in a large rural population in Bangladesh using partial least squares (PLS) regression method. PLS regression, combining features from principal component analysis and multiple linear regression, is a multivariate technique with an ability to handle multicollinearity while simultaneously handling multiple dependent variables. We analyzed maternal and infant data from singletons (n = 14,506) born during a double-masked, cluster-randomized, placebo-controlled maternal vitamin A or β-carotene supplementation trial in rural northwest Bangladesh. PLS regression results identified numerous maternal factors (parity, age, early pregnancy MUAC, living standard index, years of education, number of antenatal care visits, preterm delivery and infant sex) significantly (p<0.001) associated with newborn size. Among them, preterm delivery had the largest negative influence on newborn size (Standardized β = -0.29 − -0.19; p<0.001). Scatter plots of the scores of first two PLS components also revealed an interaction between newborn sex and preterm delivery on birth size. PLS regression was found to be more parsimonious than both ordinary least squares regression and principal component regression. It also provided more stable estimates than the ordinary least squares regression and provided the effect measure of the covariates with greater accuracy as it accounts for the correlation among the covariates and outcomes. Therefore, PLS regression is recommended when either there are multiple outcome measurements in the same study, or the covariates are correlated, or both situations exist in a dataset. PMID:29261760

Identifying maternal and infant factors associated with newborn size in rural Bangladesh by partial least squares (PLS) regression analysis.

PubMed

Kabir, Alamgir; Rahman, Md Jahanur; Shamim, Abu Ahmed; Klemm, Rolf D W; Labrique, Alain B; Rashid, Mahbubur; Christian, Parul; West, Keith P

2017-01-01

Birth weight, length and circumferences of the head, chest and arm are key measures of newborn size and health in developing countries. We assessed maternal socio-demographic factors associated with multiple measures of newborn size in a large rural population in Bangladesh using partial least squares (PLS) regression method. PLS regression, combining features from principal component analysis and multiple linear regression, is a multivariate technique with an ability to handle multicollinearity while simultaneously handling multiple dependent variables. We analyzed maternal and infant data from singletons (n = 14,506) born during a double-masked, cluster-randomized, placebo-controlled maternal vitamin A or β-carotene supplementation trial in rural northwest Bangladesh. PLS regression results identified numerous maternal factors (parity, age, early pregnancy MUAC, living standard index, years of education, number of antenatal care visits, preterm delivery and infant sex) significantly (p<0.001) associated with newborn size. Among them, preterm delivery had the largest negative influence on newborn size (Standardized β = -0.29 - -0.19; p<0.001). Scatter plots of the scores of first two PLS components also revealed an interaction between newborn sex and preterm delivery on birth size. PLS regression was found to be more parsimonious than both ordinary least squares regression and principal component regression. It also provided more stable estimates than the ordinary least squares regression and provided the effect measure of the covariates with greater accuracy as it accounts for the correlation among the covariates and outcomes. Therefore, PLS regression is recommended when either there are multiple outcome measurements in the same study, or the covariates are correlated, or both situations exist in a dataset.

The M Word: Multicollinearity in Multiple Regression.

ERIC Educational Resources Information Center

Morrow-Howell, Nancy

1994-01-01

Notes that existence of substantial correlation between two or more independent variables creates problems of multicollinearity in multiple regression. Discusses multicollinearity problem in social work research in which independent variables are usually intercorrelated. Clarifies problems created by multicollinearity, explains detection of…

[Prediction model of health workforce and beds in county hospitals of Hunan by multiple linear regression].

PubMed

Ling, Ru; Liu, Jiawang

2011-12-01

To construct prediction model for health workforce and hospital beds in county hospitals of Hunan by multiple linear regression. We surveyed 16 counties in Hunan with stratified random sampling according to uniform questionnaires,and multiple linear regression analysis with 20 quotas selected by literature view was done. Independent variables in the multiple linear regression model on medical personnels in county hospitals included the counties' urban residents' income, crude death rate, medical beds, business occupancy, professional equipment value, the number of devices valued above 10 000 yuan, fixed assets, long-term debt, medical income, medical expenses, outpatient and emergency visits, hospital visits, actual available bed days, and utilization rate of hospital beds. Independent variables in the multiple linear regression model on county hospital beds included the the population of aged 65 and above in the counties, disposable income of urban residents, medical personnel of medical institutions in county area, business occupancy, the total value of professional equipment, fixed assets, long-term debt, medical income, medical expenses, outpatient and emergency visits, hospital visits, actual available bed days, utilization rate of hospital beds, and length of hospitalization. The prediction model shows good explanatory and fitting, and may be used for short- and mid-term forecasting.

The use of regression analysis in determining reference intervals for low hematocrit and thrombocyte count in multiple electrode aggregometry and platelet function analyzer 100 testing of platelet function.

PubMed

Kuiper, Gerhardus J A J M; Houben, Rik; Wetzels, Rick J H; Verhezen, Paul W M; Oerle, Rene van; Ten Cate, Hugo; Henskens, Yvonne M C; Lancé, Marcus D

2017-11-01

Low platelet counts and hematocrit levels hinder whole blood point-of-care testing of platelet function. Thus far, no reference ranges for MEA (multiple electrode aggregometry) and PFA-100 (platelet function analyzer 100) devices exist for low ranges. Through dilution methods of volunteer whole blood, platelet function at low ranges of platelet count and hematocrit levels was assessed on MEA for four agonists and for PFA-100 in two cartridges. Using (multiple) regression analysis, 95% reference intervals were computed for these low ranges. Low platelet counts affected MEA in a positive correlation (all agonists showed r 2 ≥ 0.75) and PFA-100 in an inverse correlation (closure times were prolonged with lower platelet counts). Lowered hematocrit did not affect MEA testing, except for arachidonic acid activation (ASPI), which showed a weak positive correlation (r 2 = 0.14). Closure time on PFA-100 testing was inversely correlated with hematocrit for both cartridges. Regression analysis revealed different 95% reference intervals in comparison with originally established intervals for both MEA and PFA-100 in low platelet or hematocrit conditions. Multiple regression analysis of ASPI and both tests on the PFA-100 for combined low platelet and hematocrit conditions revealed that only PFA-100 testing should be adjusted for both thrombocytopenia and anemia. 95% reference intervals were calculated using multiple regression analysis. However, coefficients of determination of PFA-100 were poor, and some variance remained unexplained. Thus, in this pilot study using (multiple) regression analysis, we could establish reference intervals of platelet function in anemia and thrombocytopenia conditions on PFA-100 and in thrombocytopenia conditions on MEA.

Comparison of methods to determine disk and heartwood areas

Treesearch

Michael C. Wiemann; John P. Brown; Neal D. Bennett

2002-01-01

The feasibility of using radius measurements on disks to determine cross-sectional areas of tree stems and the heartwood they contain was examined in sugar maple and red oak butt logs. Areas calculated from quadratic means of four stem radii and four heartwood radii were compared with areas measured with a planimeter. The lineal measurement method was less precise for...

78 FR 50093 - Notice of Intent To Repatriate Cultural Items: U.S. Department of the Interior, National Park...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-08-16

... cultural items listed in this notice meet the definition of sacred objects. Lineal descendants or... sacred objects under 25 U.S.C. 3001. This notice is published as part of the National Park Service's... Preserve, Inc., donated the David T. Vernon Collection to Grand Teton National Park. The three sacred...

76 FR 80391 - Notice of Intent to Repatriate Cultural Items: U.S. Department of the Interior, National Park...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-12-23

... cultural items meet the definition of sacred objects and repatriation to the lineal descendant stated below... descendants of the individual who owned these sacred objects and who wish to claim the items should contact... descendants of the individual who owned these sacred objects and who wish to claim the items should contact...

77 FR 34986 - Notice of Intent To Repatriate Cultural Items: U.S. Department of the Interior, National Park...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-06-12

... appropriate Indian tribes, has determined that the cultural items meet the definition of sacred objects and... individuals who believe they are lineal descendants of the individual who owned these sacred objects and who... descendants of the individual who owned these sacred objects and who wish to claim the items should contact...

76 FR 80390 - Notice of Intent To Repatriate Cultural Items: U.S. Department of the Interior, National Park...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-12-23

... cultural items meet the definition of sacred objects and repatriation to the lineal descendant stated below... descendants of the individual who owned these sacred objects and who wish to claim the items should contact... descendants of the individual who owned these sacred objects who wish to claim the items should contact Little...

Radial dependence of lineal energy distribution of 290-MeV/u carbon and 500-MeV/u iron ion beams using a wall-less tissue-equivalent proportional counter.

PubMed

Tsuda, Shuichi; Sato, Tatsuhiko; Watanabe, Ritsuko; Takada, Masashi

2015-01-01

Using a wall-less tissue-equivalent proportional counter for a 0.72-μm site in tissue, we measured the radial dependence of the lineal energy distribution, yf(y), of 290-MeV/u carbon ions and 500-MeV/u iron ion beams. The measured yf(y) distributions and the dose-mean of y, [Formula: see text], were compared with calculations performed with the track structure simulation code TRACION and the microdosimetric function of the Particle and Heavy Ion Transport code System (PHITS). The values of the measured [Formula: see text] were consistent with calculated results within an error of 2%, but differences in the shape of yf(y) were observed for iron ion irradiation. This result indicates that further improvement of the calculation model for yf(y) distribution in PHITS is needed for the analytical function that describes energy deposition by delta rays, particularly for primary ions having linear energy transfer in excess of a few hundred keV μm(-1). © The Author 2014. Published by Oxford University Press on behalf of The Japan Radiation Research Society and Japanese Society for Radiation Oncology.

Polarización lineal en estrellas simbióticas

NASA Astrophysics Data System (ADS)

Brandi, E.; García, L. G.; Ferrer, O. E.

Se presenta un estudio de polarización lineal en las bandas UBVRI de un grupo de estrellas simbióticas, a fin de analizar el comportamiento del grado de polarización y del ángulo de posición en función de la longitud de onda. En aquellos sistemas observados repetidas veces, se analizan además variaciones temporales en los parámetros de polarización. En base a este comportamiento se determina si la polarización observada es puramente interestelar o si existe una contribución de polarización, intrínseca al objeto. La muestra de objetos estudiados presenta ciertas características comunes, es decir, son sistemas simbióticos tipo D, cuyas respectivas componentes gigantes son variables tipo Mira y además presentan nebulosas ionizadas extendidas con morfología y dimensiones conocidas. Esto es de importancia ya que el conocimiento de la estructura del gas extendido permite interpretar la geometría de scattering que produce la polarización observada. Las observaciones fueron realizadas con el Fotopolarímetro de Torino adosado al telescopio de 2.15 m del CASLEO.

Offspring sex preferences among patrilineal and matrilineal Mosuo in Southwest China revealed by differences in parity progression.

PubMed

Mattison, Siobhán M; Beheim, Bret; Chak, Bridget; Buston, Peter

2016-09-01

Son preference predominates in China, yet there are patterned exceptions to this rule. In this paper, we test whether lineality (patrilineal versus matrilineal inheritance and descent) is associated with son versus daughter preference among the ethnic Mosuo (Na) of Southwest China. Our results show (i) an increased probability of continued fertility among matrilineal women after having a son compared with a daughter and (ii) an increased probability of continued fertility among patrilineal women after having a daughter compared with a son. These results are consistent with son preference among patrilineal Mosuo and more muted daughter preference among the matrilineal Mosuo. Furthermore, we show (iii) the lowest probability of continued fertility at parity 2 once women have one daughter and one son across both systems, suggesting that preferences for at least one of each sex exist alongside preferences for the lineal sex. The Mosuo are the only known small-scale society in which two kinship systems distinguish sub-groups with many otherwise shared cultural characteristics. We discuss why this, in conjunction with differences in subsistence, may shed light on the evolutionary underpinnings of offspring sex preferences.

The response of a spherical tissue-equivalent proportional counter to iron particles from 200-1000 MeV/nucleon

NASA Technical Reports Server (NTRS)

Gersey, B. B.; Borak, T. B.; Guetersloh, S. B.; Zeitlin, C.; Miller, J.; Heilbronn, L.; Murakami, T.; Iwata, Y.; Chatterjee, A. (Principal Investigator)

2002-01-01

The radiation environment on board the space shuttle and the International Space Station includes high-Z and high-energy (HZE) particles that are part of the galactic cosmic radiation (GCR) spectrum. Iron-56 particles are considered to be one of the most biologically important parts of the GCR spectrum. Tissue-equivalent proportional counters (TEPCs) are used as active dosimeters on manned space flights. These TEPCs are further used to determine the average quality factor for each space mission. A TEPC simulating a 1-microm-diameter sphere of tissue was exposed as part of a particle spectrometer to (56)Fe particles at energies from 200-1000 MeV/nucleon. The response of TEPCs in terms of mean lineal energy, y(F), and dose mean lineal energy, y(D), as well as the energy deposited at different impact parameters through the detector was determined for six different incident energies of (56)Fe particles in this energy range. Calculations determined that charged-particle equilibrium was achieved for each of the six experiments. Energy depositions at different impact parameters were calculated using a radial dose distribution model, and the results were compared to experimental data.

A MULTI-ELEMENT THICK GAS ELECTRON MULTIPLIER-BASED MICRODOSEMETER FOR MEASUREMENT OF NEUTRONS DOSE-EQUIVALENT: A MONTE CARLO STUDY.

PubMed

Moslehi, A; Raisali, G

2017-11-01

To determine the dose-equivalent of neutrons in an extended energy range, in the present work a multi-element thick gas electron multiplier-based microdosemeter made of PMMA (Perspex) walls of 10 mm in thickness is designed. Each cavity is filled with the propane-based tissue-equivalent (TE) gas simulating 1 µm of tissue. Also, a few weight fractions of 3He are assumed to be added to the TE gas. The dose-equivalents are determined for 11 neutron energies between thermal and 14 MeV using the lineal energy distributions calculated by Geant4 simulation toolkit and also the lineal energy-based quality factors. The results show that by adding 0.04% of 3He to the TE gas in each cavity, an energy-independent dose-equivalent response within 30% uncertainty around a median value of 0.91 in the above energy range is achieved. It is concluded that after its construction, the studied microdosemeter can be used to measure the dose-equivalent of neutrons, favorably. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Offspring sex preferences among patrilineal and matrilineal Mosuo in Southwest China revealed by differences in parity progression

PubMed Central

Beheim, Bret; Chak, Bridget; Buston, Peter

2016-01-01

Son preference predominates in China, yet there are patterned exceptions to this rule. In this paper, we test whether lineality (patrilineal versus matrilineal inheritance and descent) is associated with son versus daughter preference among the ethnic Mosuo (Na) of Southwest China. Our results show (i) an increased probability of continued fertility among matrilineal women after having a son compared with a daughter and (ii) an increased probability of continued fertility among patrilineal women after having a daughter compared with a son. These results are consistent with son preference among patrilineal Mosuo and more muted daughter preference among the matrilineal Mosuo. Furthermore, we show (iii) the lowest probability of continued fertility at parity 2 once women have one daughter and one son across both systems, suggesting that preferences for at least one of each sex exist alongside preferences for the lineal sex. The Mosuo are the only known small-scale society in which two kinship systems distinguish sub-groups with many otherwise shared cultural characteristics. We discuss why this, in conjunction with differences in subsistence, may shed light on the evolutionary underpinnings of offspring sex preferences. PMID:27703713

SOME STATISTICAL ISSUES RELATED TO MULTIPLE LINEAR REGRESSION MODELING OF BEACH BACTERIA CONCENTRATIONS

EPA Science Inventory

As a fast and effective technique, the multiple linear regression (MLR) method has been widely used in modeling and prediction of beach bacteria concentrations. Among previous works on this subject, however, several issues were insufficiently or inconsistently addressed. Those is...

MULTIPLE REGRESSION MODELS FOR HINDCASTING AND FORECASTING MIDSUMMER HYPOXIA IN THE GULF OF MEXICO

EPA Science Inventory

A new suite of multiple regression models were developed that describe the relationship between the area of bottom water hypoxia along the northern Gulf of Mexico and Mississippi-Atchafalaya River nitrate concentration, total phosphorus (TP) concentration, and discharge. Variabil...

Estimate the contribution of incubation parameters influence egg hatchability using multiple linear regression analysis

PubMed Central

Khalil, Mohamed H.; Shebl, Mostafa K.; Kosba, Mohamed A.; El-Sabrout, Karim; Zaki, Nesma

2016-01-01

Aim: This research was conducted to determine the most affecting parameters on hatchability of indigenous and improved local chickens’ eggs. Materials and Methods: Five parameters were studied (fertility, early and late embryonic mortalities, shape index, egg weight, and egg weight loss) on four strains, namely Fayoumi, Alexandria, Matrouh, and Montazah. Multiple linear regression was performed on the studied parameters to determine the most influencing one on hatchability. Results: The results showed significant differences in commercial and scientific hatchability among strains. Alexandria strain has the highest significant commercial hatchability (80.70%). Regarding the studied strains, highly significant differences in hatching chick weight among strains were observed. Using multiple linear regression analysis, fertility made the greatest percent contribution (71.31%) to hatchability, and the lowest percent contributions were made by shape index and egg weight loss. Conclusion: A prediction of hatchability using multiple regression analysis could be a good tool to improve hatchability percentage in chickens. PMID:27651666

[The contribution made by neurophysiological and ultrasound imaging studies to the diagnosis of carpal tunnel syndrome. Surgical correlation].

PubMed

de Pablos, C; Velasco-Zarzosa, M; Landeras-Alvaro, R; Rubio-Lorenzo, M; Martínez-Zubieta, P

Electrophysiological study has been for long time the elected approach for the diagnosis and clinical evaluation of carpal tunnel syndrome (CTS). More recently, echography and other imaging techniques have been introduced in current medicine for their potential in the anatomical evaluation of the neural compression. To asses the usefulness of both diagnostic procedures we have compared the findings obtained by electrophysiological and echographic approaches in a group of 60 CTS patients with different degrees of the disease. In all patients the conduction velocity was evaluated in the median and cubital nerves using surface electrodes. For echography lineal transductors of 5-10 Hz and 5-12.5 MHz were employed. The patients were distributed for each test on a scale depending of the severity of the alterations detected by the corresponding technique and both files were subsequently compared by regression analysis, Pearson test and paired-test. No correlation was detected in any of the statistical test. The lack of correlation between the results of both proofs emphasizes the usefulness of the two diagnostic approaches in CTS. While electrophysiological study provides information about nerve function, ecography unravels the morphological alterations accounting for the syndrome, therefore being non-excluding complementary approaches.

Mean centering, multicollinearity, and moderators in multiple regression: The reconciliation redux.

PubMed

Iacobucci, Dawn; Schneider, Matthew J; Popovich, Deidre L; Bakamitsos, Georgios A

2017-02-01

In this article, we attempt to clarify our statements regarding the effects of mean centering. In a multiple regression with predictors A, B, and A × B (where A × B serves as an interaction term), mean centering A and B prior to computing the product term can clarify the regression coefficients (which is good) and the overall model fit R 2 will remain undisturbed (which is also good).

A Common Mechanism for Resistance to Oxime Reactivation of Acetylcholinesterase Inhibited by Organophosphorus Compounds

DTIC Science & Technology

2013-01-01

application of the Hammett equation with the constants rph in the chemistry of organophosphorus compounds, Russ. Chem. Rev. 38 (1969) 795–811. [13...of oximes and OP compounds and the ability of oximes to reactivate OP- inhibited AChE. Multiple linear regression equations were analyzed using...phosphonate pairs, 21 oxime/ phosphoramidate pairs and 12 oxime/phosphate pairs. The best linear regression equation resulting from multiple regression anal

Novel applications of multitask learning and multiple output regression to multiple genetic trait prediction.

PubMed

He, Dan; Kuhn, David; Parida, Laxmi

2016-06-15

Given a set of biallelic molecular markers, such as SNPs, with genotype values encoded numerically on a collection of plant, animal or human samples, the goal of genetic trait prediction is to predict the quantitative trait values by simultaneously modeling all marker effects. Genetic trait prediction is usually represented as linear regression models. In many cases, for the same set of samples and markers, multiple traits are observed. Some of these traits might be correlated with each other. Therefore, modeling all the multiple traits together may improve the prediction accuracy. In this work, we view the multitrait prediction problem from a machine learning angle: as either a multitask learning problem or a multiple output regression problem, depending on whether different traits share the same genotype matrix or not. We then adapted multitask learning algorithms and multiple output regression algorithms to solve the multitrait prediction problem. We proposed a few strategies to improve the least square error of the prediction from these algorithms. Our experiments show that modeling multiple traits together could improve the prediction accuracy for correlated traits. The programs we used are either public or directly from the referred authors, such as MALSAR (http://www.public.asu.edu/~jye02/Software/MALSAR/) package. The Avocado data set has not been published yet and is available upon request. dhe@us.ibm.com. © The Author 2016. Published by Oxford University Press.

Simple and multiple linear regression: sample size considerations.

PubMed

Hanley, James A

2016-11-01

The suggested "two subjects per variable" (2SPV) rule of thumb in the Austin and Steyerberg article is a chance to bring out some long-established and quite intuitive sample size considerations for both simple and multiple linear regression. This article distinguishes two of the major uses of regression models that imply very different sample size considerations, neither served well by the 2SPV rule. The first is etiological research, which contrasts mean Y levels at differing "exposure" (X) values and thus tends to focus on a single regression coefficient, possibly adjusted for confounders. The second research genre guides clinical practice. It addresses Y levels for individuals with different covariate patterns or "profiles." It focuses on the profile-specific (mean) Y levels themselves, estimating them via linear compounds of regression coefficients and covariates. By drawing on long-established closed-form variance formulae that lie beneath the standard errors in multiple regression, and by rearranging them for heuristic purposes, one arrives at quite intuitive sample size considerations for both research genres. Copyright © 2016 Elsevier Inc. All rights reserved.

Multiple imputation for cure rate quantile regression with censored data.

PubMed

Wu, Yuanshan; Yin, Guosheng

2017-03-01

The main challenge in the context of cure rate analysis is that one never knows whether censored subjects are cured or uncured, or whether they are susceptible or insusceptible to the event of interest. Considering the susceptible indicator as missing data, we propose a multiple imputation approach to cure rate quantile regression for censored data with a survival fraction. We develop an iterative algorithm to estimate the conditionally uncured probability for each subject. By utilizing this estimated probability and Bernoulli sample imputation, we can classify each subject as cured or uncured, and then employ the locally weighted method to estimate the quantile regression coefficients with only the uncured subjects. Repeating the imputation procedure multiple times and taking an average over the resultant estimators, we obtain consistent estimators for the quantile regression coefficients. Our approach relaxes the usual global linearity assumption, so that we can apply quantile regression to any particular quantile of interest. We establish asymptotic properties for the proposed estimators, including both consistency and asymptotic normality. We conduct simulation studies to assess the finite-sample performance of the proposed multiple imputation method and apply it to a lung cancer study as an illustration. © 2016, The International Biometric Society.

Undergraduate Student Motivation in Modularized Developmental Mathematics Courses

ERIC Educational Resources Information Center

Pachlhofer, Keith A.

2017-01-01

This study used the Motivated Strategies for Learning Questionnaire in modularized courses at three institutions across the nation (N = 189), and multiple regression was completed to investigate five categories of student motivation that predicted academic success and course completion. The overall multiple regression analysis was significant and…

MULGRES: a computer program for stepwise multiple regression analysis

Treesearch

A. Jeff Martin

1971-01-01

MULGRES is a computer program source deck that is designed for multiple regression analysis employing the technique of stepwise deletion in the search for most significant variables. The features of the program, along with inputs and outputs, are briefly described, with a note on machine compatibility.

Categorical Variables in Multiple Regression: Some Cautions.

ERIC Educational Resources Information Center

O'Grady, Kevin E.; Medoff, Deborah R.

1988-01-01

Limitations of dummy coding and nonsense coding as methods of coding categorical variables for use as predictors in multiple regression analysis are discussed. The combination of these approaches often yields estimates and tests of significance that are not intended by researchers for inclusion in their models. (SLD)

Multiple Imputation of a Randomly Censored Covariate Improves Logistic Regression Analysis.

PubMed

Atem, Folefac D; Qian, Jing; Maye, Jacqueline E; Johnson, Keith A; Betensky, Rebecca A

2016-01-01

Randomly censored covariates arise frequently in epidemiologic studies. The most commonly used methods, including complete case and single imputation or substitution, suffer from inefficiency and bias. They make strong parametric assumptions or they consider limit of detection censoring only. We employ multiple imputation, in conjunction with semi-parametric modeling of the censored covariate, to overcome these shortcomings and to facilitate robust estimation. We develop a multiple imputation approach for randomly censored covariates within the framework of a logistic regression model. We use the non-parametric estimate of the covariate distribution or the semiparametric Cox model estimate in the presence of additional covariates in the model. We evaluate this procedure in simulations, and compare its operating characteristics to those from the complete case analysis and a survival regression approach. We apply the procedures to an Alzheimer's study of the association between amyloid positivity and maternal age of onset of dementia. Multiple imputation achieves lower standard errors and higher power than the complete case approach under heavy and moderate censoring and is comparable under light censoring. The survival regression approach achieves the highest power among all procedures, but does not produce interpretable estimates of association. Multiple imputation offers a favorable alternative to complete case analysis and ad hoc substitution methods in the presence of randomly censored covariates within the framework of logistic regression.

Advanced Statistics for Exotic Animal Practitioners.

PubMed

Hodsoll, John; Hellier, Jennifer M; Ryan, Elizabeth G

2017-09-01

Correlation and regression assess the association between 2 or more variables. This article reviews the core knowledge needed to understand these analyses, moving from visual analysis in scatter plots through correlation, simple and multiple linear regression, and logistic regression. Correlation estimates the strength and direction of a relationship between 2 variables. Regression can be considered more general and quantifies the numerical relationships between an outcome and 1 or multiple variables in terms of a best-fit line, allowing predictions to be made. Each technique is discussed with examples and the statistical assumptions underlying their correct application. Copyright © 2017 Elsevier Inc. All rights reserved.

Use of Thematic Mapper for water quality assessment

NASA Technical Reports Server (NTRS)

Horn, E. M.; Morrissey, L. A.

1984-01-01

The evaluation of simulated TM data obtained on an ER-2 aircraft at twenty-five predesignated sample sites for mapping water quality factors such as conductivity, pH, suspended solids, turbidity, temperature, and depth, is discussed. Using a multiple regression for the seven TM bands, an equation is developed for the suspended solids. TM bands 1, 2, 3, 4, and 6 are used with logarithm conductivity in a multiple regression. The assessment of regression equations for a high coefficient of determination (R-squared) and statistical significance is considered. Confidence intervals about the mean regression point are calculated in order to assess the robustness of the regressions used for mapping conductivity, turbidity, and suspended solids, and by regressing random subsamples of sites and comparing the resultant range of R-squared, cross validation is conducted.

INTRODUCTION TO A COMBINED MULTIPLE LINEAR REGRESSION AND ARMA MODELING APPROACH FOR BEACH BACTERIA PREDICTION

EPA Science Inventory

Due to the complexity of the processes contributing to beach bacteria concentrations, many researchers rely on statistical modeling, among which multiple linear regression (MLR) modeling is most widely used. Despite its ease of use and interpretation, there may be time dependence...

Determining the Spatial and Seasonal Variability in OM/OC Ratios across the U.S. Using Multiple Regression

EPA Science Inventory

Data from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network are used to estimate organic mass to organic carbon (OM/OC) ratios across the United States by extending previously published multiple regression techniques. Our new methodology addresses com...

Analysis and Interpretation of Findings Using Multiple Regression Techniques

ERIC Educational Resources Information Center

Hoyt, William T.; Leierer, Stephen; Millington, Michael J.

2006-01-01

Multiple regression and correlation (MRC) methods form a flexible family of statistical techniques that can address a wide variety of different types of research questions of interest to rehabilitation professionals. In this article, we review basic concepts and terms, with an emphasis on interpretation of findings relevant to research questions…

Tracking the Gender Pay Gap: A Case Study

ERIC Educational Resources Information Center

Travis, Cheryl B.; Gross, Louis J.; Johnson, Bruce A.

2009-01-01

This article provides a short introduction to standard considerations in the formal study of wages and illustrates the use of multiple regression and resampling simulation approaches in a case study of faculty salaries at one university. Multiple regression is especially beneficial where it provides information on strength of association, specific…

Estimating air drying times of lumber with multiple regression

Treesearch

William T. Simpson

2004-01-01

In this study, the applicability of a multiple regression equation for estimating air drying times of red oak, sugar maple, and ponderosa pine lumber was evaluated. The equation allows prediction of estimated air drying times from historic weather records of temperature and relative humidity at any desired location.

Using Robust Variance Estimation to Combine Multiple Regression Estimates with Meta-Analysis

ERIC Educational Resources Information Center

Williams, Ryan

2013-01-01

The purpose of this study was to explore the use of robust variance estimation for combining commonly specified multiple regression models and for combining sample-dependent focal slope estimates from diversely specified models. The proposed estimator obviates traditionally required information about the covariance structure of the dependent…

Multiple Regression: A Leisurely Primer.

ERIC Educational Resources Information Center

Daniel, Larry G.; Onwuegbuzie, Anthony J.

Multiple regression is a useful statistical technique when the researcher is considering situations in which variables of interest are theorized to be multiply caused. It may also be useful in those situations in which the researchers is interested in studies of predictability of phenomena of interest. This paper provides an introduction to…

Using Monte Carlo Techniques to Demonstrate the Meaning and Implications of Multicollinearity

ERIC Educational Resources Information Center

Vaughan, Timothy S.; Berry, Kelly E.

2005-01-01

This article presents an in-class Monte Carlo demonstration, designed to demonstrate to students the implications of multicollinearity in a multiple regression study. In the demonstration, students already familiar with multiple regression concepts are presented with a scenario in which the "true" relationship between the response and…

Assessing the Impact of Influential Observations on Multiple Regression Analysis on Human Resource Research.

ERIC Educational Resources Information Center

Bates, Reid A.; Holton, Elwood F., III; Burnett, Michael F.

1999-01-01

A case study of learning transfer demonstrates the possible effect of influential observation on linear regression analysis. A diagnostic method that tests for violation of assumptions, multicollinearity, and individual and multiple influential observations helps determine which observation to delete to eliminate bias. (SK)

Earth analysis methods, subsurface feature detection methods, earth analysis devices, and articles of manufacture

DOEpatents

West, Phillip B [Idaho Falls, ID; Novascone, Stephen R [Idaho Falls, ID; Wright, Jerry P [Idaho Falls, ID

2012-05-29

Earth analysis methods, subsurface feature detection methods, earth analysis devices, and articles of manufacture are described. According to one embodiment, an earth analysis method includes engaging a device with the earth, analyzing the earth in a single substantially lineal direction using the device during the engaging, and providing information regarding a subsurface feature of the earth using the analysis.

Earth analysis methods, subsurface feature detection methods, earth analysis devices, and articles of manufacture

DOEpatents

West, Phillip B [Idaho Falls, ID; Novascone, Stephen R [Idaho Falls, ID; Wright, Jerry P [Idaho Falls, ID

2011-09-27

Earth analysis methods, subsurface feature detection methods, earth analysis devices, and articles of manufacture are described. According to one embodiment, an earth analysis method includes engaging a device with the earth, analyzing the earth in a single substantially lineal direction using the device during the engaging, and providing information regarding a subsurface feature of the earth using the analysis.

Las Matematicas: Lenguaje Universal. Grados Intermedios, Nivel 5b: Medida Lineal, Perimetro y Area (Mathematics: A Universal Language. Intermediate Grades, Level 5b: Linear Measure, Perimeter and Area).

ERIC Educational Resources Information Center

Dissemination and Assessment Center for Bilingual Education, Austin, TX.

This is one of a series of student booklets designed for use in a bilingual mathematics program in grades 6-8. The general format is to present each page in both Spanish and English. The mathematical topics in this booklet include measurement, perimeter, and area. (MK)

78 FR 45964 - Notice of Intent To Repatriate a Cultural Item: U.S. Department of the Interior, National Park...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-30

... sacred object. Lineal descendants or representatives of any Indian tribe or Native Hawaiian organization... of sacred object under 25 U.S.C. 3001. This notice is published as part of the National Park Service... purchase that represented the life ways of the area people. The one sacred object is a red catlinite pipe...

78 FR 59968 - Notice of Intent To Repatriate Cultural Items: U.S. Department of the Interior, Bureau of Indian...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-09-30

... definition of a sacred object. Lineal descendants or representatives of any Indian tribe or Native Hawaiian... the definition of sacred object under 25 U.S.C. 3001. This notice is published as part of the National... sacred object is a bowl, identified by staff at the Arizona State Museum as Tusayan White Ware, and...

76 FR 80389 - Notice of Intent To Repatriate a Cultural Item: U.S. Department of the Interior, National Park...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-12-23

... cultural item meets the definition of sacred object and repatriation to the lineal descendant stated below... descendants of the individual who owned the sacred object and who wish to claim the item should contact Little... descendants of the individual who owned the sacred object and who wish to claim the item should contact Little...

SU-F-T-126: Microdosimetic Evaluation of Proton Energy Distributions in the Vicinity of Metal Implants

DOE Office of Scientific and Technical Information (OSTI.GOV)

Heczko, S; McAuley, GA; Slater, JM

Purpose: To evaluate the impact of titanium and surgical stainless steel implants on the microscopic dose distribution in proton treatment plans Methods: Geant4 Monte Carlo simulations were used to analyze the microdosimetric distribution of proton radiation in the vicinity of 3.1 mm thick CP Grade 4 titanium (Ti) or 316 stainless steel (SS316) plates in a water phantom. Additional simulations were performed using either water, or water with a density equivalent to the respective metals (Tiwater, SS316water) (to reflect common practice in treatment planning). Implants were placed at the COM of SOBPs of 157 MeV (range of ∼15 cm inmore » water) protons with 30 or 60 mm modulation. Primary and secondary particle dose and fluence, frequency-weighted and dose-weighted average lineal energy, average radiation quality factor, dose equivalent and energy deposition histograms in the plate vicinity were compared. Results: Preliminary results show frequency-weighted (yf) and dose-weighted lineal energy (yd) was increased downstream of the Ti plate (yf = 3.1 keV/µm; yd = 5.5 keV/µm) and Tiwater (yf = 4.1 keV/µm; yd = 6.8 keV/µm) compared to that of water (ie, the absence of a plate) (yf = 2.5 keV/µm; yd = 4.5 keV/µm). In addition, downstream proton dose deposition was also elevated due to the presence of the Ti plate or Tiwater. The additional dose deposited at higher lineal energy implies that tissues downstream of the plate will receive a higher dose equivalent. Detailed analyses of the Ti, Tiwater, SS316, and SS316 water simulations will be presented. Conclusion: The presence of high-density materials introduces changes in the spatial distribution of radiation in the vicinity of an implant. Further work quantifying these effects could be incorporated into future treatment planning systems resulting in more accurate treatment plans. This project was sponsored with funding from the Department of Defense (DOD # W81XWH-10-2-0192).« less

Monte Carlo simulations of {sup 3}He ion physical characteristics in a water phantom and evaluation of radiobiological effectiveness

DOE Office of Scientific and Technical Information (OSTI.GOV)

Taleei, Reza; Guan, Fada; Peeler, Chris

Purpose: {sup 3}He ions may hold great potential for clinical therapy because of both their physical and biological properties. In this study, the authors investigated the physical properties, i.e., the depth-dose curves from primary and secondary particles, and the energy distributions of helium ({sup 3}He) ions. A relative biological effectiveness (RBE) model was applied to assess the biological effectiveness on survival of multiple cell lines. Methods: In light of the lack of experimental measurements and cross sections, the authors used Monte Carlo methods to study the energy deposition of {sup 3}He ions. The transport of {sup 3}He ions in watermore » was simulated by using three Monte Carlo codes—FLUKA, GEANT4, and MCNPX—for incident beams with Gaussian energy distributions with average energies of 527 and 699 MeV and a full width at half maximum of 3.3 MeV in both cases. The RBE of each was evaluated by using the repair-misrepair-fixation model. In all of the simulations with each of the three Monte Carlo codes, the same geometry and primary beam parameters were used. Results: Energy deposition as a function of depth and energy spectra with high resolution was calculated on the central axis of the beam. Secondary proton dose from the primary {sup 3}He beams was predicted quite differently by the three Monte Carlo systems. The predictions differed by as much as a factor of 2. Microdosimetric parameters such as dose mean lineal energy (y{sub D}), frequency mean lineal energy (y{sub F}), and frequency mean specific energy (z{sub F}) were used to characterize the radiation beam quality at four depths of the Bragg curve. Calculated RBE values were close to 1 at the entrance, reached on average 1.8 and 1.6 for prostate and head and neck cancer cell lines at the Bragg peak for both energies, but showed some variations between the different Monte Carlo codes. Conclusions: Although the Monte Carlo codes provided different results in energy deposition and especially in secondary particle production (most of the differences between the three codes were observed close to the Bragg peak, where the energy spectrum broadens), the results in terms of RBE were generally similar.« less

A novel simple QSAR model for the prediction of anti-HIV activity using multiple linear regression analysis.

PubMed

Afantitis, Antreas; Melagraki, Georgia; Sarimveis, Haralambos; Koutentis, Panayiotis A; Markopoulos, John; Igglessi-Markopoulou, Olga

2006-08-01

A quantitative-structure activity relationship was obtained by applying Multiple Linear Regression Analysis to a series of 80 1-[2-hydroxyethoxy-methyl]-6-(phenylthio) thymine (HEPT) derivatives with significant anti-HIV activity. For the selection of the best among 37 different descriptors, the Elimination Selection Stepwise Regression Method (ES-SWR) was utilized. The resulting QSAR model (R (2) (CV) = 0.8160; S (PRESS) = 0.5680) proved to be very accurate both in training and predictive stages.

Factors associated to clinical learning in nursing students in primary health care: an analytical cross-sectional study.

PubMed

Serrano-Gallardo, Pilar; Martínez-Marcos, Mercedes; Espejo-Matorrales, Flora; Arakawa, Tiemi; Magnabosco, Gabriela Tavares; Pinto, Ione Carvalho

2016-09-09

to identify the students' perception about the quality of clinical placements and asses the influence of the different tutoring processes in clinical learning. analytical cross-sectional study on second and third year nursing students (n=122) about clinical learning in primary health care. The Clinical Placement Evaluation Tool and a synthetic index of attitudes and skills were computed to give scores to the clinical learning (scale 0-10). Univariate, bivariate and multivariate (multiple linear regression) analyses were performed. the response rate was 91.8%. The most commonly identified tutoring process was "preceptor-professor" (45.2%). The clinical placement was assessed as "optimal" by 55.1%, relationship with team-preceptor was considered good by 80.4% of the cases and the average grade for clinical learning was 7.89. The multiple linear regression model with more explanatory capacity included the variables "Academic year" (beta coefficient = 1.042 for third-year students), "Primary Health Care Area (PHC)" (beta coefficient = 0.308 for Area B) and "Clinical placement perception" (beta coefficient = - 0.204 for a suboptimal perception). timeframe within the academic program, location and clinical placement perception were associated with students' clinical learning. Students' perceptions of setting quality were positive and a good team-preceptor relationship is a matter of relevance. identificar a percepção dos estudantes de enfermagem sobre a qualidade das Práticas Clínicas em Atenção Primária à Saúde e avaliar a influência dos diferentes processos de tutoria na aprendizagem clínica. um estudo analítico transversal realizado com alunos do segundo e do terceiro ano de enfermagem (n = 122) na aprendizagem clínica nos serviços de Atenção Primária à Saúde. A Ferramenta de Avaliação de Práticas Clínicas (Clinical Placement Evaluation Tool) e um índice sintético de atitudes e habilidades (escala de 0 a 10) foram calculados para marcar a aprendizagem clínica. Foram realizadas análises univariadas, bivariadas e multivariadas (regressão linear múltipla). a taxa de resposta foi de 91,8%. O processo de tutoria mais indicado foi o de "preceptor-professor" (45,2%). As Práticas Clínicas foram avaliadas como "ótimas" por 55,1%, o relacionamento com a equipe-preceptor foi considerado bom por 80,4% dos casos e a classificação média para a aprendizagem clínica foi de 7,89. O modelo de regressão linear múltipla, com mais capacidade explicativa incluiu as variáveis "ano acadêmico" (coeficiente de beta = 1.042 para alunos do terceiro ano), "área de cuidados de saúde primários" (coeficiente de beta = 0,308 para a Área B) e "percepção de práticas clínicas" (beta coeficiente = - 0,204 para uma percepção menos que ótima). O momento dentro do programa acadêmico, a localização e percepção das Práticas Clínicas foram associados com a aprendizagem clínica dos alunos. A percepção dos alunos sobre a qualidade do lugar das Práticas Clínicas foi positiva e um bom relacionamento da equipe-preceptor é uma questão de relevância. identificar la percepción del alumnado de enfermería sobre la calidad de las Prácticas Clínicas y evaluar la influencia de los diferentes procesos de tutoría en el aprendizaje clínico. estudio analítico transversal realizado con estudiantes de segundo y tercer año de enfermería (n = 122) en el aprendizaje clínico en los servicios de atención primaria de salud. La Herramienta de Evaluación de Prácticas Clínicas (Clinical Placement Evaluation Tool) y un índice sintético de las actitudes y habilidades (escala de 0 a 10) se calcularon para puntuar el aprendizaje clínico. Se realizaron análisis univariados, bivariados, multivariados (regresión lineal múltiple). la tasa de respuesta fue del 91,8%. El proceso de tutoría más comúnmente identificado fue "preceptor-profesor" (45,2%). Las Prácticas Clínicas se evaluaron como "óptimas" en un 55,1%, la relación equipo-preceptor fue considerada buena por el 80,4% de los casos y la calificación media para el aprendizaje clínico fue de 7.89. El modelo de regresión lineal múltiple con mayor capacidad explicativa incluyó las variables "año académico" (coeficiente beta = 1.042 para los estudiantes de tercer año), "Área de Atención Primaria de la Salud" (coeficiente beta = 0,308 para el Área B) y la "percepción de las Prácticas Clínicas" (coeficiente beta = - 0,204 para una percepción subóptima). el momento dentro del programa académico, la ubicación y la percepción de las Prácticas Clínicac se asociaron con el aprendizaje clínico de los estudiantes. La percepción de los estudiantes de la calidad del lugar de las Prácticas Clínicas fue positiva y la buena relación equipo-preceptor es una cuestión de relevancia.

Deriving percentage study weights in multi-parameter meta-analysis models: with application to meta-regression, network meta-analysis and one-stage individual participant data models.

PubMed

Riley, Richard D; Ensor, Joie; Jackson, Dan; Burke, Danielle L

2017-01-01

Many meta-analysis models contain multiple parameters, for example due to multiple outcomes, multiple treatments or multiple regression coefficients. In particular, meta-regression models may contain multiple study-level covariates, and one-stage individual participant data meta-analysis models may contain multiple patient-level covariates and interactions. Here, we propose how to derive percentage study weights for such situations, in order to reveal the (otherwise hidden) contribution of each study toward the parameter estimates of interest. We assume that studies are independent, and utilise a decomposition of Fisher's information matrix to decompose the total variance matrix of parameter estimates into study-specific contributions, from which percentage weights are derived. This approach generalises how percentage weights are calculated in a traditional, single parameter meta-analysis model. Application is made to one- and two-stage individual participant data meta-analyses, meta-regression and network (multivariate) meta-analysis of multiple treatments. These reveal percentage study weights toward clinically important estimates, such as summary treatment effects and treatment-covariate interactions, and are especially useful when some studies are potential outliers or at high risk of bias. We also derive percentage study weights toward methodologically interesting measures, such as the magnitude of ecological bias (difference between within-study and across-study associations) and the amount of inconsistency (difference between direct and indirect evidence in a network meta-analysis).

[Application of SAS macro to evaluated multiplicative and additive interaction in logistic and Cox regression in clinical practices].

PubMed

Nie, Z Q; Ou, Y Q; Zhuang, J; Qu, Y J; Mai, J Z; Chen, J M; Liu, X Q

2016-05-01

Conditional logistic regression analysis and unconditional logistic regression analysis are commonly used in case control study, but Cox proportional hazard model is often used in survival data analysis. Most literature only refer to main effect model, however, generalized linear model differs from general linear model, and the interaction was composed of multiplicative interaction and additive interaction. The former is only statistical significant, but the latter has biological significance. In this paper, macros was written by using SAS 9.4 and the contrast ratio, attributable proportion due to interaction and synergy index were calculated while calculating the items of logistic and Cox regression interactions, and the confidence intervals of Wald, delta and profile likelihood were used to evaluate additive interaction for the reference in big data analysis in clinical epidemiology and in analysis of genetic multiplicative and additive interactions.

Wavelet regression model in forecasting crude oil price

NASA Astrophysics Data System (ADS)

Hamid, Mohd Helmie; Shabri, Ani

2017-05-01

This study presents the performance of wavelet multiple linear regression (WMLR) technique in daily crude oil forecasting. WMLR model was developed by integrating the discrete wavelet transform (DWT) and multiple linear regression (MLR) model. The original time series was decomposed to sub-time series with different scales by wavelet theory. Correlation analysis was conducted to assist in the selection of optimal decomposed components as inputs for the WMLR model. The daily WTI crude oil price series has been used in this study to test the prediction capability of the proposed model. The forecasting performance of WMLR model were also compared with regular multiple linear regression (MLR), Autoregressive Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) using root mean square errors (RMSE) and mean absolute errors (MAE). Based on the experimental results, it appears that the WMLR model performs better than the other forecasting technique tested in this study.

Multiple regression for physiological data analysis: the problem of multicollinearity.

PubMed

Slinker, B K; Glantz, S A

1985-07-01

Multiple linear regression, in which several predictor variables are related to a response variable, is a powerful statistical tool for gaining quantitative insight into complex in vivo physiological systems. For these insights to be correct, all predictor variables must be uncorrelated. However, in many physiological experiments the predictor variables cannot be precisely controlled and thus change in parallel (i.e., they are highly correlated). There is a redundancy of information about the response, a situation called multicollinearity, that leads to numerical problems in estimating the parameters in regression equations; the parameters are often of incorrect magnitude or sign or have large standard errors. Although multicollinearity can be avoided with good experimental design, not all interesting physiological questions can be studied without encountering multicollinearity. In these cases various ad hoc procedures have been proposed to mitigate multicollinearity. Although many of these procedures are controversial, they can be helpful in applying multiple linear regression to some physiological problems.

Testing Mediation Using Multiple Regression and Structural Equation Modeling Analyses in Secondary Data

ERIC Educational Resources Information Center

Li, Spencer D.

2011-01-01

Mediation analysis in child and adolescent development research is possible using large secondary data sets. This article provides an overview of two statistical methods commonly used to test mediated effects in secondary analysis: multiple regression and structural equation modeling (SEM). Two empirical studies are presented to illustrate the…

A Simple and Convenient Method of Multiple Linear Regression to Calculate Iodine Molecular Constants

ERIC Educational Resources Information Center

Cooper, Paul D.

2010-01-01

A new procedure using a student-friendly least-squares multiple linear-regression technique utilizing a function within Microsoft Excel is described that enables students to calculate molecular constants from the vibronic spectrum of iodine. This method is advantageous pedagogically as it calculates molecular constants for ground and excited…

Conjoint Analysis: A Study of the Effects of Using Person Variables.

ERIC Educational Resources Information Center

Fraas, John W.; Newman, Isadore

Three statistical techniques--conjoint analysis, a multiple linear regression model, and a multiple linear regression model with a surrogate person variable--were used to estimate the relative importance of five university attributes for students in the process of selecting a college. The five attributes include: availability and variety of…

An Exploratory Study of Face-to-Face and Cyberbullying in Sixth Grade Students

ERIC Educational Resources Information Center

Accordino, Denise B.; Accordino, Michael P.

2011-01-01

In a pilot study, sixth grade students (N = 124) completed a questionnaire assessing students' experience with bullying and cyberbullying, demographic information, quality of parent-child relationship, and ways they have dealt with bullying/cyberbullying in the past. Two multiple regression analyses were conducted. The multiple regression analysis…

The Use of Multiple Regression and Trend Analysis to Understand Enrollment Fluctuations. AIR Forum 1979 Paper.

ERIC Educational Resources Information Center

Campbell, S. Duke; Greenberg, Barry

The development of a predictive equation capable of explaining a significant percentage of enrollment variability at Florida International University is described. A model utilizing trend analysis and a multiple regression approach to enrollment forecasting was adapted to investigate enrollment dynamics at the university. Four independent…

The Use of Multiple Regression Models to Determine if Conjoint Analysis Should Be Conducted on Aggregate Data.

ERIC Educational Resources Information Center

Fraas, John W.; Newman, Isadore

1996-01-01

In a conjoint-analysis consumer-preference study, researchers must determine whether the product factor estimates, which measure consumer preferences, should be calculated and interpreted for each respondent or collectively. Multiple regression models can determine whether to aggregate data by examining factor-respondent interaction effects. This…

Double Cross-Validation in Multiple Regression: A Method of Estimating the Stability of Results.

ERIC Educational Resources Information Center

Rowell, R. Kevin

In multiple regression analysis, where resulting predictive equation effectiveness is subject to shrinkage, it is especially important to evaluate result replicability. Double cross-validation is an empirical method by which an estimate of invariance or stability can be obtained from research data. A procedure for double cross-validation is…

Detection of whole-brain abnormalities in temporal lobe epilepsy using tensor-based morphometry with DARTEL

NASA Astrophysics Data System (ADS)

Li, Wenjing; He, Huiguang; Lu, Jingjing; Lv, Bin; Li, Meng; Jin, Zhengyu

2009-10-01

Tensor-based morphometry (TBM) is an automated technique for detecting the anatomical differences between populations by examining the gradients of the deformation fields used to nonlinearly warp MR images. The purpose of this study was to investigate the whole-brain volume changes between the patients with unilateral temporal lobe epilepsy (TLE) and the controls using TBM with DARTEL, which could achieve more accurate inter-subject registration of brain images. T1-weighted images were acquired from 21 left-TLE patients, 21 right-TLE patients and 21 healthy controls, which were matched in age and gender. The determinants of the gradient of deformation fields at voxel level were obtained to quantify the expansion or contraction for individual images relative to the template, and then logarithmical transformation was applied on it. A whole brain analysis was performed using general lineal model (GLM), and the multiple comparison was corrected by false discovery rate (FDR) with p<0.05. For left-TLE patients, significant volume reductions were found in hippocampus, cingulate gyrus, precentral gyrus, right temporal lobe and cerebellum. These results potentially support the utility of TBM with DARTEL to study the structural changes between groups.

Ridge: a computer program for calculating ridge regression estimates

Treesearch

Donald E. Hilt; Donald W. Seegrist

1977-01-01

Least-squares coefficients for multiple-regression models may be unstable when the independent variables are highly correlated. Ridge regression is a biased estimation procedure that produces stable estimates of the coefficients. Ridge regression is discussed, and a computer program for calculating the ridge coefficients is presented.

BAYESIAN LARGE-SCALE MULTIPLE REGRESSION WITH SUMMARY STATISTICS FROM GENOME-WIDE ASSOCIATION STUDIES1

PubMed Central

Zhu, Xiang; Stephens, Matthew

2017-01-01

Bayesian methods for large-scale multiple regression provide attractive approaches to the analysis of genome-wide association studies (GWAS). For example, they can estimate heritability of complex traits, allowing for both polygenic and sparse models; and by incorporating external genomic data into the priors, they can increase power and yield new biological insights. However, these methods require access to individual genotypes and phenotypes, which are often not easily available. Here we provide a framework for performing these analyses without individual-level data. Specifically, we introduce a “Regression with Summary Statistics” (RSS) likelihood, which relates the multiple regression coefficients to univariate regression results that are often easily available. The RSS likelihood requires estimates of correlations among covariates (SNPs), which also can be obtained from public databases. We perform Bayesian multiple regression analysis by combining the RSS likelihood with previously proposed prior distributions, sampling posteriors by Markov chain Monte Carlo. In a wide range of simulations RSS performs similarly to analyses using the individual data, both for estimating heritability and detecting associations. We apply RSS to a GWAS of human height that contains 253,288 individuals typed at 1.06 million SNPs, for which analyses of individual-level data are practically impossible. Estimates of heritability (52%) are consistent with, but more precise, than previous results using subsets of these data. We also identify many previously unreported loci that show evidence for association with height in our analyses. Software is available at https://github.com/stephenslab/rss. PMID:29399241

Statistical experiments using the multiple regression research for prediction of proper hardness in areas of phosphorus cast-iron brake shoes manufacturing

NASA Astrophysics Data System (ADS)

Kiss, I.; Cioată, V. G.; Ratiu, S. A.; Rackov, M.; Penčić, M.

2018-01-01

Multivariate research is important in areas of cast-iron brake shoes manufacturing, because many variables interact with each other simultaneously. This article focuses on expressing the multiple linear regression model related to the hardness assurance by the chemical composition of the phosphorous cast irons destined to the brake shoes, having in view that the regression coefficients will illustrate the unrelated contributions of each independent variable towards predicting the dependent variable. In order to settle the multiple correlations between the hardness of the cast-iron brake shoes, and their chemical compositions several regression equations has been proposed. Is searched a mathematical solution which can determine the optimum chemical composition for the hardness desirable values. Starting from the above-mentioned affirmations two new statistical experiments are effectuated related to the values of Phosphorus [P], Manganese [Mn] and Silicon [Si]. Therefore, the regression equations, which describe the mathematical dependency between the above-mentioned elements and the hardness, are determined. As result, several correlation charts will be revealed.

On the Existence of a Free Boundary for a Class of Reaction-Diffusion Systems.

DTIC Science & Technology

1982-02-01

I. Diaz. "Soluciones con soporte compacto para alguno. problemas semilineales". Collect. Math. 30 (1979), 141-179. -26- [121 J. I. Diaz. Tecnica de ...supersoluciones locales para problemas estacionarios no lineales: applicacion al estudio de flujoe subsonicos. Memory of the Real Academia de Ciencias...nonlinearity, nonlinear boundary conditions, dead core set, chemical reactions Work Unit Number I - Applied Analysis (1) Seccion de Matematicas

Applications of the microdosimetric function implemented in the macroscopic particle transport simulation code PHITS.

PubMed

Sato, Tatsuhiko; Watanabe, Ritsuko; Sihver, Lembit; Niita, Koji

2012-01-01

Microdosimetric quantities such as lineal energy are generally considered to be better indices than linear energy transfer (LET) for expressing the relative biological effectiveness (RBE) of high charge and energy particles. To calculate their probability densities (PD) in macroscopic matter, it is necessary to integrate microdosimetric tools such as track-structure simulation codes with macroscopic particle transport simulation codes. As an integration approach, the mathematical model for calculating the PD of microdosimetric quantities developed based on track-structure simulations was incorporated into the macroscopic particle transport simulation code PHITS (Particle and Heavy Ion Transport code System). The improved PHITS enables the PD in macroscopic matter to be calculated within a reasonable computation time, while taking their stochastic nature into account. The microdosimetric function of PHITS was applied to biological dose estimation for charged-particle therapy and risk estimation for astronauts. The former application was performed in combination with the microdosimetric kinetic model, while the latter employed the radiation quality factor expressed as a function of lineal energy. Owing to the unique features of the microdosimetric function, the improved PHITS has the potential to establish more sophisticated systems for radiological protection in space as well as for the treatment planning of charged-particle therapy.

Characteristics of Combined Submerged Membrane Bioreactor with Granular Activated Carbon (GAC) in Treating Lineal Alkylbenzene Sulphonates (LAS) Wastewater

NASA Astrophysics Data System (ADS)

Guo, Jifeng; Xia, Siqing; Lu, Yanjun

2010-11-01

A combined MBR (cMBR) with granular activated carbon (GAC) was used as a backbone system to treat the synthetic lineal alkylbenzene sulphonates (LAS) wastewater. The GAC was added in the MBR to improve the resistance of membrane fouling. A parallel conventional MBR (pMBR) without the GAC was run to give a contrast. The results of the process demonstrate that the cMBR process was more efficient than pMBR. It was found that the TMP changes of the cMBR were slower than the pMBR. The results demonstrated that the cMBRs membrane was better than the pMBR's after a clean period run. It was the GAC scrubbing to the membrane that delayed the membrane fouling of the cMBR. Variable critical flux was found in MBR, which showed that the cMBR could make the critical flux better than pMBR in the run time, but GAC could not improve the critical flux at the end of the period for the severe membrane fouling. Based on this theory, a variable critical flux (J) of MBR was put forward, and the relationship of J with time (t) was: J = 16.081e-0.0177t.

Ajuste de parámetros libres en teorías de campos camaleones a partir de espectros de nubes moleculares galácticas y experimentos terrestres

NASA Astrophysics Data System (ADS)

Teppa Pannia, F. A.; Landau, S. J.

Resultados recientes, basados en el análisis de espectros moleculares de nubes galácticas a través del método del amoníaco, han arrojado nuevos límites sobre la variación del parámetro adimensional μ=m_e /m_p. Los resultados indican Δ μ/μ = (μ_{obs}-μ_{lab})/μ_{lab}= (2.2± 0.4_{est} ±0.3_{sist}) times 10 ^{-8}, en acuerdo con una variación no nula de dicha cantidad (Levshakov et al. 2010). En este trabajo, motivado por los datos astronómicos, estudiamos la solución lineal del modelo teórico fenomenológico de campo escalar camaleón, presentado por Mota y Shaw (2007), que predice variaciones en μ. Con el fin de cotejar estas predicciones con los resultados observacionales, utilizamos datos de experimentos terrestres que testean violaciones al Principio de Equivalencia para analizar el valor de los parámetros libres presentes en el modelo. El trabajo realizado muestra que la solución estudiada no se puede ajustar a los datos experimentales, sugiriendo que el modelo lineal debe ser descartado para explicar las observaciones astronómicas. FULL TEXT IN SPANISH

Estimating Causal Effects of Education Interventions Using a Two-Rating Regression Discontinuity Design: Lessons from a Simulation Study

ERIC Educational Resources Information Center

Porter, Kristin E.; Reardon, Sean F.; Unlu, Fatih; Bloom, Howard S.; Robinson-Cimpian, Joseph P.

2014-01-01

A valuable extension of the single-rating regression discontinuity design (RDD) is a multiple-rating RDD (MRRDD). To date, four main methods have been used to estimate average treatment effects at the multiple treatment frontiers of an MRRDD: the "surface" method, the "frontier" method, the "binding-score" method, and…

Development of Multiple Regression Equations To Predict Fourth Graders' Achievement in Reading and Selected Content Areas.

ERIC Educational Resources Information Center

Hafner, Lawrence E.

A study developed a multiple regression prediction equation for each of six selected achievement variables in a popular standardized test of achievement. Subjects, 42 fourth-grade pupils randomly selected across several classes in a large elementary school in a north Florida city, were administered several standardized tests to determine predictor…

Physical and Cognitive-Affective Factors Associated with Fatigue in Individuals with Fibromyalgia: A Multiple Regression Analysis

ERIC Educational Resources Information Center

Muller, Veronica; Brooks, Jessica; Tu, Wei-Mo; Moser, Erin; Lo, Chu-Ling; Chan, Fong

2015-01-01

Purpose: The main objective of this study was to determine the extent to which physical and cognitive-affective factors are associated with fibromyalgia (FM) fatigue. Method: A quantitative descriptive design using correlation techniques and multiple regression analysis. The participants consisted of 302 members of the National Fibromyalgia &…

Latent Variable Regression 4-Level Hierarchical Model Using Multisite Multiple-Cohorts Longitudinal Data. CRESST Report 801

ERIC Educational Resources Information Center

Choi, Kilchan

2011-01-01

This report explores a new latent variable regression 4-level hierarchical model for monitoring school performance over time using multisite multiple-cohorts longitudinal data. This kind of data set has a 4-level hierarchical structure: time-series observation nested within students who are nested within different cohorts of students. These…

What Is Wrong with ANOVA and Multiple Regression? Analyzing Sentence Reading Times with Hierarchical Linear Models

ERIC Educational Resources Information Center

Richter, Tobias

2006-01-01

Most reading time studies using naturalistic texts yield data sets characterized by a multilevel structure: Sentences (sentence level) are nested within persons (person level). In contrast to analysis of variance and multiple regression techniques, hierarchical linear models take the multilevel structure of reading time data into account. They…

Some Applied Research Concerns Using Multiple Linear Regression Analysis.

ERIC Educational Resources Information Center

Newman, Isadore; Fraas, John W.

The intention of this paper is to provide an overall reference on how a researcher can apply multiple linear regression in order to utilize the advantages that it has to offer. The advantages and some concerns expressed about the technique are examined. A number of practical ways by which researchers can deal with such concerns as…

A Spreadsheet Tool for Learning the Multiple Regression F-Test, T-Tests, and Multicollinearity

ERIC Educational Resources Information Center

Martin, David

2008-01-01

This note presents a spreadsheet tool that allows teachers the opportunity to guide students towards answering on their own questions related to the multiple regression F-test, the t-tests, and multicollinearity. The note demonstrates approaches for using the spreadsheet that might be appropriate for three different levels of statistics classes,…

Predicting Final GPA of Graduate School Students: Comparing Artificial Neural Networking and Simultaneous Multiple Regression

ERIC Educational Resources Information Center

Anderson, Joan L.

2006-01-01

Data from graduate student applications at a large Western university were used to determine which factors were the best predictors of success in graduate school, as defined by cumulative graduate grade point average. Two statistical models were employed and compared: artificial neural networking and simultaneous multiple regression. Both models…

Computational Tools for Probing Interactions in Multiple Linear Regression, Multilevel Modeling, and Latent Curve Analysis

ERIC Educational Resources Information Center

Preacher, Kristopher J.; Curran, Patrick J.; Bauer, Daniel J.

2006-01-01

Simple slopes, regions of significance, and confidence bands are commonly used to evaluate interactions in multiple linear regression (MLR) models, and the use of these techniques has recently been extended to multilevel or hierarchical linear modeling (HLM) and latent curve analysis (LCA). However, conducting these tests and plotting the…

Regression Models for the Analysis of Longitudinal Gaussian Data from Multiple Sources

PubMed Central

O’Brien, Liam M.; Fitzmaurice, Garrett M.

2006-01-01

We present a regression model for the joint analysis of longitudinal multiple source Gaussian data. Longitudinal multiple source data arise when repeated measurements are taken from two or more sources, and each source provides a measure of the same underlying variable and on the same scale. This type of data generally produces a relatively large number of observations per subject; thus estimation of an unstructured covariance matrix often may not be possible. We consider two methods by which parsimonious models for the covariance can be obtained for longitudinal multiple source data. The methods are illustrated with an example of multiple informant data arising from a longitudinal interventional trial in psychiatry. PMID:15726666

Interpretation of commonly used statistical regression models.

PubMed

Kasza, Jessica; Wolfe, Rory

2014-01-01

A review of some regression models commonly used in respiratory health applications is provided in this article. Simple linear regression, multiple linear regression, logistic regression and ordinal logistic regression are considered. The focus of this article is on the interpretation of the regression coefficients of each model, which are illustrated through the application of these models to a respiratory health research study. © 2013 The Authors. Respirology © 2013 Asian Pacific Society of Respirology.

Applied Multiple Linear Regression: A General Research Strategy

ERIC Educational Resources Information Center

Smith, Brandon B.

1969-01-01

Illustrates some of the basic concepts and procedures for using regression analysis in experimental design, analysis of variance, analysis of covariance, and curvilinear regression. Applications to evaluation of instruction and vocational education programs are illustrated. (GR)

Population heterogeneity in the salience of multiple risk factors for adolescent delinquency.

PubMed

Lanza, Stephanie T; Cooper, Brittany R; Bray, Bethany C

2014-03-01

To present mixture regression analysis as an alternative to more standard regression analysis for predicting adolescent delinquency. We demonstrate how mixture regression analysis allows for the identification of population subgroups defined by the salience of multiple risk factors. We identified population subgroups (i.e., latent classes) of individuals based on their coefficients in a regression model predicting adolescent delinquency from eight previously established risk indices drawn from the community, school, family, peer, and individual levels. The study included N = 37,763 10th-grade adolescents who participated in the Communities That Care Youth Survey. Standard, zero-inflated, and mixture Poisson and negative binomial regression models were considered. Standard and mixture negative binomial regression models were selected as optimal. The five-class regression model was interpreted based on the class-specific regression coefficients, indicating that risk factors had varying salience across classes of adolescents. Standard regression showed that all risk factors were significantly associated with delinquency. Mixture regression provided more nuanced information, suggesting a unique set of risk factors that were salient for different subgroups of adolescents. Implications for the design of subgroup-specific interventions are discussed. Copyright © 2014 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Estimating Causal Effects of Education Interventions Using a Two-Rating Regression Discontinuity Design: Lessons from a Simulation Study and an Application

ERIC Educational Resources Information Center

Porter, Kristin E.; Reardon, Sean F.; Unlu, Fatih; Bloom, Howard S.; Cimpian, Joseph R.

2017-01-01

A valuable extension of the single-rating regression discontinuity design (RDD) is a multiple-rating RDD (MRRDD). To date, four main methods have been used to estimate average treatment effects at the multiple treatment frontiers of an MRRDD: the "surface" method, the "frontier" method, the "binding-score" method, and…

How Variables Uncorrelated with the Dependent Variable Can Actually Make Excellent Predictors: The Important Suppressor Variable Case.

ERIC Educational Resources Information Center

Woolley, Kristin K.

Many researchers are unfamiliar with suppressor variables and how they operate in multiple regression analyses. This paper describes the role suppressor variables play in a multiple regression model and provides practical examples that explain how they can change research results. A variable that when added as another predictor increases the total…

Death Anxiety as a Predictor of Posttraumatic Stress Levels among Individuals with Spinal Cord Injuries

ERIC Educational Resources Information Center

Martz, Erin

2004-01-01

Because the onset of a spinal cord injury may involve a brush with death and because serious injury and disability can act as a reminder of death, death anxiety was examined as a predictor of posttraumatic stress levels among individuals with disabilities. This cross-sectional study used multiple regression and multivariate multiple regression to…

Multicollinearity is a red herring in the search for moderator variables: A guide to interpreting moderated multiple regression models and a critique of Iacobucci, Schneider, Popovich, and Bakamitsos (2016).

PubMed

McClelland, Gary H; Irwin, Julie R; Disatnik, David; Sivan, Liron

2017-02-01

Multicollinearity is irrelevant to the search for moderator variables, contrary to the implications of Iacobucci, Schneider, Popovich, and Bakamitsos (Behavior Research Methods, 2016, this issue). Multicollinearity is like the red herring in a mystery novel that distracts the statistical detective from the pursuit of a true moderator relationship. We show multicollinearity is completely irrelevant for tests of moderator variables. Furthermore, readers of Iacobucci et al. might be confused by a number of their errors. We note those errors, but more positively, we describe a variety of methods researchers might use to test and interpret their moderated multiple regression models, including two-stage testing, mean-centering, spotlighting, orthogonalizing, and floodlighting without regard to putative issues of multicollinearity. We cite a number of recent studies in the psychological literature in which the researchers used these methods appropriately to test, to interpret, and to report their moderated multiple regression models. We conclude with a set of recommendations for the analysis and reporting of moderated multiple regression that should help researchers better understand their models and facilitate generalizations across studies.

Use of principal-component, correlation, and stepwise multiple-regression analyses to investigate selected physical and hydraulic properties of carbonate-rock aquifers

USGS Publications Warehouse

Brown, C. Erwin

1993-01-01

Correlation analysis in conjunction with principal-component and multiple-regression analyses were applied to laboratory chemical and petrographic data to assess the usefulness of these techniques in evaluating selected physical and hydraulic properties of carbonate-rock aquifers in central Pennsylvania. Correlation and principal-component analyses were used to establish relations and associations among variables, to determine dimensions of property variation of samples, and to filter the variables containing similar information. Principal-component and correlation analyses showed that porosity is related to other measured variables and that permeability is most related to porosity and grain size. Four principal components are found to be significant in explaining the variance of data. Stepwise multiple-regression analysis was used to see how well the measured variables could predict porosity and (or) permeability for this suite of rocks. The variation in permeability and porosity is not totally predicted by the other variables, but the regression is significant at the 5% significance level. ?? 1993.

[Establishment of multiple regression model for virulence factors of Saccharomyces albicans by random amplified polymorphic DNA bands].

PubMed

Liu, Qi; Wu, Youcong; Yuan, Youhua; Bai, Li; Niu, Kun

2011-12-01

To research the relationship between the virulence factors of Saccharomyces albicans (S. albicans) and the random amplified polymorphic DNA (RAPD) bands of them, and establish the regression model by multiple regression analysis. Extracellular phospholipase, secreted proteinase, ability to generate germ tubes and adhere to oral mucosal cells of 92 strains of S. albicans were measured in vitro; RAPD-polymerase chain reaction (RAPD-PCR) was used to get their bands. Multiple regression for virulence factors of S. albicans and RAPD-PCR bands was established. The extracellular phospholipase activity was associated with 4 RAPD bands: 350, 450, 650 and 1 300 bp (P < 0.05); secreted proteinase activity of S. albicans was associated with 2 bands: 350 and 1 200 bp (P < 0.05); the ability of germ tube produce was associated with 2 bands: 400 and 550 bp (P < 0.05). Some RAPD bands will reflect the virulence factors of S. albicans indirectly. These bands would contain some important messages for regulation of S. albicans virulence factors.

Simultaneous multiple non-crossing quantile regression estimation using kernel constraints

PubMed Central

Liu, Yufeng; Wu, Yichao

2011-01-01

Quantile regression (QR) is a very useful statistical tool for learning the relationship between the response variable and covariates. For many applications, one often needs to estimate multiple conditional quantile functions of the response variable given covariates. Although one can estimate multiple quantiles separately, it is of great interest to estimate them simultaneously. One advantage of simultaneous estimation is that multiple quantiles can share strength among them to gain better estimation accuracy than individually estimated quantile functions. Another important advantage of joint estimation is the feasibility of incorporating simultaneous non-crossing constraints of QR functions. In this paper, we propose a new kernel-based multiple QR estimation technique, namely simultaneous non-crossing quantile regression (SNQR). We use kernel representations for QR functions and apply constraints on the kernel coefficients to avoid crossing. Both unregularised and regularised SNQR techniques are considered. Asymptotic properties such as asymptotic normality of linear SNQR and oracle properties of the sparse linear SNQR are developed. Our numerical results demonstrate the competitive performance of our SNQR over the original individual QR estimation. PMID:22190842

The Quasi Government: Hybrid Organizations with Both Government and Private Sector Legal Characteristics

DTIC Science & Technology

2005-05-18

Legal Services Corporation , for example, would be a quago, while the Red Cross would be a quango. Whatever the value of the quago/quango designations...especially in the comparative international literature on corporate organizations, it shall not be used here. This report follows the lineal spectrum...evidenced in the establishment of the Communications Satellite Corporation (ComSat) in 1962. Congress, in this instance, created a private, for

Monitoring heavy metal Cr in soil based on hyperspectral data using regression analysis

NASA Astrophysics Data System (ADS)

Zhang, Ningyu; Xu, Fuyun; Zhuang, Shidong; He, Changwei

2016-10-01

Heavy metal pollution in soils is one of the most critical problems in the global ecology and environment safety nowadays. Hyperspectral remote sensing and its application is capable of high speed, low cost, less risk and less damage, and provides a good method for detecting heavy metals in soil. This paper proposed a new idea of applying regression analysis of stepwise multiple regression between the spectral data and monitoring the amount of heavy metal Cr by sample points in soil for environmental protection. In the measurement, a FieldSpec HandHeld spectroradiometer is used to collect reflectance spectra of sample points over the wavelength range of 325-1075 nm. Then the spectral data measured by the spectroradiometer is preprocessed to reduced the influence of the external factors, and the preprocessed methods include first-order differential equation, second-order differential equation and continuum removal method. The algorithms of stepwise multiple regression are established accordingly, and the accuracy of each equation is tested. The results showed that the accuracy of first-order differential equation works best, which makes it feasible to predict the content of heavy metal Cr by using stepwise multiple regression.

Forecasting USAF JP-8 Fuel Needs

DTIC Science & Technology

2009-03-01

versus complex ones. When we consider long -term forecasts, 5-years in this case, multiple regression outperforms ANN modeling within the specified...with more simple and easy-to-implement methods, versus complex ones. When we consider long -term 5-year forecasts, our multiple regression model...effort. The insight and experience was certainly appreciated. Special thanks to my Turkish peers for their continuous support and help during this long

The Overall Odds Ratio as an Intuitive Effect Size Index for Multiple Logistic Regression: Examination of Further Refinements

ERIC Educational Resources Information Center

Le, Huy; Marcus, Justin

2012-01-01

This study used Monte Carlo simulation to examine the properties of the overall odds ratio (OOR), which was recently introduced as an index for overall effect size in multiple logistic regression. It was found that the OOR was relatively independent of study base rate and performed better than most commonly used R-square analogs in indexing model…

Future Performance Trend Indicators: A Current Value Approach to Human Resources Accounting. Report III. Multivariate Predictions of Organizational Performance Across Time.

ERIC Educational Resources Information Center

Pecorella, Patricia A.; Bowers, David G.

Multiple regression in a double cross-validated design was used to predict two performance measures (total variable expense and absence rate) by multi-month period in five industrial firms. The regressions do cross-validate, and produce multiple coefficients which display both concurrent and predictive effects, peaking 18 months to two years…

Using multiple calibration sets to improve the quantitative accuracy of partial least squares (PLS) regression on open-path fourier transform infrared (OP/FT-IR) spectra of ammonia over wide concentration ranges

USDA-ARS?s Scientific Manuscript database

A technique of using multiple calibration sets in partial least squares regression (PLS) was proposed to improve the quantitative determination of ammonia from open-path Fourier transform infrared spectra. The spectra were measured near animal farms, and the path-integrated concentration of ammonia...

FIRE: an SPSS program for variable selection in multiple linear regression analysis via the relative importance of predictors.

PubMed

Lorenzo-Seva, Urbano; Ferrando, Pere J

2011-03-01

We provide an SPSS program that implements currently recommended techniques and recent developments for selecting variables in multiple linear regression analysis via the relative importance of predictors. The approach consists of: (1) optimally splitting the data for cross-validation, (2) selecting the final set of predictors to be retained in the equation regression, and (3) assessing the behavior of the chosen model using standard indices and procedures. The SPSS syntax, a short manual, and data files related to this article are available as supplemental materials from brm.psychonomic-journals.org/content/supplemental.

Standardized Regression Coefficients as Indices of Effect Sizes in Meta-Analysis

ERIC Educational Resources Information Center

Kim, Rae Seon

2011-01-01

When conducting a meta-analysis, it is common to find many collected studies that report regression analyses, because multiple regression analysis is widely used in many fields. Meta-analysis uses effect sizes drawn from individual studies as a means of synthesizing a collection of results. However, indices of effect size from regression analyses…

Evaluation of the comprehensive palatability of Japanese sake paired with dishes by multiple regression analysis based on subdomains.

PubMed

Nakamura, Ryo; Nakano, Kumiko; Tamura, Hiroyasu; Mizunuma, Masaki; Fushiki, Tohru; Hirata, Dai

2017-08-01

Many factors contribute to palatability. In order to evaluate the palatability of Japanese alcohol sake paired with certain dishes by integrating multiple factors, here we applied an evaluation method previously reported for palatability of cheese by multiple regression analysis based on 3 subdomain factors (rewarding, cultural, and informational). We asked 94 Japanese participants/subjects to evaluate the palatability of sake (1st evaluation/E1 for the first cup, 2nd/E2 and 3rd/E3 for the palatability with aftertaste/afterglow of certain dishes) and to respond to a questionnaire related to 3 subdomains. In E1, 3 factors were extracted by a factor analysis, and the subsequent multiple regression analyses indicated that the palatability of sake was interpreted by mainly the rewarding. Further, the results of attribution-dissections in E1 indicated that 2 factors (rewarding and informational) contributed to the palatability. Finally, our results indicated that the palatability of sake was influenced by the dish eaten just before drinking.

Correlation and simple linear regression.

PubMed

Eberly, Lynn E

2007-01-01

This chapter highlights important steps in using correlation and simple linear regression to address scientific questions about the association of two continuous variables with each other. These steps include estimation and inference, assessing model fit, the connection between regression and ANOVA, and study design. Examples in microbiology are used throughout. This chapter provides a framework that is helpful in understanding more complex statistical techniques, such as multiple linear regression, linear mixed effects models, logistic regression, and proportional hazards regression.

Seafloor morphology related to recent tectonics in the Alboran Sea Basin

NASA Astrophysics Data System (ADS)

Vázquez, Juan-Tomás; Estrada, Ferran; Vegas, Ramon; Ercilla, Gemma; Medialdea, Teresa; d'Acremont, Elia; Alonso, Belen; Fernández-Salas, Luis-Miguel; Gómez-Ballesteros, María; Somoza, Luis; Bárcenas, Patricia; Palomino, Desirée; Gorini, Christian

2014-05-01

A detailed geomorphological study of the northern part of the Alboran Sea Basin has been realized based on the combined analysis of multibeam swath bathymetric data and medium to very high resolution seismic profiles (singled Sparker, Airgun, TOPAS and Atlas PARASOUND P35). This has enabled us to define several tectonic-related seafloor features and their role in the recent tectonics. The observed morpho-tectonic features correspond to: i) lineal scarps with a wide range of dimensions and following several trends ,WNW-ESE, NE-SW, NNE-SSW and N-S; ii) NE-SW to NNE-SSW-oriented compressive ridges; iii) ENE-WSW to NE-SW-striking antiforms; iv) NNE-SSW-oriented lineal depressions; v) rhomb-shaped depressions; vi) lineal valleys, canyons and gullies with WNW-ESE, and N-S orientations; and vii) N-S directed dissected valleys, canyons and gullies. Three families of faults and related folds, with NE-SW, WNW-ESE and NNE-SSW to N-S have been interpreted within this geomorphological scheme. The NE-SW family corresponds to: a) major scarps in both flanks of the Alboran Ridge and b) the offshore prolongation of La Serrata Fault, and both have been considered as a set of sinistral strike-slip faults. To this family, some compressive ridges, antiforms and occasionally reverse faults have been correlated. The WNW-ESE family corresponds to a set of faulted valleys (occasionally with rhomb-shaped depressions), fault scarps and linear inflection points occurring in the northern Alboran margin and the Yusuf-Habibas corridor. This family has been interpreted as transtensive dextral strike-slip faults. The NNE-SSW to N-S family corresponds to a penetrative system of linear fault scarps and tectonic depressions that cross-cut the Alboran Ridge and the Djibouti-Motril marginal plateau. This family can be considered as more recent since it offsets the other two families and shows a minor importance with regard to the main reliefs. This communication is a contribution to the Spanish R + D + I projects MONTERA (CTM2009-14157-C02) and MOWER (CTM2012-39599-C03), the Project MOSAIC of scientific excellence of Andalusia(P06-RNM-01594) and the European ESF projects EUROFLEET SARAS and TOPOMED_SPAIN (CGL2008-03474-E).

Factor analysis and multiple regression between topography and precipitation on Jeju Island, Korea

NASA Astrophysics Data System (ADS)

Um, Myoung-Jin; Yun, Hyeseon; Jeong, Chang-Sam; Heo, Jun-Haeng

2011-11-01

SummaryIn this study, new factors that influence precipitation were extracted from geographic variables using factor analysis, which allow for an accurate estimation of orographic precipitation. Correlation analysis was also used to examine the relationship between nine topographic variables from digital elevation models (DEMs) and the precipitation in Jeju Island. In addition, a spatial analysis was performed in order to verify the validity of the regression model. From the results of the correlation analysis, it was found that all of the topographic variables had a positive correlation with the precipitation. The relations between the variables also changed in accordance with a change in the precipitation duration. However, upon examining the correlation matrix, no significant relationship between the latitude and the aspect was found. According to the factor analysis, eight topographic variables (latitude being the exception) were found to have a direct influence on the precipitation. Three factors were then extracted from the eight topographic variables. By directly comparing the multiple regression model with the factors (model 1) to the multiple regression model with the topographic variables (model 3), it was found that model 1 did not violate the limits of statistical significance and multicollinearity. As such, model 1 was considered to be appropriate for estimating the precipitation when taking into account the topography. In the study of model 1, the multiple regression model using factor analysis was found to be the best method for estimating the orographic precipitation on Jeju Island.

Weather Impact on Airport Arrival Meter Fix Throughput

NASA Technical Reports Server (NTRS)

Wang, Yao

2017-01-01

Time-based flow management provides arrival aircraft schedules based on arrival airport conditions, airport capacity, required spacing, and weather conditions. In order to meet a scheduled time at which arrival aircraft can cross an airport arrival meter fix prior to entering the airport terminal airspace, air traffic controllers make regulations on air traffic. Severe weather may create an airport arrival bottleneck if one or more of airport arrival meter fixes are partially or completely blocked by the weather and the arrival demand has not been reduced accordingly. Under these conditions, aircraft are frequently being put in holding patterns until they can be rerouted. A model that predicts the weather impacted meter fix throughput may help air traffic controllers direct arrival flows into the airport more efficiently, minimizing arrival meter fix congestion. This paper presents an analysis of air traffic flows across arrival meter fixes at the Newark Liberty International Airport (EWR). Several scenarios of weather impacted EWR arrival fix flows are described. Furthermore, multiple linear regression and regression tree ensemble learning approaches for translating multiple sector Weather Impacted Traffic Indexes (WITI) to EWR arrival meter fix throughputs are examined. These weather translation models are developed and validated using the EWR arrival flight and weather data for the period of April-September in 2014. This study also compares the performance of the regression tree ensemble with traditional multiple linear regression models for estimating the weather impacted throughputs at each of the EWR arrival meter fixes. For all meter fixes investigated, the results from the regression tree ensemble weather translation models show a stronger correlation between model outputs and observed meter fix throughputs than that produced from multiple linear regression method.

Determination of osteoporosis risk factors using a multiple logistic regression model in postmenopausal Turkish women.

PubMed

Akkus, Zeki; Camdeviren, Handan; Celik, Fatma; Gur, Ali; Nas, Kemal

2005-09-01

To determine the risk factors of osteoporosis using a multiple binary logistic regression method and to assess the risk variables for osteoporosis, which is a major and growing health problem in many countries. We presented a case-control study, consisting of 126 postmenopausal healthy women as control group and 225 postmenopausal osteoporotic women as the case group. The study was carried out in the Department of Physical Medicine and Rehabilitation, Dicle University, Diyarbakir, Turkey between 1999-2002. The data from the 351 participants were collected using a standard questionnaire that contains 43 variables. A multiple logistic regression model was then used to evaluate the data and to find the best regression model. We classified 80.1% (281/351) of the participants using the regression model. Furthermore, the specificity value of the model was 67% (84/126) of the control group while the sensitivity value was 88% (197/225) of the case group. We found the distribution of residual values standardized for final model to be exponential using the Kolmogorow-Smirnow test (p=0.193). The receiver operating characteristic curve was found successful to predict patients with risk for osteoporosis. This study suggests that low levels of dietary calcium intake, physical activity, education, and longer duration of menopause are independent predictors of the risk of low bone density in our population. Adequate dietary calcium intake in combination with maintaining a daily physical activity, increasing educational level, decreasing birth rate, and duration of breast-feeding may contribute to healthy bones and play a role in practical prevention of osteoporosis in Southeast Anatolia. In addition, the findings of the present study indicate that the use of multivariate statistical method as a multiple logistic regression in osteoporosis, which maybe influenced by many variables, is better than univariate statistical evaluation.

Practical Guidance for Conducting Mediation Analysis With Multiple Mediators Using Inverse Odds Ratio Weighting

PubMed Central

Nguyen, Quynh C.; Osypuk, Theresa L.; Schmidt, Nicole M.; Glymour, M. Maria; Tchetgen Tchetgen, Eric J.

2015-01-01

Despite the recent flourishing of mediation analysis techniques, many modern approaches are difficult to implement or applicable to only a restricted range of regression models. This report provides practical guidance for implementing a new technique utilizing inverse odds ratio weighting (IORW) to estimate natural direct and indirect effects for mediation analyses. IORW takes advantage of the odds ratio's invariance property and condenses information on the odds ratio for the relationship between the exposure (treatment) and multiple mediators, conditional on covariates, by regressing exposure on mediators and covariates. The inverse of the covariate-adjusted exposure-mediator odds ratio association is used to weight the primary analytical regression of the outcome on treatment. The treatment coefficient in such a weighted regression estimates the natural direct effect of treatment on the outcome, and indirect effects are identified by subtracting direct effects from total effects. Weighting renders treatment and mediators independent, thereby deactivating indirect pathways of the mediators. This new mediation technique accommodates multiple discrete or continuous mediators. IORW is easily implemented and is appropriate for any standard regression model, including quantile regression and survival analysis. An empirical example is given using data from the Moving to Opportunity (1994–2002) experiment, testing whether neighborhood context mediated the effects of a housing voucher program on obesity. Relevant Stata code (StataCorp LP, College Station, Texas) is provided. PMID:25693776

A Statistical Multimodel Ensemble Approach to Improving Long-Range Forecasting in Pakistan

DTIC Science & Technology

2012-03-01

Impact of global warming on monsoon variability in Pakistan. J. Anim. Pl. Sci., 21, no. 1, 107–110. Gillies, S., T. Murphree, and D. Meyer, 2012...are generated by multiple regression models that relate globally distributed oceanic and atmospheric predictors to local predictands. The...generated by multiple regression models that relate globally distributed oceanic and atmospheric predictors to local predictands. The predictands are

Suppression Situations in Multiple Linear Regression

ERIC Educational Resources Information Center

Shieh, Gwowen

2006-01-01

This article proposes alternative expressions for the two most prevailing definitions of suppression without resorting to the standardized regression modeling. The formulation provides a simple basis for the examination of their relationship. For the two-predictor regression, the author demonstrates that the previous results in the literature are…

Reduction of shading-derived artifacts in skin chromophore imaging without measurements or assumptions about the shape of the subject

NASA Astrophysics Data System (ADS)

Yoshida, Kenichiro; Nishidate, Izumi; Ojima, Nobutoshi; Iwata, Kayoko

2014-01-01

To quantitatively evaluate skin chromophores over a wide region of curved skin surface, we propose an approach that suppresses the effect of the shading-derived error in the reflectance on the estimation of chromophore concentrations, without sacrificing the accuracy of that estimation. In our method, we use multiple regression analysis, assuming the absorbance spectrum as the response variable and the extinction coefficients of melanin, oxygenated hemoglobin, and deoxygenated hemoglobin as the predictor variables. The concentrations of melanin and total hemoglobin are determined from the multiple regression coefficients using compensation formulae (CF) based on the diffuse reflectance spectra derived from a Monte Carlo simulation. To suppress the shading-derived error, we investigated three different combinations of multiple regression coefficients for the CF. In vivo measurements with the forearm skin demonstrated that the proposed approach can reduce the estimation errors that are due to shading-derived errors in the reflectance. With the best combination of multiple regression coefficients, we estimated that the ratio of the error to the chromophore concentrations is about 10%. The proposed method does not require any measurements or assumptions about the shape of the subjects; this is an advantage over other studies related to the reduction of shading-derived errors.

Quantitative assessment of cervical vertebral maturation using cone beam computed tomography in Korean girls.

PubMed

Byun, Bo-Ram; Kim, Yong-Il; Yamaguchi, Tetsutaro; Maki, Koutaro; Son, Woo-Sung

2015-01-01

This study was aimed to examine the correlation between skeletal maturation status and parameters from the odontoid process/body of the second vertebra and the bodies of third and fourth cervical vertebrae and simultaneously build multiple regression models to be able to estimate skeletal maturation status in Korean girls. Hand-wrist radiographs and cone beam computed tomography (CBCT) images were obtained from 74 Korean girls (6-18 years of age). CBCT-generated cervical vertebral maturation (CVM) was used to demarcate the odontoid process and the body of the second cervical vertebra, based on the dentocentral synchondrosis. Correlation coefficient analysis and multiple linear regression analysis were used for each parameter of the cervical vertebrae (P < 0.05). Forty-seven of 64 parameters from CBCT-generated CVM (independent variables) exhibited statistically significant correlations (P < 0.05). The multiple regression model with the greatest R (2) had six parameters (PH2/W2, UW2/W2, (OH+AH2)/LW2, UW3/LW3, D3, and H4/W4) as independent variables with a variance inflation factor (VIF) of <2. CBCT-generated CVM was able to include parameters from the second cervical vertebral body and odontoid process, respectively, for the multiple regression models. This suggests that quantitative analysis might be used to estimate skeletal maturation status.

An Evaluation of a Modified Simulated Annealing Algorithm for Various Formulations

DTIC Science & Technology

1990-08-01

trials of the K"h Markov chain, is sufficiently close to q(c, ), the stationary distribution at ck la (Lk,c,,) - q(c.) < epsilon Requiring the final...Wiley and Sons . Aarts, E. H. L., & Van Laarhoven, P. J. M. (1985). Statistical cooling: A general approach to combinatorial optimization problems...Birkhoff, G. (1946). Tres observaciones sobre el algebra lineal, Rev. Univ. Nac. TucumanSer. A, 5, 147-151. Bohr, Niels (1913). Old quantum theory

The Overall Response of Composite Materials Undergoing Large Deformations

DTIC Science & Technology

1992-10-15

University, December 11, 1991. "Propiedades effectivas de materiales compuestos ." & "Propiedades effectivas de materiales compuestos no-lineales...uncoupled. As we will see in the ensuing discussions, this inter -mode coupling is one of the intrinsic features of laminated (and other anisotropic...3 15 fir -0.5 M3 0- 10 0 o0 2 3 4 5 Fig. 3. The inter -relations between the shear stress f and the longitudinal shear strain ,, of the

Using Regression Equations Built from Summary Data in the Psychological Assessment of the Individual Case: Extension to Multiple Regression

ERIC Educational Resources Information Center

Crawford, John R.; Garthwaite, Paul H.; Denham, Annie K.; Chelune, Gordon J.

2012-01-01

Regression equations have many useful roles in psychological assessment. Moreover, there is a large reservoir of published data that could be used to build regression equations; these equations could then be employed to test a wide variety of hypotheses concerning the functioning of individual cases. This resource is currently underused because…

NEUDOSE: A CubeSat Mission for Dosimetry of Charged Particles and Neutrons in Low-Earth Orbit.

PubMed

Hanu, A R; Barberiz, J; Bonneville, D; Byun, S H; Chen, L; Ciambella, C; Dao, E; Deshpande, V; Garnett, R; Hunter, S D; Jhirad, A; Johnston, E M; Kordic, M; Kurnell, M; Lopera, L; McFadden, M; Melnichuk, A; Nguyen, J; Otto, A; Scott, R; Wagner, D L; Wiendels, M

2017-01-01

During space missions, astronauts are exposed to a stream of energetic and highly ionizing radiation particles that can suppress immune system function, increase cancer risks and even induce acute radiation syndrome if the exposure is large enough. As human exploration goals shift from missions in low-Earth orbit (LEO) to long-duration interplanetary missions, radiation protection remains one of the key technological issues that must be resolved. In this work, we introduce the NEUtron DOSimetry & Exploration (NEUDOSE) CubeSat mission, which will provide new measurements of dose and space radiation quality factors to improve the accuracy of cancer risk projections for current and future space missions. The primary objective of the NEUDOSE CubeSat is to map the in situ lineal energy spectra produced by charged particles and neutrons in LEO where most of the preparatory activities for future interplanetary missions are currently taking place. To perform these measurements, the NEUDOSE CubeSat is equipped with the Charged & Neutral Particle Tissue Equivalent Proportional Counter (CNP-TEPC), an advanced radiation monitoring instrument that uses active coincidence techniques to separate the interactions of charged particles and neutrons in real time. The NEUDOSE CubeSat, currently under development at McMaster University, provides a modern approach to test the CNP-TEPC instrument directly in the unique environment of outer space while simultaneously collecting new georeferenced lineal energy spectra of the radiation environment in LEO.

Functional Regression Models for Epistasis Analysis of Multiple Quantitative Traits.

PubMed

Zhang, Futao; Xie, Dan; Liang, Meimei; Xiong, Momiao

2016-04-01

To date, most genetic analyses of phenotypes have focused on analyzing single traits or analyzing each phenotype independently. However, joint epistasis analysis of multiple complementary traits will increase statistical power and improve our understanding of the complicated genetic structure of the complex diseases. Despite their importance in uncovering the genetic structure of complex traits, the statistical methods for identifying epistasis in multiple phenotypes remains fundamentally unexplored. To fill this gap, we formulate a test for interaction between two genes in multiple quantitative trait analysis as a multiple functional regression (MFRG) in which the genotype functions (genetic variant profiles) are defined as a function of the genomic position of the genetic variants. We use large-scale simulations to calculate Type I error rates for testing interaction between two genes with multiple phenotypes and to compare the power with multivariate pairwise interaction analysis and single trait interaction analysis by a single variate functional regression model. To further evaluate performance, the MFRG for epistasis analysis is applied to five phenotypes of exome sequence data from the NHLBI's Exome Sequencing Project (ESP) to detect pleiotropic epistasis. A total of 267 pairs of genes that formed a genetic interaction network showed significant evidence of epistasis influencing five traits. The results demonstrate that the joint interaction analysis of multiple phenotypes has a much higher power to detect interaction than the interaction analysis of a single trait and may open a new direction to fully uncovering the genetic structure of multiple phenotypes.

Elbow joint angle and elbow movement velocity estimation using NARX-multiple layer perceptron neural network model with surface EMG time domain parameters.

PubMed

Raj, Retheep; Sivanandan, K S

2017-01-01

Estimation of elbow dynamics has been the object of numerous investigations. In this work a solution is proposed for estimating elbow movement velocity and elbow joint angle from Surface Electromyography (SEMG) signals. Here the Surface Electromyography signals are acquired from the biceps brachii muscle of human hand. Two time-domain parameters, Integrated EMG (IEMG) and Zero Crossing (ZC), are extracted from the Surface Electromyography signal. The relationship between the time domain parameters, IEMG and ZC with elbow angular displacement and elbow angular velocity during extension and flexion of the elbow are studied. A multiple input-multiple output model is derived for identifying the kinematics of elbow. A Nonlinear Auto Regressive with eXogenous inputs (NARX) structure based multiple layer perceptron neural network (MLPNN) model is proposed for the estimation of elbow joint angle and elbow angular velocity. The proposed NARX MLPNN model is trained using Levenberg-marquardt based algorithm. The proposed model is estimating the elbow joint angle and elbow movement angular velocity with appreciable accuracy. The model is validated using regression coefficient value (R). The average regression coefficient value (R) obtained for elbow angular displacement prediction is 0.9641 and for the elbow anglular velocity prediction is 0.9347. The Nonlinear Auto Regressive with eXogenous inputs (NARX) structure based multiple layer perceptron neural networks (MLPNN) model can be used for the estimation of angular displacement and movement angular velocity of the elbow with good accuracy.

Monte Carlo calculation of the maximum therapeutic gain of tumor antivascular alpha therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, Chen-Yu; Oborn, Bradley M.; Guatelli, Susanna

Purpose: Metastatic melanoma lesions experienced marked regression after systemic targeted alpha therapy in a phase 1 clinical trial. This unexpected response was ascribed to tumor antivascular alpha therapy (TAVAT), in which effective tumor regression is achieved by killing endothelial cells (ECs) in tumor capillaries and, thus, depriving cancer cells of nutrition and oxygen. The purpose of this paper is to quantitatively analyze the therapeutic efficacy and safety of TAVAT by building up the testing Monte Carlo microdosimetric models. Methods: Geant4 was adapted to simulate the spatial nonuniform distribution of the alpha emitter {sup 213}Bi. The intraluminal model was designed tomore » simulate the background dose to normal tissue capillary ECs from the nontargeted activity in the blood. The perivascular model calculates the EC dose from the activity bound to the perivascular cancer cells. The key parameters are the probability of an alpha particle traversing an EC nucleus, the energy deposition, the lineal energy transfer, and the specific energy. These results were then applied to interpret the clinical trial. Cell survival rate and therapeutic gain were determined. Results: The specific energy for an alpha particle hitting an EC nucleus in the intraluminal and perivascular models is 0.35 and 0.37 Gy, respectively. As the average probability of traversal in these models is 2.7% and 1.1%, the mean specific energy per decay drops to 1.0 cGy and 0.4 cGy, which demonstrates that the source distribution has a significant impact on the dose. Using the melanoma clinical trial activity of 25 mCi, the dose to tumor EC nucleus is found to be 3.2 Gy and to a normal capillary EC nucleus to be 1.8 cGy. These data give a maximum therapeutic gain of about 180 and validate the TAVAT concept. Conclusions: TAVAT can deliver a cytotoxic dose to tumor capillaries without being toxic to normal tissue capillaries.« less

Polymorphisms in genes coding for HSP-70 are associated with gastric cancer and duodenal ulcer in a population at high risk of gastric cancer in Costa Rica.

PubMed

Ferrer-Ferrer, Maura; Malespín-Bendaña, Wendy; Ramírez, Vanessa; González, María Isabel; Carvajal, Adriana; Une, Clas

2013-08-01

Costa Rica has among the highest incidence and mortality rates for gastric cancer worldwide. The reasons for this are largely unknown. Polymorphisms of inflammatory response genes including genes encoding heat shock proteins (HSP) have been shown to be associated with the risk of gastric cancer in some populations. This study addresses the possible association between the HSP70-2 +1267 and HSP70-Hom +2437 polymorphisms and the risk of developing gastric cancer in a high-risk population in Costa Rica. DNA from 39 individuals diagnosed with gastric cancer, 79 healthy controls, 55 individuals with chronic gastritis and 52 individuals with duodenal ulcer was genotyped for the polymorphisms HSP70-2 +1267 and HSP70-Hom +2437 by RFLP. Logistic regression analysis was used to determine possible associations with the diagnoses and lineal regression analysis to determine associations with blood pepsinogen (PGs) levels as measured by serology. The GA genotype of HSP70-2 was associated with increased risk of gastric cancer (OR = 3.42; 95% CI = 1.27-9.21; p = 0.015) and duodenal ulcer (OR = 2.57; 95% CI = 1.03-6.36; p = 0.042) as compared to the GG genotype. Persons with C carrier genotypes of HSP70-Hom were significantly less susceptible to gastric cancer than those with the TT genotype (OR = 0.29; 95% CI = 0.09-0.87; p = 0.027). The C carrier genotype was associated with lower PGI concentrations but none of the polymorphisms were associated with PGI/PGII. Polymorphisms of HSP70 genes are associated with the development of gastric cancer and duodenal ulcers in a population at high risk for gastric cancer in Costa Rica. Copyright © 2013 IMSS. Published by Elsevier Inc. All rights reserved.

Regression Commonality Analysis: A Technique for Quantitative Theory Building

ERIC Educational Resources Information Center

Nimon, Kim; Reio, Thomas G., Jr.

2011-01-01

When it comes to multiple linear regression analysis (MLR), it is common for social and behavioral science researchers to rely predominately on beta weights when evaluating how predictors contribute to a regression model. Presenting an underutilized statistical technique, this article describes how organizational researchers can use commonality…

Precision Efficacy Analysis for Regression.

ERIC Educational Resources Information Center

Brooks, Gordon P.

When multiple linear regression is used to develop a prediction model, sample size must be large enough to ensure stable coefficients. If the derivation sample size is inadequate, the model may not predict well for future subjects. The precision efficacy analysis for regression (PEAR) method uses a cross- validity approach to select sample sizes…

Guidelines and Procedures for Computing Time-Series Suspended-Sediment Concentrations and Loads from In-Stream Turbidity-Sensor and Streamflow Data

USGS Publications Warehouse

Rasmussen, Patrick P.; Gray, John R.; Glysson, G. Douglas; Ziegler, Andrew C.

2009-01-01

In-stream continuous turbidity and streamflow data, calibrated with measured suspended-sediment concentration data, can be used to compute a time series of suspended-sediment concentration and load at a stream site. Development of a simple linear (ordinary least squares) regression model for computing suspended-sediment concentrations from instantaneous turbidity data is the first step in the computation process. If the model standard percentage error (MSPE) of the simple linear regression model meets a minimum criterion, this model should be used to compute a time series of suspended-sediment concentrations. Otherwise, a multiple linear regression model using paired instantaneous turbidity and streamflow data is developed and compared to the simple regression model. If the inclusion of the streamflow variable proves to be statistically significant and the uncertainty associated with the multiple regression model results in an improvement over that for the simple linear model, the turbidity-streamflow multiple linear regression model should be used to compute a suspended-sediment concentration time series. The computed concentration time series is subsequently used with its paired streamflow time series to compute suspended-sediment loads by standard U.S. Geological Survey techniques. Once an acceptable regression model is developed, it can be used to compute suspended-sediment concentration beyond the period of record used in model development with proper ongoing collection and analysis of calibration samples. Regression models to compute suspended-sediment concentrations are generally site specific and should never be considered static, but they represent a set period in a continually dynamic system in which additional data will help verify any change in sediment load, type, and source.

Scaling effects on spring phenology detections from MODIS data at multiple spatial resolutions over the contiguous United States

NASA Astrophysics Data System (ADS)

Peng, Dailiang; Zhang, Xiaoyang; Zhang, Bing; Liu, Liangyun; Liu, Xinjie; Huete, Alfredo R.; Huang, Wenjiang; Wang, Siyuan; Luo, Shezhou; Zhang, Xiao; Zhang, Helin

2017-10-01

Land surface phenology (LSP) has been widely retrieved from satellite data at multiple spatial resolutions, but the spatial scaling effects on LSP detection are poorly understood. In this study, we collected enhanced vegetation index (EVI, 250 m) from collection 6 MOD13Q1 product over the contiguous United States (CONUS) in 2007 and 2008, and generated a set of multiple spatial resolution EVI data by resampling 250 m to 2 × 250 m and 3 × 250 m, 4 × 250 m, …, 35 × 250 m. These EVI time series were then used to detect the start of spring season (SOS) at various spatial resolutions. Further the SOS variation across scales was examined at each coarse resolution grid (35 × 250 m ≈ 8 km, refer to as reference grid) and ecoregion. Finally, the SOS scaling effects were associated with landscape fragment, proportion of primary land cover type, and spatial variability of seasonal greenness variation within each reference grid. The results revealed the influences of satellite spatial resolutions on SOS retrievals and the related impact factors. Specifically, SOS significantly varied lineally or logarithmically across scales although the relationship could be either positive or negative. The overall SOS values averaged from spatial resolutions between 250 m and 35 × 250 m at large ecosystem regions were generally similar with a difference less than 5 days, while the SOS values within the reference grid could differ greatly in some local areas. Moreover, the standard deviation of SOS across scales in the reference grid was less than 5 days in more than 70% of area over the CONUS, which was smaller in northeastern than in southern and western regions. The SOS scaling effect was significantly associated with heterogeneity of vegetation properties characterized using land landscape fragment, proportion of primary land cover type, and spatial variability of seasonal greenness variation, but the latter was the most important impact factor.

The prediction of intelligence in preschool children using alternative models to regression.

PubMed

Finch, W Holmes; Chang, Mei; Davis, Andrew S; Holden, Jocelyn E; Rothlisberg, Barbara A; McIntosh, David E

2011-12-01

Statistical prediction of an outcome variable using multiple independent variables is a common practice in the social and behavioral sciences. For example, neuropsychologists are sometimes called upon to provide predictions of preinjury cognitive functioning for individuals who have suffered a traumatic brain injury. Typically, these predictions are made using standard multiple linear regression models with several demographic variables (e.g., gender, ethnicity, education level) as predictors. Prior research has shown conflicting evidence regarding the ability of such models to provide accurate predictions of outcome variables such as full-scale intelligence (FSIQ) test scores. The present study had two goals: (1) to demonstrate the utility of a set of alternative prediction methods that have been applied extensively in the natural sciences and business but have not been frequently explored in the social sciences and (2) to develop models that can be used to predict premorbid cognitive functioning in preschool children. Predictions of Stanford-Binet 5 FSIQ scores for preschool-aged children is used to compare the performance of a multiple regression model with several of these alternative methods. Results demonstrate that classification and regression trees provided more accurate predictions of FSIQ scores than does the more traditional regression approach. Implications of these results are discussed.

Optimization of fixture layouts of glass laser optics using multiple kernel regression.

PubMed

Su, Jianhua; Cao, Enhua; Qiao, Hong

2014-05-10

We aim to build an integrated fixturing model to describe the structural properties and thermal properties of the support frame of glass laser optics. Therefore, (a) a near global optimal set of clamps can be computed to minimize the surface shape error of the glass laser optic based on the proposed model, and (b) a desired surface shape error can be obtained by adjusting the clamping forces under various environmental temperatures based on the model. To construct the model, we develop a new multiple kernel learning method and call it multiple kernel support vector functional regression. The proposed method uses two layer regressions to group and order the data sources by the weights of the kernels and the factors of the layers. Because of that, the influences of the clamps and the temperature can be evaluated by grouping them into different layers.

Prediction of anthropometric foot characteristics in children.

PubMed

Morrison, Stewart C; Durward, Brian R; Watt, Gordon F; Donaldson, Malcolm D C

2009-01-01

The establishment of growth reference values is needed in pediatric practice where pathologic conditions can have a detrimental effect on the growth and development of the pediatric foot. This study aims to use multiple regression to evaluate the effects of multiple predictor variables (height, age, body mass, and gender) on anthropometric characteristics of the peripubescent foot. Two hundred children aged 9 to 12 years were recruited, and three anthropometric measurements of the pediatric foot were recorded (foot length, forefoot width, and navicular height). Multiple regression analysis was conducted, and coefficients for gender, height, and body mass all had significant relationships for the prediction of forefoot width and foot length (P < or = .05, r > or = 0.7). The coefficients for gender and body mass were not significant for the prediction of navicular height (P > or = .05), whereas height was (P < or = .05). Normative growth reference values and prognostic regression equations are presented for the peripubescent foot.

Birthweight Related Factors in Northwestern Iran: Using Quantile Regression Method.

PubMed

Fallah, Ramazan; Kazemnejad, Anoshirvan; Zayeri, Farid; Shoghli, Alireza

2015-11-18

Birthweight is one of the most important predicting indicators of the health status in adulthood. Having a balanced birthweight is one of the priorities of the health system in most of the industrial and developed countries. This indicator is used to assess the growth and health status of the infants. The aim of this study was to assess the birthweight of the neonates by using quantile regression in Zanjan province. This analytical descriptive study was carried out using pre-registered (March 2010 - March 2012) data of neonates in urban/rural health centers of Zanjan province using multiple-stage cluster sampling. Data were analyzed using multiple linear regressions andquantile regression method and SAS 9.2 statistical software. From 8456 newborn baby, 4146 (49%) were female. The mean age of the mothers was 27.1±5.4 years. The mean birthweight of the neonates was 3104 ± 431 grams. Five hundred and seventy-three patients (6.8%) of the neonates were less than 2500 grams. In all quantiles, gestational age of neonates (p<0.05), weight and educational level of the mothers (p<0.05) showed a linear significant relationship with the i of the neonates. However, sex and birth rank of the neonates, mothers age, place of residence (urban/rural) and career were not significant in all quantiles (p>0.05). This study revealed the results of multiple linear regression and quantile regression were not identical. We strictly recommend the use of quantile regression when an asymmetric response variable or data with outliers is available.

Birthweight Related Factors in Northwestern Iran: Using Quantile Regression Method

PubMed Central

Fallah, Ramazan; Kazemnejad, Anoshirvan; Zayeri, Farid; Shoghli, Alireza

2016-01-01

Introduction: Birthweight is one of the most important predicting indicators of the health status in adulthood. Having a balanced birthweight is one of the priorities of the health system in most of the industrial and developed countries. This indicator is used to assess the growth and health status of the infants. The aim of this study was to assess the birthweight of the neonates by using quantile regression in Zanjan province. Methods: This analytical descriptive study was carried out using pre-registered (March 2010 - March 2012) data of neonates in urban/rural health centers of Zanjan province using multiple-stage cluster sampling. Data were analyzed using multiple linear regressions andquantile regression method and SAS 9.2 statistical software. Results: From 8456 newborn baby, 4146 (49%) were female. The mean age of the mothers was 27.1±5.4 years. The mean birthweight of the neonates was 3104 ± 431 grams. Five hundred and seventy-three patients (6.8%) of the neonates were less than 2500 grams. In all quantiles, gestational age of neonates (p<0.05), weight and educational level of the mothers (p<0.05) showed a linear significant relationship with the i of the neonates. However, sex and birth rank of the neonates, mothers age, place of residence (urban/rural) and career were not significant in all quantiles (p>0.05). Conclusion: This study revealed the results of multiple linear regression and quantile regression were not identical. We strictly recommend the use of quantile regression when an asymmetric response variable or data with outliers is available. PMID:26925889

Weighted regression analysis and interval estimators

Treesearch

Donald W. Seegrist

1974-01-01

A method for deriving the weighted least squares estimators for the parameters of a multiple regression model. Confidence intervals for expected values, and prediction intervals for the means of future samples are given.

Estimation of premorbid general fluid intelligence using traditional Chinese reading performance in Taiwanese samples.

PubMed

Chen, Ying-Jen; Ho, Meng-Yang; Chen, Kwan-Ju; Hsu, Chia-Fen; Ryu, Shan-Jin

2009-08-01

The aims of the present study were to (i) investigate if traditional Chinese word reading ability can be used for estimating premorbid general intelligence; and (ii) to provide multiple regression equations for estimating premorbid performance on Raven's Standard Progressive Matrices (RSPM), using age, years of education and Chinese Graded Word Reading Test (CGWRT) scores as predictor variables. Four hundred and twenty-six healthy volunteers (201 male, 225 female), aged 16-93 years (mean +/- SD, 41.92 +/- 18.19 years) undertook the tests individually under supervised conditions. Seventy percent of subjects were randomly allocated to the derivation group (n = 296), and the rest to the validation group (n = 130). RSPM score was positively correlated with CGWRT score and years of education. RSPM and CGWRT scores and years of education were also inversely correlated with age, but the declining trend for RSPM performance against age was steeper than that for CGWRT performance. Separate multiple regression equations were derived for estimating RSPM scores using different combinations of age, years of education, and CGWRT score for both groups. The multiple regression coefficient of each equation ranged from 0.71 to 0.80 with the standard error of estimate between 7 and 8 RSPM points. When fitting the data of one group to the equations derived from its counterpart group, the cross-validation multiple regression coefficients ranged from 0.71 to 0.79. There were no significant differences in the 'predicted-obtained' RSPM discrepancies between any equations. The regression equations derived in the present study may provide a basis for estimating premorbid RSPM performance.

Overall Preference of Running Shoes Can Be Predicted by Suitable Perception Factors Using a Multiple Regression Model.

PubMed

Tay, Cheryl Sihui; Sterzing, Thorsten; Lim, Chen Yen; Ding, Rui; Kong, Pui Wah

2017-05-01

This study examined (a) the strength of four individual footwear perception factors to influence the overall preference of running shoes and (b) whether these perception factors satisfied the nonmulticollinear assumption in a regression model. Running footwear must fulfill multiple functional criteria to satisfy its potential users. Footwear perception factors, such as fit and cushioning, are commonly used to guide shoe design and development, but it is unclear whether running-footwear users are able to differentiate one factor from another. One hundred casual runners assessed four running shoes on a 15-cm visual analogue scale for four footwear perception factors (fit, cushioning, arch support, and stability) as well as for overall preference during a treadmill running protocol. Diagnostic tests showed an absence of multicollinearity between factors, where values for tolerance ranged from .36 to .72, corresponding to variance inflation factors of 2.8 to 1.4. The multiple regression model of these four footwear perception variables accounted for 77.7% to 81.6% of variance in overall preference, with each factor explaining a unique part of the total variance. Casual runners were able to rate each footwear perception factor separately, thus assigning each factor a true potential to improve overall preference for the users. The results also support the use of a multiple regression model of footwear perception factors to predict overall running shoe preference. Regression modeling is a useful tool for running-shoe manufacturers to more precisely evaluate how individual factors contribute to the subjective assessment of running footwear.

A population-based study on the association between rheumatoid arthritis and voice problems.

PubMed

Hah, J Hun; An, Soo-Youn; Sim, Songyong; Kim, So Young; Oh, Dong Jun; Park, Bumjung; Kim, Sung-Gyun; Choi, Hyo Geun

2016-07-01

The objective of this study was to investigate whether rheumatoid arthritis increases the frequency of organic laryngeal lesions and the subjective voice complaint rate in those with no organic laryngeal lesion. We performed a cross-sectional study using the data from 19,368 participants (418 rheumatoid arthritis patients and 18,950 controls) of the 2008-2011 Korea National Health and Nutrition Examination Survey. The associations between rheumatoid arthritis and organic laryngeal lesions/subjective voice complaints were analyzed using simple/multiple logistic regression analysis with complex sample adjusting for confounding factors, including age, sex, smoking status, stress level, and body mass index, which could provoke voice problems. Vocal nodules, vocal polyp, and vocal palsy were not associated with rheumatoid arthritis in a multiple regression analysis, and only laryngitis showed a positive association (adjusted odds ratio, 1.59; 95 % confidence interval, 1.01-2.52; P = 0.047). Rheumatoid arthritis was associated with subjective voice discomfort in a simple regression analysis, but not in a multiple regression analysis. Participants with rheumatoid arthritis were older, more often female, and had higher stress levels than those without rheumatoid arthritis. These factors were associated with subjective voice complaints in both simple and multiple regression analyses. Rheumatoid arthritis was not associated with organic laryngeal diseases except laryngitis. Rheumatoid arthritis did not increase the odds ratio for subjective voice complaints. Voice problems in participants with rheumatoid arthritis originated from the characteristics of the rheumatoid arthritis group (higher mean age, female sex, and stress level) rather than rheumatoid arthritis itself.

Predicting MHC-II binding affinity using multiple instance regression

PubMed Central

EL-Manzalawy, Yasser; Dobbs, Drena; Honavar, Vasant

2011-01-01

Reliably predicting the ability of antigen peptides to bind to major histocompatibility complex class II (MHC-II) molecules is an essential step in developing new vaccines. Uncovering the amino acid sequence correlates of the binding affinity of MHC-II binding peptides is important for understanding pathogenesis and immune response. The task of predicting MHC-II binding peptides is complicated by the significant variability in their length. Most existing computational methods for predicting MHC-II binding peptides focus on identifying a nine amino acids core region in each binding peptide. We formulate the problems of qualitatively and quantitatively predicting flexible length MHC-II peptides as multiple instance learning and multiple instance regression problems, respectively. Based on this formulation, we introduce MHCMIR, a novel method for predicting MHC-II binding affinity using multiple instance regression. We present results of experiments using several benchmark datasets that show that MHCMIR is competitive with the state-of-the-art methods for predicting MHC-II binding peptides. An online web server that implements the MHCMIR method for MHC-II binding affinity prediction is freely accessible at http://ailab.cs.iastate.edu/mhcmir. PMID:20855923

Multiple-Shrinkage Multinomial Probit Models with Applications to Simulating Geographies in Public Use Data.

PubMed

Burgette, Lane F; Reiter, Jerome P

2013-06-01

Multinomial outcomes with many levels can be challenging to model. Information typically accrues slowly with increasing sample size, yet the parameter space expands rapidly with additional covariates. Shrinking all regression parameters towards zero, as often done in models of continuous or binary response variables, is unsatisfactory, since setting parameters equal to zero in multinomial models does not necessarily imply "no effect." We propose an approach to modeling multinomial outcomes with many levels based on a Bayesian multinomial probit (MNP) model and a multiple shrinkage prior distribution for the regression parameters. The prior distribution encourages the MNP regression parameters to shrink toward a number of learned locations, thereby substantially reducing the dimension of the parameter space. Using simulated data, we compare the predictive performance of this model against two other recently-proposed methods for big multinomial models. The results suggest that the fully Bayesian, multiple shrinkage approach can outperform these other methods. We apply the multiple shrinkage MNP to simulating replacement values for areal identifiers, e.g., census tract indicators, in order to protect data confidentiality in public use datasets.

Environmental Impact Statement of Operation and Maintenance, Lake Shelbyville, Illinois. Volume I.

DTIC Science & Technology

1975-01-01

utilities. These include.’ 56.2 miles of power lines, 45.5 miles of telephone lines, and 17,000 lineal feet of gas and oil pipelines. One cemetery...lake shoreline from erosive wave action. it also serves to separate the potentially conflicting activi- ties of fishermen and power boaters. No-wake...were valuable to archeo- logical kowledge of the Midwest. The lake was filled after conclusive 11-54 results of the 1963-67 archeological digging had

Building Trust: The Challenge of Building Partnership Capacity in U.S.-Mexico Military Relations

DTIC Science & Technology

2014-03-01

culture devoted to an extreme lineal hierarchy, especially during this developmental process, tends to hinder the ability of the member to retain and use...www.sedena.gob.mx/index.php/ educacion -militar. 25 educational objectives. These extreme vertical culture becomes a huge road block for those who want others to...in Veracruz and the acts of valor by the Mexican people— especially by the naval cadets—were fully embedded throughout the formation of all Mexican

Quantile Regression in the Study of Developmental Sciences

ERIC Educational Resources Information Center

Petscher, Yaacov; Logan, Jessica A. R.

2014-01-01

Linear regression analysis is one of the most common techniques applied in developmental research, but only allows for an estimate of the average relations between the predictor(s) and the outcome. This study describes quantile regression, which provides estimates of the relations between the predictor(s) and outcome, but across multiple points of…

Maintenance Operations in Mission Oriented Protective Posture Level IV (MOPPIV)

DTIC Science & Technology

1987-10-01

Repair FADAC Printed Circuit Board ............. 6 3. Data Analysis Techniques ............................. 6 a. Multiple Linear Regression... ANALYSIS /DISCUSSION ............................... 12 1. Exa-ple of Regression Analysis ..................... 12 S2. Regression results for all tasks...6 * TABLE 9. Task Grouping for Analysis ........................ 7 "TABXLE 10. Remove/Replace H60A3 Power Pack................. 8 TABLE

Interactions between cadmium and decabrominated diphenyl ether on blood cells count in rats-Multiple factorial regression analysis.

PubMed

Curcic, Marijana; Buha, Aleksandra; Stankovic, Sanja; Milovanovic, Vesna; Bulat, Zorica; Đukić-Ćosić, Danijela; Antonijević, Evica; Vučinić, Slavica; Matović, Vesna; Antonijevic, Biljana

2017-02-01

The objective of this study was to assess toxicity of Cd and BDE-209 mixture on haematological parameters in subacutely exposed rats and to determine the presence and type of interactions between these two chemicals using multiple factorial regression analysis. Furthermore, for the assessment of interaction type, an isobologram based methodology was applied and compared with multiple factorial regression analysis. Chemicals were given by oral gavage to the male Wistar rats weighing 200-240g for 28days. Animals were divided in 16 groups (8/group): control vehiculum group, three groups of rats were treated with 2.5, 7.5 or 15mg Cd/kg/day. These doses were chosen on the bases of literature data and reflect relatively high Cd environmental exposure, three groups of rats were treated with 1000, 2000 or 4000mg BDE-209/kg/bw/day, doses proved to induce toxic effects in rats. Furthermore, nine groups of animals were treated with different mixtures of Cd and BDE-209 containing doses of Cd and BDE-209 stated above. Blood samples were taken at the end of experiment and red blood cells, white blood cells and platelets counts were determined. For interaction assessment multiple factorial regression analysis and fitted isobologram approach were used. In this study, we focused on multiple factorial regression analysis as a method for interaction assessment. We also investigated the interactions between Cd and BDE-209 by the derived model for the description of the obtained fitted isobologram curves. Current study indicated that co-exposure to Cd and BDE-209 can result in significant decrease in RBC count, increase in WBC count and decrease in PLT count, when compared with controls. Multiple factorial regression analysis used for the assessment of interactions type between Cd and BDE-209 indicated synergism for the effect on RBC count and no interactions i.e. additivity for the effects on WBC and PLT counts. On the other hand, isobologram based approach showed slight antagonism for the effects on RBC and WBC while no interactions were proved for the joint effect on PLT count. These results confirm that the assessment of interactions between chemicals in the mixture greatly depends on the concept or method used for this evaluation. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Estimation of 1RM for knee extension based on the maximal isometric muscle strength and body composition.

PubMed

Kanada, Yoshikiyo; Sakurai, Hiroaki; Sugiura, Yoshito; Arai, Tomoaki; Koyama, Soichiro; Tanabe, Shigeo

2017-11-01

[Purpose] To create a regression formula in order to estimate 1RM for knee extensors, based on the maximal isometric muscle strength measured using a hand-held dynamometer and data regarding the body composition. [Subjects and Methods] Measurement was performed in 21 healthy males in their twenties to thirties. Single regression analysis was performed, with measurement values representing 1RM and the maximal isometric muscle strength as dependent and independent variables, respectively. Furthermore, multiple regression analysis was performed, with data regarding the body composition incorporated as another independent variable, in addition to the maximal isometric muscle strength. [Results] Through single regression analysis with the maximal isometric muscle strength as an independent variable, the following regression formula was created: 1RM (kg)=0.714 + 0.783 × maximal isometric muscle strength (kgf). On multiple regression analysis, only the total muscle mass was extracted. [Conclusion] A highly accurate regression formula to estimate 1RM was created based on both the maximal isometric muscle strength and body composition. Using a hand-held dynamometer and body composition analyzer, it was possible to measure these items in a short time, and obtain clinically useful results.

Data Mining Methods Applied to Flight Operations Quality Assurance Data: A Comparison to Standard Statistical Methods

NASA Technical Reports Server (NTRS)

Stolzer, Alan J.; Halford, Carl

2007-01-01

In a previous study, multiple regression techniques were applied to Flight Operations Quality Assurance-derived data to develop parsimonious model(s) for fuel consumption on the Boeing 757 airplane. The present study examined several data mining algorithms, including neural networks, on the fuel consumption problem and compared them to the multiple regression results obtained earlier. Using regression methods, parsimonious models were obtained that explained approximately 85% of the variation in fuel flow. In general data mining methods were more effective in predicting fuel consumption. Classification and Regression Tree methods reported correlation coefficients of .91 to .92, and General Linear Models and Multilayer Perceptron neural networks reported correlation coefficients of about .99. These data mining models show great promise for use in further examining large FOQA databases for operational and safety improvements.

Cross Validation of Selection of Variables in Multiple Regression.

DTIC Science & Technology

1979-12-01

55 vii CROSS VALIDATION OF SELECTION OF VARIABLES IN MULTIPLE REGRESSION I Introduction Background Long term DoD planning gcals...028545024 .31109000 BF * SS - .008700618 .0471961 Constant - .70977903 85.146786 55 had adequate predictive capabilities; the other two models (the...71ZCO F111D Control 54 73EGO FlIID Computer, General Purpose 55 73EPO FII1D Converter-Multiplexer 56 73HAO flllD Stabilizer Platform 57 73HCO F1ID

Quantitative Assessment of Cervical Vertebral Maturation Using Cone Beam Computed Tomography in Korean Girls

PubMed Central

Byun, Bo-Ram; Kim, Yong-Il; Maki, Koutaro; Son, Woo-Sung

2015-01-01

This study was aimed to examine the correlation between skeletal maturation status and parameters from the odontoid process/body of the second vertebra and the bodies of third and fourth cervical vertebrae and simultaneously build multiple regression models to be able to estimate skeletal maturation status in Korean girls. Hand-wrist radiographs and cone beam computed tomography (CBCT) images were obtained from 74 Korean girls (6–18 years of age). CBCT-generated cervical vertebral maturation (CVM) was used to demarcate the odontoid process and the body of the second cervical vertebra, based on the dentocentral synchondrosis. Correlation coefficient analysis and multiple linear regression analysis were used for each parameter of the cervical vertebrae (P < 0.05). Forty-seven of 64 parameters from CBCT-generated CVM (independent variables) exhibited statistically significant correlations (P < 0.05). The multiple regression model with the greatest R 2 had six parameters (PH2/W2, UW2/W2, (OH+AH2)/LW2, UW3/LW3, D3, and H4/W4) as independent variables with a variance inflation factor (VIF) of <2. CBCT-generated CVM was able to include parameters from the second cervical vertebral body and odontoid process, respectively, for the multiple regression models. This suggests that quantitative analysis might be used to estimate skeletal maturation status. PMID:25878721

Comparing cluster-level dynamic treatment regimens using sequential, multiple assignment, randomized trials: Regression estimation and sample size considerations.

PubMed

NeCamp, Timothy; Kilbourne, Amy; Almirall, Daniel

2017-08-01

Cluster-level dynamic treatment regimens can be used to guide sequential treatment decision-making at the cluster level in order to improve outcomes at the individual or patient-level. In a cluster-level dynamic treatment regimen, the treatment is potentially adapted and re-adapted over time based on changes in the cluster that could be impacted by prior intervention, including aggregate measures of the individuals or patients that compose it. Cluster-randomized sequential multiple assignment randomized trials can be used to answer multiple open questions preventing scientists from developing high-quality cluster-level dynamic treatment regimens. In a cluster-randomized sequential multiple assignment randomized trial, sequential randomizations occur at the cluster level and outcomes are observed at the individual level. This manuscript makes two contributions to the design and analysis of cluster-randomized sequential multiple assignment randomized trials. First, a weighted least squares regression approach is proposed for comparing the mean of a patient-level outcome between the cluster-level dynamic treatment regimens embedded in a sequential multiple assignment randomized trial. The regression approach facilitates the use of baseline covariates which is often critical in the analysis of cluster-level trials. Second, sample size calculators are derived for two common cluster-randomized sequential multiple assignment randomized trial designs for use when the primary aim is a between-dynamic treatment regimen comparison of the mean of a continuous patient-level outcome. The methods are motivated by the Adaptive Implementation of Effective Programs Trial which is, to our knowledge, the first-ever cluster-randomized sequential multiple assignment randomized trial in psychiatry.

Adjusted variable plots for Cox's proportional hazards regression model.

PubMed

Hall, C B; Zeger, S L; Bandeen-Roche, K J

1996-01-01

Adjusted variable plots are useful in linear regression for outlier detection and for qualitative evaluation of the fit of a model. In this paper, we extend adjusted variable plots to Cox's proportional hazards model for possibly censored survival data. We propose three different plots: a risk level adjusted variable (RLAV) plot in which each observation in each risk set appears, a subject level adjusted variable (SLAV) plot in which each subject is represented by one point, and an event level adjusted variable (ELAV) plot in which the entire risk set at each failure event is represented by a single point. The latter two plots are derived from the RLAV by combining multiple points. In each point, the regression coefficient and standard error from a Cox proportional hazards regression is obtained by a simple linear regression through the origin fit to the coordinates of the pictured points. The plots are illustrated with a reanalysis of a dataset of 65 patients with multiple myeloma.

Analysis of Binary Adherence Data in the Setting of Polypharmacy: A Comparison of Different Approaches

PubMed Central

Esserman, Denise A.; Moore, Charity G.; Roth, Mary T.

2009-01-01

Older community dwelling adults often take multiple medications for numerous chronic diseases. Non-adherence to these medications can have a large public health impact. Therefore, the measurement and modeling of medication adherence in the setting of polypharmacy is an important area of research. We apply a variety of different modeling techniques (standard linear regression; weighted linear regression; adjusted linear regression; naïve logistic regression; beta-binomial (BB) regression; generalized estimating equations (GEE)) to binary medication adherence data from a study in a North Carolina based population of older adults, where each medication an individual was taking was classified as adherent or non-adherent. In addition, through simulation we compare these different methods based on Type I error rates, bias, power, empirical 95% coverage, and goodness of fit. We find that estimation and inference using GEE is robust to a wide variety of scenarios and we recommend using this in the setting of polypharmacy when adherence is dichotomously measured for multiple medications per person. PMID:20414358

Selection of a Geostatistical Method to Interpolate Soil Properties of the State Crop Testing Fields using Attributes of a Digital Terrain Model

NASA Astrophysics Data System (ADS)

Sahabiev, I. A.; Ryazanov, S. S.; Kolcova, T. G.; Grigoryan, B. R.

2018-03-01

The three most common techniques to interpolate soil properties at a field scale—ordinary kriging (OK), regression kriging with multiple linear regression drift model (RK + MLR), and regression kriging with principal component regression drift model (RK + PCR)—were examined. The results of the performed study were compiled into an algorithm of choosing the most appropriate soil mapping technique. Relief attributes were used as the auxiliary variables. When spatial dependence of a target variable was strong, the OK method showed more accurate interpolation results, and the inclusion of the auxiliary data resulted in an insignificant improvement in prediction accuracy. According to the algorithm, the RK + PCR method effectively eliminates multicollinearity of explanatory variables. However, if the number of predictors is less than ten, the probability of multicollinearity is reduced, and application of the PCR becomes irrational. In that case, the multiple linear regression should be used instead.

Genetic Programming Transforms in Linear Regression Situations

NASA Astrophysics Data System (ADS)

Castillo, Flor; Kordon, Arthur; Villa, Carlos

The chapter summarizes the use of Genetic Programming (GP) inMultiple Linear Regression (MLR) to address multicollinearity and Lack of Fit (LOF). The basis of the proposed method is applying appropriate input transforms (model respecification) that deal with these issues while preserving the information content of the original variables. The transforms are selected from symbolic regression models with optimal trade-off between accuracy of prediction and expressional complexity, generated by multiobjective Pareto-front GP. The chapter includes a comparative study of the GP-generated transforms with Ridge Regression, a variant of ordinary Multiple Linear Regression, which has been a useful and commonly employed approach for reducing multicollinearity. The advantages of GP-generated model respecification are clearly defined and demonstrated. Some recommendations for transforms selection are given as well. The application benefits of the proposed approach are illustrated with a real industrial application in one of the broadest empirical modeling areas in manufacturing - robust inferential sensors. The chapter contributes to increasing the awareness of the potential of GP in statistical model building by MLR.

A Solution to Separation and Multicollinearity in Multiple Logistic Regression

PubMed Central

Shen, Jianzhao; Gao, Sujuan

2010-01-01

In dementia screening tests, item selection for shortening an existing screening test can be achieved using multiple logistic regression. However, maximum likelihood estimates for such logistic regression models often experience serious bias or even non-existence because of separation and multicollinearity problems resulting from a large number of highly correlated items. Firth (1993, Biometrika, 80(1), 27–38) proposed a penalized likelihood estimator for generalized linear models and it was shown to reduce bias and the non-existence problems. The ridge regression has been used in logistic regression to stabilize the estimates in cases of multicollinearity. However, neither solves the problems for each other. In this paper, we propose a double penalized maximum likelihood estimator combining Firth’s penalized likelihood equation with a ridge parameter. We present a simulation study evaluating the empirical performance of the double penalized likelihood estimator in small to moderate sample sizes. We demonstrate the proposed approach using a current screening data from a community-based dementia study. PMID:20376286

A Solution to Separation and Multicollinearity in Multiple Logistic Regression.

PubMed

Shen, Jianzhao; Gao, Sujuan

2008-10-01

In dementia screening tests, item selection for shortening an existing screening test can be achieved using multiple logistic regression. However, maximum likelihood estimates for such logistic regression models often experience serious bias or even non-existence because of separation and multicollinearity problems resulting from a large number of highly correlated items. Firth (1993, Biometrika, 80(1), 27-38) proposed a penalized likelihood estimator for generalized linear models and it was shown to reduce bias and the non-existence problems. The ridge regression has been used in logistic regression to stabilize the estimates in cases of multicollinearity. However, neither solves the problems for each other. In this paper, we propose a double penalized maximum likelihood estimator combining Firth's penalized likelihood equation with a ridge parameter. We present a simulation study evaluating the empirical performance of the double penalized likelihood estimator in small to moderate sample sizes. We demonstrate the proposed approach using a current screening data from a community-based dementia study.

A regression technique for evaluation and quantification for water quality parameters from remote sensing data

NASA Technical Reports Server (NTRS)

Whitlock, C. H.; Kuo, C. Y.

1979-01-01

The objective of this paper is to define optical physics and/or environmental conditions under which the linear multiple-regression should be applicable. An investigation of the signal-response equations is conducted and the concept is tested by application to actual remote sensing data from a laboratory experiment performed under controlled conditions. Investigation of the signal-response equations shows that the exact solution for a number of optical physics conditions is of the same form as a linearized multiple-regression equation, even if nonlinear contributions from surface reflections, atmospheric constituents, or other water pollutants are included. Limitations on achieving this type of solution are defined.

RRegrs: an R package for computer-aided model selection with multiple regression models.

PubMed

Tsiliki, Georgia; Munteanu, Cristian R; Seoane, Jose A; Fernandez-Lozano, Carlos; Sarimveis, Haralambos; Willighagen, Egon L

2015-01-01

Predictive regression models can be created with many different modelling approaches. Choices need to be made for data set splitting, cross-validation methods, specific regression parameters and best model criteria, as they all affect the accuracy and efficiency of the produced predictive models, and therefore, raising model reproducibility and comparison issues. Cheminformatics and bioinformatics are extensively using predictive modelling and exhibit a need for standardization of these methodologies in order to assist model selection and speed up the process of predictive model development. A tool accessible to all users, irrespectively of their statistical knowledge, would be valuable if it tests several simple and complex regression models and validation schemes, produce unified reports, and offer the option to be integrated into more extensive studies. Additionally, such methodology should be implemented as a free programming package, in order to be continuously adapted and redistributed by others. We propose an integrated framework for creating multiple regression models, called RRegrs. The tool offers the option of ten simple and complex regression methods combined with repeated 10-fold and leave-one-out cross-validation. Methods include Multiple Linear regression, Generalized Linear Model with Stepwise Feature Selection, Partial Least Squares regression, Lasso regression, and Support Vector Machines Recursive Feature Elimination. The new framework is an automated fully validated procedure which produces standardized reports to quickly oversee the impact of choices in modelling algorithms and assess the model and cross-validation results. The methodology was implemented as an open source R package, available at https://www.github.com/enanomapper/RRegrs, by reusing and extending on the caret package. The universality of the new methodology is demonstrated using five standard data sets from different scientific fields. Its efficiency in cheminformatics and QSAR modelling is shown with three use cases: proteomics data for surface-modified gold nanoparticles, nano-metal oxides descriptor data, and molecular descriptors for acute aquatic toxicity data. The results show that for all data sets RRegrs reports models with equal or better performance for both training and test sets than those reported in the original publications. Its good performance as well as its adaptability in terms of parameter optimization could make RRegrs a popular framework to assist the initial exploration of predictive models, and with that, the design of more comprehensive in silico screening applications.Graphical abstractRRegrs is a computer-aided model selection framework for R multiple regression models; this is a fully validated procedure with application to QSAR modelling.

Analytical framework for reconstructing heterogeneous environmental variables from mammal community structure.

PubMed

Louys, Julien; Meloro, Carlo; Elton, Sarah; Ditchfield, Peter; Bishop, Laura C

2015-01-01

We test the performance of two models that use mammalian communities to reconstruct multivariate palaeoenvironments. While both models exploit the correlation between mammal communities (defined in terms of functional groups) and arboreal heterogeneity, the first uses a multiple multivariate regression of community structure and arboreal heterogeneity, while the second uses a linear regression of the principal components of each ecospace. The success of these methods means the palaeoenvironment of a particular locality can be reconstructed in terms of the proportions of heavy, moderate, light, and absent tree canopy cover. The linear regression is less biased, and more precisely and accurately reconstructs heavy tree canopy cover than the multiple multivariate model. However, the multiple multivariate model performs better than the linear regression for all other canopy cover categories. Both models consistently perform better than randomly generated reconstructions. We apply both models to the palaeocommunity of the Upper Laetolil Beds, Tanzania. Our reconstructions indicate that there was very little heavy tree cover at this site (likely less than 10%), with the palaeo-landscape instead comprising a mixture of light and absent tree cover. These reconstructions help resolve the previous conflicting palaeoecological reconstructions made for this site. Copyright © 2014 Elsevier Ltd. All rights reserved.

Building a new predictor for multiple linear regression technique-based corrective maintenance turnaround time.

PubMed

Cruz, Antonio M; Barr, Cameron; Puñales-Pozo, Elsa

2008-01-01

This research's main goals were to build a predictor for a turnaround time (TAT) indicator for estimating its values and use a numerical clustering technique for finding possible causes of undesirable TAT values. The following stages were used: domain understanding, data characterisation and sample reduction and insight characterisation. Building the TAT indicator multiple linear regression predictor and clustering techniques were used for improving corrective maintenance task efficiency in a clinical engineering department (CED). The indicator being studied was turnaround time (TAT). Multiple linear regression was used for building a predictive TAT value model. The variables contributing to such model were clinical engineering department response time (CE(rt), 0.415 positive coefficient), stock service response time (Stock(rt), 0.734 positive coefficient), priority level (0.21 positive coefficient) and service time (0.06 positive coefficient). The regression process showed heavy reliance on Stock(rt), CE(rt) and priority, in that order. Clustering techniques revealed the main causes of high TAT values. This examination has provided a means for analysing current technical service quality and effectiveness. In doing so, it has demonstrated a process for identifying areas and methods of improvement and a model against which to analyse these methods' effectiveness.

Effects of land cover, topography, and built structure on seasonal water quality at multiple spatial scales.

PubMed

Pratt, Bethany; Chang, Heejun

2012-03-30

The relationship among land cover, topography, built structure and stream water quality in the Portland Metro region of Oregon and Clark County, Washington areas, USA, is analyzed using ordinary least squares (OLS) and geographically weighted (GWR) multiple regression models. Two scales of analysis, a sectional watershed and a buffer, offered a local and a global investigation of the sources of stream pollutants. Model accuracy, measured by R(2) values, fluctuated according to the scale, season, and regression method used. While most wet season water quality parameters are associated with urban land covers, most dry season water quality parameters are related topographic features such as elevation and slope. GWR models, which take into consideration local relations of spatial autocorrelation, had stronger results than OLS regression models. In the multiple regression models, sectioned watershed results were consistently better than the sectioned buffer results, except for dry season pH and stream temperature parameters. This suggests that while riparian land cover does have an effect on water quality, a wider contributing area needs to be included in order to account for distant sources of pollutants. Copyright © 2012 Elsevier B.V. All rights reserved.

Development of a Multiple Linear Regression Model to Forecast Facility Electrical Consumption at an Air Force Base.

DTIC Science & Technology

1981-09-01

corresponds to the same square footage that consumed the electrical energy. 3. The basic assumptions of multiple linear regres- sion, as enumerated in...7. Data related to the sample of bases is assumed to be representative of bases in the population. Limitations Basic limitations on this research were... Ratemaking --Overview. Rand Report R-5894, Santa Monica CA, May 1977. Chatterjee, Samprit, and Bertram Price. Regression Analysis by Example. New York: John

Multiple-trait structured antedependence model to study the relationship between litter size and birth weight in pigs and rabbits.

PubMed

David, Ingrid; Garreau, Hervé; Balmisse, Elodie; Billon, Yvon; Canario, Laurianne

2017-01-20

Some genetic studies need to take into account correlations between traits that are repeatedly measured over time. Multiple-trait random regression models are commonly used to analyze repeated traits but suffer from several major drawbacks. In the present study, we developed a multiple-trait extension of the structured antedependence model (SAD) to overcome this issue and validated its usefulness by modeling the association between litter size (LS) and average birth weight (ABW) over parities in pigs and rabbits. The single-trait SAD model assumes that a random effect at time [Formula: see text] can be explained by the previous values of the random effect (i.e. at previous times). The proposed multiple-trait extension of the SAD model consists in adding a cross-antedependence parameter to the single-trait SAD model. This model can be easily fitted using ASReml and the OWN Fortran program that we have developed. In comparison with the random regression model, we used our multiple-trait SAD model to analyze the LS and ABW of 4345 litters from 1817 Large White sows and 8706 litters from 2286 L-1777 does over a maximum of five successive parities. For both species, the multiple-trait SAD fitted the data better than the random regression model. The difference between AIC of the two models (AIC_random regression-AIC_SAD) were equal to 7 and 227 for pigs and rabbits, respectively. A similar pattern of heritability and correlation estimates was obtained for both species. Heritabilities were lower for LS (ranging from 0.09 to 0.29) than for ABW (ranging from 0.23 to 0.39). The general trend was a decrease of the genetic correlation for a given trait between more distant parities. Estimates of genetic correlations between LS and ABW were negative and ranged from -0.03 to -0.52 across parities. No correlation was observed between the permanent environmental effects, except between the permanent environmental effects of LS and ABW of the same parity, for which the estimate of the correlation was strongly negative (ranging from -0.57 to -0.67). We demonstrated that application of our multiple-trait SAD model is feasible for studying several traits with repeated measurements and showed that it provided a better fit to the data than the random regression model.

5 CFR 591.219 - How does OPM compute shelter price indexes?

Code of Federal Regulations, 2014 CFR

2014-01-01

... estimates in hedonic regressions (a type of multiple regression) to compute for each COLA survey area the price index for rental and/or rental equivalent units of comparable quality and size between the COLA...

5 CFR 591.219 - How does OPM compute shelter price indexes?

Code of Federal Regulations, 2011 CFR

2011-01-01

... estimates in hedonic regressions (a type of multiple regression) to compute for each COLA survey area the price index for rental and/or rental equivalent units of comparable quality and size between the COLA...

5 CFR 591.219 - How does OPM compute shelter price indexes?

Code of Federal Regulations, 2013 CFR

2013-01-01

... estimates in hedonic regressions (a type of multiple regression) to compute for each COLA survey area the price index for rental and/or rental equivalent units of comparable quality and size between the COLA...

5 CFR 591.219 - How does OPM compute shelter price indexes?

Code of Federal Regulations, 2012 CFR

2012-01-01

... estimates in hedonic regressions (a type of multiple regression) to compute for each COLA survey area the price index for rental and/or rental equivalent units of comparable quality and size between the COLA...

Toward customer-centric organizational science: A common language effect size indicator for multiple linear regressions and regressions with higher-order terms.

PubMed

Krasikova, Dina V; Le, Huy; Bachura, Eric

2018-06-01

To address a long-standing concern regarding a gap between organizational science and practice, scholars called for more intuitive and meaningful ways of communicating research results to users of academic research. In this article, we develop a common language effect size index (CLβ) that can help translate research results to practice. We demonstrate how CLβ can be computed and used to interpret the effects of continuous and categorical predictors in multiple linear regression models. We also elaborate on how the proposed CLβ index is computed and used to interpret interactions and nonlinear effects in regression models. In addition, we test the robustness of the proposed index to violations of normality and provide means for computing standard errors and constructing confidence intervals around its estimates. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Modeling brook trout presence and absence from landscape variables using four different analytical methods

USGS Publications Warehouse

Steen, Paul J.; Passino-Reader, Dora R.; Wiley, Michael J.

2006-01-01

As a part of the Great Lakes Regional Aquatic Gap Analysis Project, we evaluated methodologies for modeling associations between fish species and habitat characteristics at a landscape scale. To do this, we created brook trout Salvelinus fontinalis presence and absence models based on four different techniques: multiple linear regression, logistic regression, neural networks, and classification trees. The models were tested in two ways: by application to an independent validation database and cross-validation using the training data, and by visual comparison of statewide distribution maps with historically recorded occurrences from the Michigan Fish Atlas. Although differences in the accuracy of our models were slight, the logistic regression model predicted with the least error, followed by multiple regression, then classification trees, then the neural networks. These models will provide natural resource managers a way to identify habitats requiring protection for the conservation of fish species.

Schistosomiasis Breeding Environment Situation Analysis in Dongting Lake Area

NASA Astrophysics Data System (ADS)

Li, Chuanrong; Jia, Yuanyuan; Ma, Lingling; Liu, Zhaoyan; Qian, Yonggang

2013-01-01

Monitoring environmental characteristics, such as vegetation, soil moisture et al., of Oncomelania hupensis (O. hupensis)’ spatial/temporal distribution is of vital importance to the schistosomiasis prevention and control. In this study, the relationship between environmental factors derived from remotely sensed data and the density of O. hupensis was analyzed by a multiple linear regression model. Secondly, spatial analysis of the regression residual was investigated by the semi-variogram method. Thirdly, spatial analysis of the regression residual and the multiple linear regression model were both employed to estimate the spatial variation of O. hupensis density. Finally, the approach was used to monitor and predict the spatial and temporal variations of oncomelania of Dongting Lake region, China. And the areas of potential O. hupensis habitats were predicted and the influence of Three Gorges Dam (TGB)project on the density of O. hupensis was analyzed.

Installation restoration program. Phase II - confirmation/quantification. Stage 1 for American Lake Garden Tract, Washington. Final report, March-July 1985

DOE Office of Scientific and Technical Information (OSTI.GOV)

Greilling, R.W.; Peshkin, R.L.

1985-12-20

An IRP Phase II (Stage 1) Confirmation/Quantification Investigation was performed in the American Lake Garden Tract residential community as a consequence of previously confirmed groundwater contamination by like compounds both in the Garden Tract and on McChord AFB. A similar type of groundwater problem was believed to exist at the opposite end of the residential area, and may possibly be related to Army operations on Fort Lewis. The field study was designed to identify the type, quantity, and extent of groundwater contamination by expanding the study area to include all of the Garden tract, the entire west half of McChordmore » AFB, and the northern one-third of the Fort Lewis Logistics Center. Field investigations consisted of 58,000 lineal feet of self-potential and 24,000 lineal feet of seismic refraction surveys. Forty electrical resistance stations were established. Twenty-six two-inch-diameter monitoring wells were constructed. More than 225 water samples from more than 60 EPA, Army, and Air Force monitoring wells, plus domestic water-supply wells were characterized for volatile organic chemicals. All wells were sounded at least weekly for static water levels, and in-situ hydrochemical properties were monitored. Study results confirm independent sources of chlorinated hydrocarbon contamination exist on each military facility, and that these contaminants are migrating into different parts of the American Lake Garden Tract.« less

Microdosimetry of low-energy electrons.

PubMed

Liamsuwan, Thiansin; Emfietzoglou, Dimitris; Uehara, Shuzo; Nikjoo, Hooshang

2012-12-01

To investigate differences in energy depositions and microdosimetric parameters of low-energy electrons in liquid and gaseous water using Monte Carlo track structure simulations. KURBUC-liq (Kyushu University and Radiobiology Unit Code for liquid water) was used for simulating electron tracks in liquid water. The inelastic scattering cross sections of liquid water were obtained from the dielectric response model of Emfietzoglou et al. (Radiation Research 2005;164:202-211). Frequencies of energy deposited in nanometre-size cylindrical targets per unit absorbed dose and associated lineal energies were calculated for 100-5000 eV monoenergetic electrons and the electron spectrum of carbon K edge X-rays. The results for liquid water were compared with those for water vapour. Regardless of electron energy, there is a limit how much energy electron tracks can deposit in a target. Phase effects on the frequencies of energy depositions are largely visible for the targets with diameters and heights smaller than 30 nm. For the target of 2.3 nm by 2.3 nm (similar to dimension of DNA segments), the calculated frequency- and dose-mean lineal energies for liquid water are up to 40% smaller than those for water vapour. The corresponding difference is less than 12% for the targets with diameters ≥ 30 nm. Condensed-phase effects are non-negligible for microdosimetry of low-energy electrons for targets with sizes smaller than a few tens of nanometres, similar to dimensions of DNA molecular structures and nucleosomes.

Modification of the USLE K factor for soil erodibility assessment on calcareous soils in Iran

NASA Astrophysics Data System (ADS)

Ostovari, Yaser; Ghorbani-Dashtaki, Shoja; Bahrami, Hossein-Ali; Naderi, Mehdi; Dematte, Jose Alexandre M.; Kerry, Ruth

2016-11-01

The measurement of soil erodibility (K) in the field is tedious, time-consuming and expensive; therefore, its prediction through pedotransfer functions (PTFs) could be far less costly and time-consuming. The aim of this study was to develop new PTFs to estimate the K factor using multiple linear regression, Mamdani fuzzy inference systems, and artificial neural networks. For this purpose, K was measured in 40 erosion plots with natural rainfall. Various soil properties including the soil particle size distribution, calcium carbonate equivalent, organic matter, permeability, and wet-aggregate stability were measured. The results showed that the mean measured K was 0.014 t h MJ- 1 mm- 1 and 2.08 times less than the estimated mean K (0.030 t h MJ- 1 mm- 1) using the USLE model. Permeability, wet-aggregate stability, very fine sand, and calcium carbonate were selected as independent variables by forward stepwise regression in order to assess the ability of multiple linear regression, Mamdani fuzzy inference systems and artificial neural networks to predict K. The calcium carbonate equivalent, which is not accounted for in the USLE model, had a significant impact on K in multiple linear regression due to its strong influence on the stability of aggregates and soil permeability. Statistical indices in validation and calibration datasets determined that the artificial neural networks method with the highest R2, lowest RMSE, and lowest ME was the best model for estimating the K factor. A strong correlation (R2 = 0.81, n = 40, p < 0.05) between the estimated K from multiple linear regression and measured K indicates that the use of calcium carbonate equivalent as a predictor variable gives a better estimation of K in areas with calcareous soils.

Analysis and prediction of flow from local source in a river basin using a Neuro-fuzzy modeling tool.

PubMed

Aqil, Muhammad; Kita, Ichiro; Yano, Akira; Nishiyama, Soichi

2007-10-01

Traditionally, the multiple linear regression technique has been one of the most widely used models in simulating hydrological time series. However, when the nonlinear phenomenon is significant, the multiple linear will fail to develop an appropriate predictive model. Recently, neuro-fuzzy systems have gained much popularity for calibrating the nonlinear relationships. This study evaluated the potential of a neuro-fuzzy system as an alternative to the traditional statistical regression technique for the purpose of predicting flow from a local source in a river basin. The effectiveness of the proposed identification technique was demonstrated through a simulation study of the river flow time series of the Citarum River in Indonesia. Furthermore, in order to provide the uncertainty associated with the estimation of river flow, a Monte Carlo simulation was performed. As a comparison, a multiple linear regression analysis that was being used by the Citarum River Authority was also examined using various statistical indices. The simulation results using 95% confidence intervals indicated that the neuro-fuzzy model consistently underestimated the magnitude of high flow while the low and medium flow magnitudes were estimated closer to the observed data. The comparison of the prediction accuracy of the neuro-fuzzy and linear regression methods indicated that the neuro-fuzzy approach was more accurate in predicting river flow dynamics. The neuro-fuzzy model was able to improve the root mean square error (RMSE) and mean absolute percentage error (MAPE) values of the multiple linear regression forecasts by about 13.52% and 10.73%, respectively. Considering its simplicity and efficiency, the neuro-fuzzy model is recommended as an alternative tool for modeling of flow dynamics in the study area.

Soil Cd, Cr, Cu, Ni, Pb and Zn sorption and retention models using SVM: Variable selection and competitive model.

PubMed

González Costa, J J; Reigosa, M J; Matías, J M; Covelo, E F

2017-09-01

The aim of this study was to model the sorption and retention of Cd, Cu, Ni, Pb and Zn in soils. To that extent, the sorption and retention of these metals were studied and the soil characterization was performed separately. Multiple stepwise regression was used to produce multivariate models with linear techniques and with support vector machines, all of which included 15 explanatory variables characterizing soils. When the R-squared values are represented, two different groups are noticed. Cr, Cu and Pb sorption and retention show a higher R-squared; the most explanatory variables being humified organic matter, Al oxides and, in some cases, cation-exchange capacity (CEC). The other group of metals (Cd, Ni and Zn) shows a lower R-squared, and clays are the most explanatory variables, including a percentage of vermiculite and slime. In some cases, quartz, plagioclase or hematite percentages also show some explanatory capacity. Support Vector Machine (SVM) regression shows that the different models are not as regular as in multiple regression in terms of number of variables, the regression for nickel adsorption being the one with the highest number of variables in its optimal model. On the other hand, there are cases where the most explanatory variables are the same for two metals, as it happens with Cd and Cr adsorption. A similar adsorption mechanism is thus postulated. These patterns of the introduction of variables in the model allow us to create explainability sequences. Those which are the most similar to the selectivity sequences obtained by Covelo (2005) are Mn oxides in multiple regression and change capacity in SVM. Among all the variables, the only one that is explanatory for all the metals after applying the maximum parsimony principle is the percentage of sand in the retention process. In the competitive model arising from the aforementioned sequences, the most intense competitiveness for the adsorption and retention of different metals appears between Cr and Cd, Cu and Zn in multiple regression; and between Cr and Cd in SVM regression. Copyright © 2017 Elsevier B.V. All rights reserved.

Practical guidance for conducting mediation analysis with multiple mediators using inverse odds ratio weighting.

PubMed

Nguyen, Quynh C; Osypuk, Theresa L; Schmidt, Nicole M; Glymour, M Maria; Tchetgen Tchetgen, Eric J

2015-03-01

Despite the recent flourishing of mediation analysis techniques, many modern approaches are difficult to implement or applicable to only a restricted range of regression models. This report provides practical guidance for implementing a new technique utilizing inverse odds ratio weighting (IORW) to estimate natural direct and indirect effects for mediation analyses. IORW takes advantage of the odds ratio's invariance property and condenses information on the odds ratio for the relationship between the exposure (treatment) and multiple mediators, conditional on covariates, by regressing exposure on mediators and covariates. The inverse of the covariate-adjusted exposure-mediator odds ratio association is used to weight the primary analytical regression of the outcome on treatment. The treatment coefficient in such a weighted regression estimates the natural direct effect of treatment on the outcome, and indirect effects are identified by subtracting direct effects from total effects. Weighting renders treatment and mediators independent, thereby deactivating indirect pathways of the mediators. This new mediation technique accommodates multiple discrete or continuous mediators. IORW is easily implemented and is appropriate for any standard regression model, including quantile regression and survival analysis. An empirical example is given using data from the Moving to Opportunity (1994-2002) experiment, testing whether neighborhood context mediated the effects of a housing voucher program on obesity. Relevant Stata code (StataCorp LP, College Station, Texas) is provided. © The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Neural network and multiple linear regression to predict school children dimensions for ergonomic school furniture design.

PubMed

Agha, Salah R; Alnahhal, Mohammed J

2012-11-01

The current study investigates the possibility of obtaining the anthropometric dimensions, critical to school furniture design, without measuring all of them. The study first selects some anthropometric dimensions that are easy to measure. Two methods are then used to check if these easy-to-measure dimensions can predict the dimensions critical to the furniture design. These methods are multiple linear regression and neural networks. Each dimension that is deemed necessary to ergonomically design school furniture is expressed as a function of some other measured anthropometric dimensions. Results show that out of the five dimensions needed for chair design, four can be related to other dimensions that can be measured while children are standing. Therefore, the method suggested here would definitely save time and effort and avoid the difficulty of dealing with students while measuring these dimensions. In general, it was found that neural networks perform better than multiple linear regression in the current study. Copyright © 2012 Elsevier Ltd and The Ergonomics Society. All rights reserved.

A Modified Double Multiple Nonlinear Regression Constitutive Equation for Modeling and Prediction of High Temperature Flow Behavior of BFe10-1-2 Alloy

NASA Astrophysics Data System (ADS)

Cai, Jun; Wang, Kuaishe; Shi, Jiamin; Wang, Wen; Liu, Yingying

2018-01-01

Constitutive analysis for hot working of BFe10-1-2 alloy was carried out by using experimental stress-strain data from isothermal hot compression tests, in a wide range of temperature of 1,023 1,273 K, and strain rate range of 0.001 10 s-1. A constitutive equation based on modified double multiple nonlinear regression was proposed considering the independent effects of strain, strain rate, temperature and their interrelation. The predicted flow stress data calculated from the developed equation was compared with the experimental data. Correlation coefficient (R), average absolute relative error (AARE) and relative errors were introduced to verify the validity of the developed constitutive equation. Subsequently, a comparative study was made on the capability of strain-compensated Arrhenius-type constitutive model. The results showed that the developed constitutive equation based on modified double multiple nonlinear regression could predict flow stress of BFe10-1-2 alloy with good correlation and generalization.

Comparing methods of analysing datasets with small clusters: case studies using four paediatric datasets.

PubMed

Marston, Louise; Peacock, Janet L; Yu, Keming; Brocklehurst, Peter; Calvert, Sandra A; Greenough, Anne; Marlow, Neil

2009-07-01

Studies of prematurely born infants contain a relatively large percentage of multiple births, so the resulting data have a hierarchical structure with small clusters of size 1, 2 or 3. Ignoring the clustering may lead to incorrect inferences. The aim of this study was to compare statistical methods which can be used to analyse such data: generalised estimating equations, multilevel models, multiple linear regression and logistic regression. Four datasets which differed in total size and in percentage of multiple births (n = 254, multiple 18%; n = 176, multiple 9%; n = 10 098, multiple 3%; n = 1585, multiple 8%) were analysed. With the continuous outcome, two-level models produced similar results in the larger dataset, while generalised least squares multilevel modelling (ML GLS 'xtreg' in Stata) and maximum likelihood multilevel modelling (ML MLE 'xtmixed' in Stata) produced divergent estimates using the smaller dataset. For the dichotomous outcome, most methods, except generalised least squares multilevel modelling (ML GH 'xtlogit' in Stata) gave similar odds ratios and 95% confidence intervals within datasets. For the continuous outcome, our results suggest using multilevel modelling. We conclude that generalised least squares multilevel modelling (ML GLS 'xtreg' in Stata) and maximum likelihood multilevel modelling (ML MLE 'xtmixed' in Stata) should be used with caution when the dataset is small. Where the outcome is dichotomous and there is a relatively large percentage of non-independent data, it is recommended that these are accounted for in analyses using logistic regression with adjusted standard errors or multilevel modelling. If, however, the dataset has a small percentage of clusters greater than size 1 (e.g. a population dataset of children where there are few multiples) there appears to be less need to adjust for clustering.

The extraction of simple relationships in growth factor-specific multiple-input and multiple-output systems in cell-fate decisions by backward elimination PLS regression.

PubMed

Akimoto, Yuki; Yugi, Katsuyuki; Uda, Shinsuke; Kudo, Takamasa; Komori, Yasunori; Kubota, Hiroyuki; Kuroda, Shinya

2013-01-01

Cells use common signaling molecules for the selective control of downstream gene expression and cell-fate decisions. The relationship between signaling molecules and downstream gene expression and cellular phenotypes is a multiple-input and multiple-output (MIMO) system and is difficult to understand due to its complexity. For example, it has been reported that, in PC12 cells, different types of growth factors activate MAP kinases (MAPKs) including ERK, JNK, and p38, and CREB, for selective protein expression of immediate early genes (IEGs) such as c-FOS, c-JUN, EGR1, JUNB, and FOSB, leading to cell differentiation, proliferation and cell death; however, how multiple-inputs such as MAPKs and CREB regulate multiple-outputs such as expression of the IEGs and cellular phenotypes remains unclear. To address this issue, we employed a statistical method called partial least squares (PLS) regression, which involves a reduction of the dimensionality of the inputs and outputs into latent variables and a linear regression between these latent variables. We measured 1,200 data points for MAPKs and CREB as the inputs and 1,900 data points for IEGs and cellular phenotypes as the outputs, and we constructed the PLS model from these data. The PLS model highlighted the complexity of the MIMO system and growth factor-specific input-output relationships of cell-fate decisions in PC12 cells. Furthermore, to reduce the complexity, we applied a backward elimination method to the PLS regression, in which 60 input variables were reduced to 5 variables, including the phosphorylation of ERK at 10 min, CREB at 5 min and 60 min, AKT at 5 min and JNK at 30 min. The simple PLS model with only 5 input variables demonstrated a predictive ability comparable to that of the full PLS model. The 5 input variables effectively extracted the growth factor-specific simple relationships within the MIMO system in cell-fate decisions in PC12 cells.

Detection of epistatic effects with logic regression and a classical linear regression model.

PubMed

Malina, Magdalena; Ickstadt, Katja; Schwender, Holger; Posch, Martin; Bogdan, Małgorzata

2014-02-01

To locate multiple interacting quantitative trait loci (QTL) influencing a trait of interest within experimental populations, usually methods as the Cockerham's model are applied. Within this framework, interactions are understood as the part of the joined effect of several genes which cannot be explained as the sum of their additive effects. However, if a change in the phenotype (as disease) is caused by Boolean combinations of genotypes of several QTLs, this Cockerham's approach is often not capable to identify them properly. To detect such interactions more efficiently, we propose a logic regression framework. Even though with the logic regression approach a larger number of models has to be considered (requiring more stringent multiple testing correction) the efficient representation of higher order logic interactions in logic regression models leads to a significant increase of power to detect such interactions as compared to a Cockerham's approach. The increase in power is demonstrated analytically for a simple two-way interaction model and illustrated in more complex settings with simulation study and real data analysis.

COLD TRAP

DOEpatents

Milleron, N.

1963-03-12

An improved linear-flow cold trap is designed for highvacuum applications such as mitigating back migration of diffusion pump oil moiecules. A central pot of liquid nitrogen is nested within and supported by a surrounding, vertical, helical coil of metai sheet, all enveloped by a larger, upright, cylindrical, vacuum vessel. The vertical interstices between successive turns of the coil afford lineal, axial, high-vacuum passages between open mouths at top and bottom of said vessel, while the coil, being cold by virtue of thermal contact of its innermost turn with the nitrogen pot, affords expansive proximate condensation surfaces. (AEC)

Regression in autistic spectrum disorders.

PubMed

Stefanatos, Gerry A

2008-12-01

A significant proportion of children diagnosed with Autistic Spectrum Disorder experience a developmental regression characterized by a loss of previously-acquired skills. This may involve a loss of speech or social responsitivity, but often entails both. This paper critically reviews the phenomena of regression in autistic spectrum disorders, highlighting the characteristics of regression, age of onset, temporal course, and long-term outcome. Important considerations for diagnosis are discussed and multiple etiological factors currently hypothesized to underlie the phenomenon are reviewed. It is argued that regressive autistic spectrum disorders can be conceptualized on a spectrum with other regressive disorders that may share common pathophysiological features. The implications of this viewpoint are discussed.

Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses.

PubMed

Faul, Franz; Erdfelder, Edgar; Buchner, Axel; Lang, Albert-Georg

2009-11-01

G*Power is a free power analysis program for a variety of statistical tests. We present extensions and improvements of the version introduced by Faul, Erdfelder, Lang, and Buchner (2007) in the domain of correlation and regression analyses. In the new version, we have added procedures to analyze the power of tests based on (1) single-sample tetrachoric correlations, (2) comparisons of dependent correlations, (3) bivariate linear regression, (4) multiple linear regression based on the random predictor model, (5) logistic regression, and (6) Poisson regression. We describe these new features and provide a brief introduction to their scope and handling.

Interpret with caution: multicollinearity in multiple regression of cognitive data.

PubMed

Morrison, Catriona M

2003-08-01

Shibihara and Kondo in 2002 reported a reanalysis of the 1997 Kanji picture-naming data of Yamazaki, Ellis, Morrison, and Lambon-Ralph in which independent variables were highly correlated. Their addition of the variable visual familiarity altered the previously reported pattern of results, indicating that visual familiarity, but not age of acquisition, was important in predicting Kanji naming speed. The present paper argues that caution should be taken when drawing conclusions from multiple regression analyses in which the independent variables are so highly correlated, as such multicollinearity can lead to unreliable output.

STATLIB: NSWC Library of Statistical Programs and Subroutines

DTIC Science & Technology

1989-08-01

Uncorrelated Weighted Polynomial Regression 41 .WEPORC Correlated Weighted Polynomial Regression 45 MROP Multiple Regression Using Orthogonal Polynomials ...could not and should not be con- NSWC TR 89-97 verted to the new general purpose computer (the current CDC 995). Some were designed tu compute...personal computers. They are referred to as SPSSPC+, BMDPC, and SASPC and in general are less comprehensive than their mainframe counterparts. The basic

Parametric optimization of multiple quality characteristics in laser cutting of Inconel-718 by using hybrid approach of multiple regression analysis and genetic algorithm

NASA Astrophysics Data System (ADS)

Shrivastava, Prashant Kumar; Pandey, Arun Kumar

2018-06-01

Inconel-718 has found high demand in different industries due to their superior mechanical properties. The traditional cutting methods are facing difficulties for cutting these alloys due to their low thermal potential, lower elasticity and high chemical compatibility at inflated temperature. The challenges of machining and/or finishing of unusual shapes and/or sizes in these materials have also faced by traditional machining. Laser beam cutting may be applied for the miniaturization and ultra-precision cutting and/or finishing by appropriate control of different process parameter. This paper present multi-objective optimization the kerf deviation, kerf width and kerf taper in the laser cutting of Incone-718 sheet. The second order regression models have been developed for different quality characteristics by using the experimental data obtained through experimentation. The regression models have been used as objective function for multi-objective optimization based on the hybrid approach of multiple regression analysis and genetic algorithm. The comparison of optimization results to experimental results shows an improvement of 88%, 10.63% and 42.15% in kerf deviation, kerf width and kerf taper, respectively. Finally, the effects of different process parameters on quality characteristics have also been discussed.

Relative efficiency of joint-model and full-conditional-specification multiple imputation when conditional models are compatible: The general location model.

PubMed

Seaman, Shaun R; Hughes, Rachael A

2018-06-01

Estimating the parameters of a regression model of interest is complicated by missing data on the variables in that model. Multiple imputation is commonly used to handle these missing data. Joint model multiple imputation and full-conditional specification multiple imputation are known to yield imputed data with the same asymptotic distribution when the conditional models of full-conditional specification are compatible with that joint model. We show that this asymptotic equivalence of imputation distributions does not imply that joint model multiple imputation and full-conditional specification multiple imputation will also yield asymptotically equally efficient inference about the parameters of the model of interest, nor that they will be equally robust to misspecification of the joint model. When the conditional models used by full-conditional specification multiple imputation are linear, logistic and multinomial regressions, these are compatible with a restricted general location joint model. We show that multiple imputation using the restricted general location joint model can be substantially more asymptotically efficient than full-conditional specification multiple imputation, but this typically requires very strong associations between variables. When associations are weaker, the efficiency gain is small. Moreover, full-conditional specification multiple imputation is shown to be potentially much more robust than joint model multiple imputation using the restricted general location model to mispecification of that model when there is substantial missingness in the outcome variable.

Application of third molar development and eruption models in estimating dental age in Malay sub-adults.

PubMed

Mohd Yusof, Mohd Yusmiaidil Putera; Cauwels, Rita; Deschepper, Ellen; Martens, Luc

2015-08-01

The third molar development (TMD) has been widely utilized as one of the radiographic method for dental age estimation. By using the same radiograph of the same individual, third molar eruption (TME) information can be incorporated to the TMD regression model. This study aims to evaluate the performance of dental age estimation in individual method models and the combined model (TMD and TME) based on the classic regressions of multiple linear and principal component analysis. A sample of 705 digital panoramic radiographs of Malay sub-adults aged between 14.1 and 23.8 years was collected. The techniques described by Gleiser and Hunt (modified by Kohler) and Olze were employed to stage the TMD and TME, respectively. The data was divided to develop three respective models based on the two regressions of multiple linear and principal component analysis. The trained models were then validated on the test sample and the accuracy of age prediction was compared between each model. The coefficient of determination (R²) and root mean square error (RMSE) were calculated. In both genders, adjusted R² yielded an increment in the linear regressions of combined model as compared to the individual models. The overall decrease in RMSE was detected in combined model as compared to TMD (0.03-0.06) and TME (0.2-0.8). In principal component regression, low value of adjusted R(2) and high RMSE except in male were exhibited in combined model. Dental age estimation is better predicted using combined model in multiple linear regression models. Copyright © 2015 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

On the method of Ermakov and Zolotukhin for multiple integration

NASA Technical Reports Server (NTRS)

Cranley, R.; Patterson, T. N. L.

1971-01-01

By introducing the idea of pseudo-implementation, a practical assessment of the method for multiple integration is made. The performance of the method is found to be unimpressive in comparison with a recent regression method.

Criteria for the use of regression analysis for remote sensing of sediment and pollutants

NASA Technical Reports Server (NTRS)

Whitlock, C. H.; Kuo, C. Y.; Lecroy, S. R.

1982-01-01

An examination of limitations, requirements, and precision of the linear multiple-regression technique for quantification of marine environmental parameters is conducted. Both environmental and optical physics conditions have been defined for which an exact solution to the signal response equations is of the same form as the multiple regression equation. Various statistical parameters are examined to define a criteria for selection of an unbiased fit when upwelled radiance values contain error and are correlated with each other. Field experimental data are examined to define data smoothing requirements in order to satisfy the criteria of Daniel and Wood (1971). Recommendations are made concerning improved selection of ground-truth locations to maximize variance and to minimize physical errors associated with the remote sensing experiment.

Introduction to the use of regression models in epidemiology.

PubMed

Bender, Ralf

2009-01-01

Regression modeling is one of the most important statistical techniques used in analytical epidemiology. By means of regression models the effect of one or several explanatory variables (e.g., exposures, subject characteristics, risk factors) on a response variable such as mortality or cancer can be investigated. From multiple regression models, adjusted effect estimates can be obtained that take the effect of potential confounders into account. Regression methods can be applied in all epidemiologic study designs so that they represent a universal tool for data analysis in epidemiology. Different kinds of regression models have been developed in dependence on the measurement scale of the response variable and the study design. The most important methods are linear regression for continuous outcomes, logistic regression for binary outcomes, Cox regression for time-to-event data, and Poisson regression for frequencies and rates. This chapter provides a nontechnical introduction to these regression models with illustrating examples from cancer research.

Using regression equations built from summary data in the psychological assessment of the individual case: extension to multiple regression.

PubMed

Crawford, John R; Garthwaite, Paul H; Denham, Annie K; Chelune, Gordon J

2012-12-01

Regression equations have many useful roles in psychological assessment. Moreover, there is a large reservoir of published data that could be used to build regression equations; these equations could then be employed to test a wide variety of hypotheses concerning the functioning of individual cases. This resource is currently underused because (a) not all psychologists are aware that regression equations can be built not only from raw data but also using only basic summary data for a sample, and (b) the computations involved are tedious and prone to error. In an attempt to overcome these barriers, Crawford and Garthwaite (2007) provided methods to build and apply simple linear regression models using summary statistics as data. In the present study, we extend this work to set out the steps required to build multiple regression models from sample summary statistics and the further steps required to compute the associated statistics for drawing inferences concerning an individual case. We also develop, describe, and make available a computer program that implements these methods. Although there are caveats associated with the use of the methods, these need to be balanced against pragmatic considerations and against the alternative of either entirely ignoring a pertinent data set or using it informally to provide a clinical "guesstimate." Upgraded versions of earlier programs for regression in the single case are also provided; these add the point and interval estimates of effect size developed in the present article.

Temporal Synchronization Analysis for Improving Regression Modeling of Fecal Indicator Bacteria Levels

EPA Science Inventory

Multiple linear regression models are often used to predict levels of fecal indicator bacteria (FIB) in recreational swimming waters based on independent variables (IVs) such as meteorologic, hydrodynamic, and water-quality measures. The IVs used for these analyses are traditiona...

Epidemiologic programs for computers and calculators. A microcomputer program for multiple logistic regression by unconditional and conditional maximum likelihood methods.

PubMed

Campos-Filho, N; Franco, E L

1989-02-01

A frequent procedure in matched case-control studies is to report results from the multivariate unmatched analyses if they do not differ substantially from the ones obtained after conditioning on the matching variables. Although conceptually simple, this rule requires that an extensive series of logistic regression models be evaluated by both the conditional and unconditional maximum likelihood methods. Most computer programs for logistic regression employ only one maximum likelihood method, which requires that the analyses be performed in separate steps. This paper describes a Pascal microcomputer (IBM PC) program that performs multiple logistic regression by both maximum likelihood estimation methods, which obviates the need for switching between programs to obtain relative risk estimates from both matched and unmatched analyses. The program calculates most standard statistics and allows factoring of categorical or continuous variables by two distinct methods of contrast. A built-in, descriptive statistics option allows the user to inspect the distribution of cases and controls across categories of any given variable.

Multiple linear regression and regression with time series error models in forecasting PM10 concentrations in Peninsular Malaysia.

PubMed

Ng, Kar Yong; Awang, Norhashidah

2018-01-06

Frequent haze occurrences in Malaysia have made the management of PM 10 (particulate matter with aerodynamic less than 10 μm) pollution a critical task. This requires knowledge on factors associating with PM 10 variation and good forecast of PM 10 concentrations. Hence, this paper demonstrates the prediction of 1-day-ahead daily average PM 10 concentrations based on predictor variables including meteorological parameters and gaseous pollutants. Three different models were built. They were multiple linear regression (MLR) model with lagged predictor variables (MLR1), MLR model with lagged predictor variables and PM 10 concentrations (MLR2) and regression with time series error (RTSE) model. The findings revealed that humidity, temperature, wind speed, wind direction, carbon monoxide and ozone were the main factors explaining the PM 10 variation in Peninsular Malaysia. Comparison among the three models showed that MLR2 model was on a same level with RTSE model in terms of forecasting accuracy, while MLR1 model was the worst.

Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation

PubMed Central

Carvalho, Carlos; Gomes, Danielo G.; Agoulmine, Nazim; de Souza, José Neuman

2011-01-01

This paper proposes a method based on multivariate spatial and temporal correlation to improve prediction accuracy in data reduction for Wireless Sensor Networks (WSN). Prediction of data not sent to the sink node is a technique used to save energy in WSNs by reducing the amount of data traffic. However, it may not be very accurate. Simulations were made involving simple linear regression and multiple linear regression functions to assess the performance of the proposed method. The results show a higher correlation between gathered inputs when compared to time, which is an independent variable widely used for prediction and forecasting. Prediction accuracy is lower when simple linear regression is used, whereas multiple linear regression is the most accurate one. In addition to that, our proposal outperforms some current solutions by about 50% in humidity prediction and 21% in light prediction. To the best of our knowledge, we believe that we are probably the first to address prediction based on multivariate correlation for WSN data reduction. PMID:22346626

Multiple regression analysis in nomogram development for myopic wavefront laser in situ keratomileusis: Improving astigmatic outcomes.

PubMed

Allan, Bruce D; Hassan, Hala; Ieong, Alvin

2015-05-01

To describe and evaluate a new multiple regression-derived nomogram for myopic wavefront laser in situ keratomileusis (LASIK). Moorfields Eye Hospital, London, United Kingdom. Prospective comparative case series. Multiple regression modeling was used to derive a simplified formula for adjusting attempted spherical correction in myopic LASIK. An adaptation of Thibos' power vector method was then applied to derive adjustments to attempted cylindrical correction in eyes with 1.0 diopter (D) or more of preoperative cylinder. These elements were combined in a new nomogram (nomogram II). The 3-month refractive results for myopic wavefront LASIK (spherical equivalent ≤11.0 D; cylinder ≤4.5 D) were compared between 299 consecutive eyes treated using the earlier nomogram (nomogram I) in 2009 and 2010 and 414 eyes treated using nomogram II in 2011 and 2012. There was no significant difference in treatment accuracy (variance in the postoperative manifest refraction spherical equivalent error) between nomogram I and nomogram II (P = .73, Bartlett test). Fewer patients treated with nomogram II had more than 0.5 D of residual postoperative astigmatism (P = .0001, Fisher exact test). There was no significant coupling between adjustments to the attempted cylinder and the achieved sphere (P = .18, t test). Discarding marginal influences from a multiple regression-derived nomogram for myopic wavefront LASIK had no clinically significant effect on treatment accuracy. Thibos' power vector method can be used to guide adjustments to the treatment cylinder alongside nomograms designed to optimize postoperative spherical equivalent results in myopic LASIK. mentioned. Copyright © 2015 ASCRS and ESCRS. Published by Elsevier Inc. All rights reserved.

The relationship between quality of work life and turnover intention of primary health care nurses in Saudi Arabia.

PubMed

Almalki, Mohammed J; FitzGerald, Gerry; Clark, Michele

2012-09-12

Quality of work life (QWL) has been found to influence the commitment of health professionals, including nurses. However, reliable information on QWL and turnover intention of primary health care (PHC) nurses is limited. The aim of this study was to examine the relationship between QWL and turnover intention of PHC nurses in Saudi Arabia. A cross-sectional survey was used in this study. Data were collected using Brooks' survey of Quality of Nursing Work Life, the Anticipated Turnover Scale and demographic data questions. A total of 508 PHC nurses in the Jazan Region, Saudi Arabia, completed the questionnaire (RR = 87%). Descriptive statistics, t-test, ANOVA, General Linear Model (GLM) univariate analysis, standard multiple regression, and hierarchical multiple regression were applied for analysis using SPSS v17 for Windows. Findings suggested that the respondents were dissatisfied with their work life, with almost 40% indicating a turnover intention from their current PHC centres. Turnover intention was significantly related to QWL. Using standard multiple regression, 26% of the variance in turnover intention was explained by QWL, p < 0.001, with R2 = .263. Further analysis using hierarchical multiple regression found that the total variance explained by the model as a whole (demographics and QWL) was 32.1%, p < 0.001. QWL explained an additional 19% of the variance in turnover intention, after controlling for demographic variables. Creating and maintaining a healthy work life for PHC nurses is very important to improve their work satisfaction, reduce turnover, enhance productivity and improve nursing care outcomes.

The relationship between quality of work life and turnover intention of primary health care nurses in Saudi Arabia

PubMed Central

2012-01-01

Background Quality of work life (QWL) has been found to influence the commitment of health professionals, including nurses. However, reliable information on QWL and turnover intention of primary health care (PHC) nurses is limited. The aim of this study was to examine the relationship between QWL and turnover intention of PHC nurses in Saudi Arabia. Methods A cross-sectional survey was used in this study. Data were collected using Brooks’ survey of Quality of Nursing Work Life, the Anticipated Turnover Scale and demographic data questions. A total of 508 PHC nurses in the Jazan Region, Saudi Arabia, completed the questionnaire (RR = 87%). Descriptive statistics, t-test, ANOVA, General Linear Model (GLM) univariate analysis, standard multiple regression, and hierarchical multiple regression were applied for analysis using SPSS v17 for Windows. Results Findings suggested that the respondents were dissatisfied with their work life, with almost 40% indicating a turnover intention from their current PHC centres. Turnover intention was significantly related to QWL. Using standard multiple regression, 26% of the variance in turnover intention was explained by QWL, p < 0.001, with R2 = .263. Further analysis using hierarchical multiple regression found that the total variance explained by the model as a whole (demographics and QWL) was 32.1%, p < 0.001. QWL explained an additional 19% of the variance in turnover intention, after controlling for demographic variables. Conclusions Creating and maintaining a healthy work life for PHC nurses is very important to improve their work satisfaction, reduce turnover, enhance productivity and improve nursing care outcomes. PMID:22970764

Risk factors for autistic regression: results of an ambispective cohort study.

PubMed

Zhang, Ying; Xu, Qiong; Liu, Jing; Li, She-chang; Xu, Xiu

2012-08-01

A subgroup of children diagnosed with autism experience developmental regression featured by a loss of previously acquired abilities. The pathogeny of autistic regression is unknown, although many risk factors likely exist. To better characterize autistic regression and investigate the association between autistic regression and potential influencing factors in Chinese autistic children, we conducted an ambispective study with a cohort of 170 autistic subjects. Analyses by multiple logistic regression showed significant correlations between autistic regression and febrile seizures (OR = 3.53, 95% CI = 1.17-10.65, P = .025), as well as with a family history of neuropsychiatric disorders (OR = 3.62, 95% CI = 1.35-9.71, P = .011). This study suggests that febrile seizures and family history of neuropsychiatric disorders are correlated with autistic regression.

Early Parallel Activation of Semantics and Phonology in Picture Naming: Evidence from a Multiple Linear Regression MEG Study

PubMed Central

Miozzo, Michele; Pulvermüller, Friedemann; Hauk, Olaf

2015-01-01

The time course of brain activation during word production has become an area of increasingly intense investigation in cognitive neuroscience. The predominant view has been that semantic and phonological processes are activated sequentially, at about 150 and 200–400 ms after picture onset. Although evidence from prior studies has been interpreted as supporting this view, these studies were arguably not ideally suited to detect early brain activation of semantic and phonological processes. We here used a multiple linear regression approach to magnetoencephalography (MEG) analysis of picture naming in order to investigate early effects of variables specifically related to visual, semantic, and phonological processing. This was combined with distributed minimum-norm source estimation and region-of-interest analysis. Brain activation associated with visual image complexity appeared in occipital cortex at about 100 ms after picture presentation onset. At about 150 ms, semantic variables became physiologically manifest in left frontotemporal regions. In the same latency range, we found an effect of phonological variables in the left middle temporal gyrus. Our results demonstrate that multiple linear regression analysis is sensitive to early effects of multiple psycholinguistic variables in picture naming. Crucially, our results suggest that access to phonological information might begin in parallel with semantic processing around 150 ms after picture onset. PMID:25005037

An Investigation of the Fit of Linear Regression Models to Data from an SAT[R] Validity Study. Research Report 2011-3

ERIC Educational Resources Information Center

Kobrin, Jennifer L.; Sinharay, Sandip; Haberman, Shelby J.; Chajewski, Michael

2011-01-01

This study examined the adequacy of a multiple linear regression model for predicting first-year college grade point average (FYGPA) using SAT[R] scores and high school grade point average (HSGPA). A variety of techniques, both graphical and statistical, were used to examine if it is possible to improve on the linear regression model. The results…

Determining Sample Size for Accurate Estimation of the Squared Multiple Correlation Coefficient.

ERIC Educational Resources Information Center

Algina, James; Olejnik, Stephen

2000-01-01

Discusses determining sample size for estimation of the squared multiple correlation coefficient and presents regression equations that permit determination of the sample size for estimating this parameter for up to 20 predictor variables. (SLD)

Comparison of two-concentration with multi-concentration linear regressions: Retrospective data analysis of multiple regulated LC-MS bioanalytical projects.

PubMed

Musuku, Adrien; Tan, Aimin; Awaiye, Kayode; Trabelsi, Fethi

2013-09-01

Linear calibration is usually performed using eight to ten calibration concentration levels in regulated LC-MS bioanalysis because a minimum of six are specified in regulatory guidelines. However, we have previously reported that two-concentration linear calibration is as reliable as or even better than using multiple concentrations. The purpose of this research is to compare two-concentration with multiple-concentration linear calibration through retrospective data analysis of multiple bioanalytical projects that were conducted in an independent regulated bioanalytical laboratory. A total of 12 bioanalytical projects were randomly selected: two validations and two studies for each of the three most commonly used types of sample extraction methods (protein precipitation, liquid-liquid extraction, solid-phase extraction). When the existing data were retrospectively linearly regressed using only the lowest and the highest concentration levels, no extra batch failure/QC rejection was observed and the differences in accuracy and precision between the original multi-concentration regression and the new two-concentration linear regression are negligible. Specifically, the differences in overall mean apparent bias (square root of mean individual bias squares) are within the ranges of -0.3% to 0.7% and 0.1-0.7% for the validations and studies, respectively. The differences in mean QC concentrations are within the ranges of -0.6% to 1.8% and -0.8% to 2.5% for the validations and studies, respectively. The differences in %CV are within the ranges of -0.7% to 0.9% and -0.3% to 0.6% for the validations and studies, respectively. The average differences in study sample concentrations are within the range of -0.8% to 2.3%. With two-concentration linear regression, an average of 13% of time and cost could have been saved for each batch together with 53% of saving in the lead-in for each project (the preparation of working standard solutions, spiking, and aliquoting). Furthermore, examples are given as how to evaluate the linearity over the entire concentration range when only two concentration levels are used for linear regression. To conclude, two-concentration linear regression is accurate and robust enough for routine use in regulated LC-MS bioanalysis and it significantly saves time and cost as well. Copyright © 2013 Elsevier B.V. All rights reserved.

Flood characteristics of Alaskan streams

USGS Publications Warehouse

Lamke, R.D.

1979-01-01

Peak discharge data for Alaskan streams are summarized and analyzed. Multiple-regression equations relating peak discharge magnitude and frequency to climatic and physical characteristics of 260 gaged basins were determined in order to estimate average recurrence interval of floods at ungaged sites. These equations are for 1.25-, 2-, 5-, 10-, 25-, and 50-year average recurrence intervals. In this report, Alaska was divided into two regions, one having a maritime climate with fall and winter rains and floods, the other having spring and summer floods of a variety or combinations of causes. Average standard errors of the six multiple-regression equations for these two regions were 48 and 74 percent, respectively. Maximum recorded floods at more than 400 sites throughout Alaska are tabulated. Maps showing lines of equal intensity of the principal climatic variables found to be significant (mean annual precipitation and mean minimum January temperature), and location of the 260 sites used in the multiple-regression analyses are included. Little flood data have been collected in western and arctic Alaska, and the predictive equations are therefore less reliable for those areas. (Woodard-USGS)

Estimation of nutrients and organic matter in Korean swine slurry using multiple regression analysis of physical and chemical properties.

PubMed

Suresh, Arumuganainar; Choi, Hong Lim

2011-10-01

Swine waste land application has increased due to organic fertilization, but excess application in an arable system can cause environmental risk. Therefore, in situ characterizations of such resources are important prior to application. To explore this, 41 swine slurry samples were collected from Korea, and wide differences were observed in the physico-biochemical properties. However, significant (P<0.001) multiple property correlations (R²) were obtained between nutrients with specific gravity (SG), electrical conductivity (EC), total solids (TS) and pH. The different combinations of hydrometer, EC meter, drying oven and pH meter were found useful to estimate Mn, Fe, Ca, K, Al, Na, N and 5-day biochemical oxygen demands (BOD₅) at improved R² values of 0.83, 0.82, 0.77, 0.75, 0.67, 0.47, 0.88 and 0.70, respectively. The results from this study suggest that multiple property regressions can facilitate the prediction of micronutrients and organic matter much better than a single property regression for livestock waste. Copyright © 2011 Elsevier Ltd. All rights reserved.

Theory of mind and executive function: working-memory capacity and inhibitory control as predictors of false-belief task performance.

PubMed

Mutter, Brigitte; Alcorn, Mark B; Welsh, Marilyn

2006-06-01

This study of the relationship between theory of mind and executive function examined whether on the false-belief task age differences between 3 and 5 ears of age are related to development of working-memory capacity and inhibitory processes. 72 children completed tasks measuring false belief, working memory, and inhibition. Significant age effects were observed for false-belief and working-memory performance, as well as for the false-alarm and perseveration measures of inhibition. A simultaneous multiple linear regression specified the contribution of age, inhibition, and working memory to the prediction of false-belief performance. This model was significant, explaining a total of 36% of the variance. To examine the independent contributions of the working-memory and inhibition variables, after controlling for age, two hierarchical multiple linear regressions were conducted. These multiple regression analyses indicate that working memory and inhibition make small, overlapping contributions to false-belief performance after accounting for age, but that working memory, as measured in this study, is a somewhat better predictor of false-belief understanding than is inhibition.

Mapping diffuse photosynthetically active radiation from satellite data in Thailand

NASA Astrophysics Data System (ADS)

Choosri, P.; Janjai, S.; Nunez, M.; Buntoung, S.; Charuchittipan, D.

2017-12-01

In this paper, calculation of monthly average hourly diffuse photosynthetically active radiation (PAR) using satellite data is proposed. Diffuse PAR was analyzed at four stations in Thailand. A radiative transfer model was used for calculating the diffuse PAR for cloudless sky conditions. Differences between the diffuse PAR under all sky conditions obtained from the ground-based measurements and those from the model are representative of cloud effects. Two models are developed, one describing diffuse PAR only as a function of solar zenith angle, and the second one as a multiple linear regression with solar zenith angle and satellite reflectivity acting linearly and aerosol optical depth acting in logarithmic functions. When tested with an independent data set, the multiple regression model performed best with a higher coefficient of variance R2 (0.78 vs. 0.70), lower root mean square difference (RMSD) (12.92% vs. 13.05%) and the same mean bias difference (MBD) of -2.20%. Results from the multiple regression model are used to map diffuse PAR throughout the country as monthly averages of hourly data.

Clifford support vector machines for classification, regression, and recurrence.

PubMed

Bayro-Corrochano, Eduardo Jose; Arana-Daniel, Nancy

2010-11-01

This paper introduces the Clifford support vector machines (CSVM) as a generalization of the real and complex-valued support vector machines using the Clifford geometric algebra. In this framework, we handle the design of kernels involving the Clifford or geometric product. In this approach, one redefines the optimization variables as multivectors. This allows us to have a multivector as output. Therefore, we can represent multiple classes according to the dimension of the geometric algebra in which we work. We show that one can apply CSVM for classification and regression and also to build a recurrent CSVM. The CSVM is an attractive approach for the multiple input multiple output processing of high-dimensional geometric entities. We carried out comparisons between CSVM and the current approaches to solve multiclass classification and regression. We also study the performance of the recurrent CSVM with experiments involving time series. The authors believe that this paper can be of great use for researchers and practitioners interested in multiclass hypercomplex computing, particularly for applications in complex and quaternion signal and image processing, satellite control, neurocomputation, pattern recognition, computer vision, augmented virtual reality, robotics, and humanoids.

A general equation to obtain multiple cut-off scores on a test from multinomial logistic regression.

PubMed

Bersabé, Rosa; Rivas, Teresa

2010-05-01

The authors derive a general equation to compute multiple cut-offs on a total test score in order to classify individuals into more than two ordinal categories. The equation is derived from the multinomial logistic regression (MLR) model, which is an extension of the binary logistic regression (BLR) model to accommodate polytomous outcome variables. From this analytical procedure, cut-off scores are established at the test score (the predictor variable) at which an individual is as likely to be in category j as in category j+1 of an ordinal outcome variable. The application of the complete procedure is illustrated by an example with data from an actual study on eating disorders. In this example, two cut-off scores on the Eating Attitudes Test (EAT-26) scores are obtained in order to classify individuals into three ordinal categories: asymptomatic, symptomatic and eating disorder. Diagnoses were made from the responses to a self-report (Q-EDD) that operationalises DSM-IV criteria for eating disorders. Alternatives to the MLR model to set multiple cut-off scores are discussed.

Application of Partial Least Square (PLS) Regression to Determine Landscape-Scale Aquatic Resources Vulnerability in the Ozark Mountains

EPA Science Inventory

Partial least squares (PLS) analysis offers a number of advantages over the more traditionally used regression analyses applied in landscape ecology, particularly for determining the associations among multiple constituents of surface water and landscape configuration. Common dat...

Cluster Analysis of Campylobacter jejuni Genotypes Isolated from Small and Medium-Sized Mammalian Wildlife and Bovine Livestock from Ontario Farms.

PubMed

Viswanathan, M; Pearl, D L; Taboada, E N; Parmley, E J; Mutschall, S K; Jardine, C M

2017-05-01

Using data collected from a cross-sectional study of 25 farms (eight beef, eight swine and nine dairy) in 2010, we assessed clustering of molecular subtypes of C. jejuni based on a Campylobacter-specific 40 gene comparative genomic fingerprinting assay (CGF40) subtypes, using unweighted pair-group method with arithmetic mean (UPGMA) analysis, and multiple correspondence analysis. Exact logistic regression was used to determine which genes differentiate wildlife and livestock subtypes in our study population. A total of 33 bovine livestock (17 beef and 16 dairy), 26 wildlife (20 raccoon (Procyon lotor), five skunk (Mephitis mephitis) and one mouse (Peromyscus spp.) C. jejuni isolates were subtyped using CGF40. Dendrogram analysis, based on UPGMA, showed distinct branches separating bovine livestock and mammalian wildlife isolates. Furthermore, two-dimensional multiple correspondence analysis was highly concordant with dendrogram analysis showing clear differentiation between livestock and wildlife CGF40 subtypes. Based on multilevel logistic regression models with a random intercept for farm of origin, we found that isolates in general, and raccoons more specifically, were significantly more likely to be part of the wildlife branch. Exact logistic regression conducted gene by gene revealed 15 genes that were predictive of whether an isolate was of wildlife or bovine livestock isolate origin. Both multiple correspondence analysis and exact logistic regression revealed that in most cases, the presence of a particular gene (13 of 15) was associated with an isolate being of livestock rather than wildlife origin. In conclusion, the evidence gained from dendrogram analysis, multiple correspondence analysis and exact logistic regression indicates that mammalian wildlife carry CGF40 subtypes of C. jejuni distinct from those carried by bovine livestock. Future studies focused on source attribution of C. jejuni in human infections will help determine whether wildlife transmit Campylobacter jejuni directly to humans. © 2016 Blackwell Verlag GmbH.

Estimation of lung tumor position from multiple anatomical features on 4D-CT using multiple regression analysis.

PubMed

Ono, Tomohiro; Nakamura, Mitsuhiro; Hirose, Yoshinori; Kitsuda, Kenji; Ono, Yuka; Ishigaki, Takashi; Hiraoka, Masahiro

2017-09-01

To estimate the lung tumor position from multiple anatomical features on four-dimensional computed tomography (4D-CT) data sets using single regression analysis (SRA) and multiple regression analysis (MRA) approach and evaluate an impact of the approach on internal target volume (ITV) for stereotactic body radiotherapy (SBRT) of the lung. Eleven consecutive lung cancer patients (12 cases) underwent 4D-CT scanning. The three-dimensional (3D) lung tumor motion exceeded 5 mm. The 3D tumor position and anatomical features, including lung volume, diaphragm, abdominal wall, and chest wall positions, were measured on 4D-CT images. The tumor position was estimated by SRA using each anatomical feature and MRA using all anatomical features. The difference between the actual and estimated tumor positions was defined as the root-mean-square error (RMSE). A standard partial regression coefficient for the MRA was evaluated. The 3D lung tumor position showed a high correlation with the lung volume (R = 0.92 ± 0.10). Additionally, ITVs derived from SRA and MRA approaches were compared with ITV derived from contouring gross tumor volumes on all 10 phases of the 4D-CT (conventional ITV). The RMSE of the SRA was within 3.7 mm in all directions. Also, the RMSE of the MRA was within 1.6 mm in all directions. The standard partial regression coefficient for the lung volume was the largest and had the most influence on the estimated tumor position. Compared with conventional ITV, average percentage decrease of ITV were 31.9% and 38.3% using SRA and MRA approaches, respectively. The estimation accuracy of lung tumor position was improved by the MRA approach, which provided smaller ITV than conventional ITV. © 2017 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

Using the Ridge Regression Procedures to Estimate the Multiple Linear Regression Coefficients

NASA Astrophysics Data System (ADS)

Gorgees, HazimMansoor; Mahdi, FatimahAssim

2018-05-01

This article concerns with comparing the performance of different types of ordinary ridge regression estimators that have been already proposed to estimate the regression parameters when the near exact linear relationships among the explanatory variables is presented. For this situations we employ the data obtained from tagi gas filling company during the period (2008-2010). The main result we reached is that the method based on the condition number performs better than other methods since it has smaller mean square error (MSE) than the other stated methods.

Comparison of Different Shrinkage Formulas in Estimating Population Multiple Correlation Coefficients.

ERIC Educational Resources Information Center

Carter, David S.

1979-01-01

There are a variety of formulas for reducing the positive bias which occurs in estimating R squared in multiple regression or correlation equations. Five different formulas are evaluated in a Monte Carlo study, and recommendations are made. (JKS)

Estimating Optimal Transformations for Multiple Regression and Correlation.

DTIC Science & Technology

1982-07-01

S w.EECTli1Z"", , J OCT 0 11982 u! !for Public its... .. . ESTIMATING OPTIMAL TRANSFORMATIONS FOR MULTIPLE REGRESSION AND CORRELATION by Leo...in the plot lb of *(yk) versus 1 < k < 200. Figure lc is a plot of $*(xk) versus xk. These plots clearly suggest the transformati " s 6(y) = log(y) and...direct .814 .022 ACE .808 .031 -13- Figure la6L ’ ’ I . . . S " ’ ’ . . I ’ 6- - - .4...... Co o • . o ’ 0 0.2 0.4 0.5 0.8 1 Fi gure lb2 2 2 // II / / -/

Bark analysis as a guide to cassava nutrition in Sierra Leone

DOE Office of Scientific and Technical Information (OSTI.GOV)

Godfrey-Sam-Aggrey, W.; Garber, M.J.

1979-01-01

Cassava main stem barks from two experiments in which similar fertilizers were applied directly in a 2/sup 5/ confounded factorial design were analyzed and the bark nutrients used as a guide to cassava nutrition. The application of multiple regression analysis to the respective root yields and bark nutrient concentrations enable nutrient levels and optimum adjusted root yields to be derived. Differences in bark nutrient concentrations reflected soil fertility levels. Bark analysis and the application of multiple regression analysis to root yields and bark nutrients appear to be useful tools for predicting fertilizer recommendations for cassava production.

Estimation of perceptible water vapor of atmosphere using artificial neural network, support vector machine and multiple linear regression algorithm and their comparative study

NASA Astrophysics Data System (ADS)

Shastri, Niket; Pathak, Kamlesh

2018-05-01

The water vapor content in atmosphere plays very important role in climate. In this paper the application of GPS signal in meteorology is discussed, which is useful technique that is used to estimate the perceptible water vapor of atmosphere. In this paper various algorithms like artificial neural network, support vector machine and multiple linear regression are use to predict perceptible water vapor. The comparative studies in terms of root mean square error and mean absolute errors are also carried out for all the algorithms.

Order Selection for General Expression of Nonlinear Autoregressive Model Based on Multivariate Stepwise Regression

NASA Astrophysics Data System (ADS)

Shi, Jinfei; Zhu, Songqing; Chen, Ruwen

2017-12-01

An order selection method based on multiple stepwise regressions is proposed for General Expression of Nonlinear Autoregressive model which converts the model order problem into the variable selection of multiple linear regression equation. The partial autocorrelation function is adopted to define the linear term in GNAR model. The result is set as the initial model, and then the nonlinear terms are introduced gradually. Statistics are chosen to study the improvements of both the new introduced and originally existed variables for the model characteristics, which are adopted to determine the model variables to retain or eliminate. So the optimal model is obtained through data fitting effect measurement or significance test. The simulation and classic time-series data experiment results show that the method proposed is simple, reliable and can be applied to practical engineering.

Health-related quality of life in patients with dual diagnosis: clinical correlates.

PubMed

Benaiges, Irina; Prat, Gemma; Adan, Ana

2012-09-05

Although the studies published so far have found an affectation in the Health Related Quality of Life (HRQOL) in both psychiatric and substance use dependence disorders, very few studies have applied HRQOL as an assessment measure in patients suffering both comorbid conditions, or Dual Diagnosis. The aim of the current study was to assess HRQOL in a group of patients with Dual Diagnosis compared to two other non-comorbid groups and to determine what clinical factors are related to HRQOL. Cross-sectional assessment of three experimental groups was made through the Short Form - 36 Item Health Survey (SF-36). The sample consisted of a group with Dual Diagnosis (DD; N = 35), one with Severe Mental Illness alone (SMI; N = 35) and another one with Substance Use Dependence alone (SUD; N = 35). The sample was composed only by males. To assess the clinical correlates of SF-36 HRQOL, lineal regression analyses were carried out. The DD group showed lower scores in most of the subscales, and in the mental health domain. The group with SUD showed in general a better state in the HRQOL while the group with SMI held an intermediate position with respect to the other two groups. Daily medication, suicidal attempts and daily number of coffees were significantly associated to HRQOL, especially in the DD group. The DD group showed lower self-reported mental health quality of life. Assessment of HRQOL in dual patients allows to identify specific needs in this population, and may help to establish therapeutic goals to improve interventions.

[Care costs and activity in the last three months of life of cancer patients who died in the Basque Country (Spain)].

PubMed

Nuño-Solinís, Roberto; Herrera Molina, Emilio; Librada Flores, Silvia; Orueta Mendía, Juan F; Cabrera-León, Andrés

To analyse the use of health resources and its budget in the last months of life of the population who died from malignant neoplasm in the Basque Autonomous Country (Spain). Retrospective observational study of a population with a diagnosis of malignancy deceased in the Basque Country (2010 and 2011). MDS and Mortality Register. gender, age, place of death, tumour location, clinical activity data and costs in the last three months of life. We performed a descriptive analysis of clinical activity and costs, and lineal multivariate regressions to obtain the adjusted mean costs by gender, age and place of death. 9,333 deaths from malignancy were identified in 2010 and 2011. 65.4% were men, 61.5% aged 70 or over, mean age 72.9 years, 71.1% died in hospital. People who died in the hospital had an average cost of about double that of the people who died at home (€14,794 and €7,491, respectively; p <0.001) and 31.3% higher than in the nursing home (€11,269; p <0.001). Greater interventions at the end of life at the community level are necessary, strengthening the care capacity of primary health care, both from training and support from expert teams in order to change the current care profile to a more outpatient care that allows a lower consumption of resources and greater care at home. Copyright © 2016 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.

Regression Analysis with Dummy Variables: Use and Interpretation.

ERIC Educational Resources Information Center

Hinkle, Dennis E.; Oliver, J. Dale

1986-01-01

Multiple regression analysis (MRA) may be used when both continuous and categorical variables are included as independent research variables. The use of MRA with categorical variables involves dummy coding, that is, assigning zeros and ones to levels of categorical variables. Caution is urged in results interpretation. (Author/CH)

Applied Statistics: From Bivariate through Multivariate Techniques [with CD-ROM

ERIC Educational Resources Information Center

Warner, Rebecca M.

2007-01-01

This book provides a clear introduction to widely used topics in bivariate and multivariate statistics, including multiple regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression. The approach is applied and does not require formal mathematics; equations are accompanied by verbal explanations. Students are asked…

REGRESSION MODELS THAT RELATE STREAMS TO WATERSHEDS: COPING WITH NUMEROUS, COLLINEAR PEDICTORS

EPA Science Inventory

GIS efforts can produce a very large number of watershed variables (climate, land use/land cover and topography, all defined for multiple areas of influence) that could serve as candidate predictors in a regression model of reach-scale stream features. Invariably, many of these ...

Identifying the Factors That Influence Change in SEBD Using Logistic Regression Analysis

ERIC Educational Resources Information Center

Camilleri, Liberato; Cefai, Carmel

2013-01-01

Multiple linear regression and ANOVA models are widely used in applications since they provide effective statistical tools for assessing the relationship between a continuous dependent variable and several predictors. However these models rely heavily on linearity and normality assumptions and they do not accommodate categorical dependent…

A Constrained Linear Estimator for Multiple Regression

ERIC Educational Resources Information Center

Davis-Stober, Clintin P.; Dana, Jason; Budescu, David V.

2010-01-01

"Improper linear models" (see Dawes, Am. Psychol. 34:571-582, "1979"), such as equal weighting, have garnered interest as alternatives to standard regression models. We analyze the general circumstances under which these models perform well by recasting a class of "improper" linear models as "proper" statistical models with a single predictor. We…

Regression of a vaginal leiomyoma after ovariohysterectomy in a dog: a case report.

PubMed

Sathya, Suresh; Linn, Kathleen

2014-01-01

An 11 yr old female mixed-breed Siberian husky was presented with a history of sanguineous vaginal discharge, swelling of the perineal area, decreased appetite, and lethargy. A single, large vaginal leiomyoma and multiple mammary tumors were diagnosed. Mastectomy and ovariohysterectomy were performed. The vaginal leiomyoma regressed completely after ovariohysterectomy. This is the first reported case of spontaneous regression of a vaginal leiomyoma after ovariohysterectomy in a dog.

Association of Emotional Labor and Occupational Stressors with Depressive Symptoms among Women Sales Workers at a Clothing Shopping Mall in the Republic of Korea: A Cross-Sectional Study

PubMed Central

Chung, Yuh-Jin; Jung, Woo-Chul

2017-01-01

In the distribution service industry, sales people often experience multiple occupational stressors such as excessive emotional labor, workplace mistreatment, and job insecurity. The present study aimed to explore the associations of these stressors with depressive symptoms among women sales workers at a clothing shopping mall in Korea. A cross sectional study was conducted on 583 women who consist of clothing sales workers and manual workers using a structured questionnaire to assess demographic factors, occupational stressors, and depressive symptoms. Multiple regression analyses were performed to explore the association of these stressors with depressive symptoms. Scores for job stress subscales such as job demand, job control, and job insecurity were higher among sales workers than among manual workers (p < 0.01). The multiple regression analysis revealed the association between occupation and depressive symptoms after controlling for age, educational level, cohabiting status, and occupational stressors (sβ = 0.08, p = 0.04). A significant interaction effect between occupation and social support was also observed in this model (sβ = −0.09, p = 0.02). The multiple regression analysis stratified by occupation showed that job demand, job insecurity, and workplace mistreatment were significantly associated with depressive symptoms in both occupations (p < 0.05), although the strength of statistical associations were slightly different. We found negative associations of social support (sβ = −0.22, p < 0.01) and emotional effort (sβ = −0.17, p < 0.01) with depressive symptoms in another multiple regression model for sales workers. Emotional dissonance (sβ = 0.23, p < 0.01) showed positive association with depressive symptoms in this model. The result of this study indicated that reducing occupational stressors would be effective for women sales workers to prevent depressive symptoms. In particular, promoting social support could be the most effective way to promote women sales workers’ mental health. PMID:29168777

Association of Emotional Labor and Occupational Stressors with Depressive Symptoms among Women Sales Workers at a Clothing Shopping Mall in the Republic of Korea: A Cross-Sectional Study.

PubMed

Chung, Yuh-Jin; Jung, Woo-Chul; Kim, Hyunjoo; Cho, Seong-Sik

2017-11-23

In the distribution service industry, sales people often experience multiple occupational stressors such as excessive emotional labor, workplace mistreatment, and job insecurity. The present study aimed to explore the associations of these stressors with depressive symptoms among women sales workers at a clothing shopping mall in Korea. A cross sectional study was conducted on 583 women who consist of clothing sales workers and manual workers using a structured questionnaire to assess demographic factors, occupational stressors, and depressive symptoms. Multiple regression analyses were performed to explore the association of these stressors with depressive symptoms. Scores for job stress subscales such as job demand, job control, and job insecurity were higher among sales workers than among manual workers ( p < 0.01). The multiple regression analysis revealed the association between occupation and depressive symptoms after controlling for age, educational level, cohabiting status, and occupational stressors (sβ = 0.08, p = 0.04). A significant interaction effect between occupation and social support was also observed in this model (sβ = -0.09, p = 0.02). The multiple regression analysis stratified by occupation showed that job demand, job insecurity, and workplace mistreatment were significantly associated with depressive symptoms in both occupations ( p < 0.05), although the strength of statistical associations were slightly different. We found negative associations of social support (sβ = -0.22, p < 0.01) and emotional effort (sβ = -0.17, p < 0.01) with depressive symptoms in another multiple regression model for sales workers. Emotional dissonance (sβ = 0.23, p < 0.01) showed positive association with depressive symptoms in this model. The result of this study indicated that reducing occupational stressors would be effective for women sales workers to prevent depressive symptoms. In particular, promoting social support could be the most effective way to promote women sales workers' mental health.

When Law Students Read Multiple Documents about Global Warming: Examining the Role of Topic-Specific Beliefs about the Nature of Knowledge and Knowing

ERIC Educational Resources Information Center

Braten, Ivar; Stromso, Helge I.

2010-01-01

In this study, law students (n = 49) read multiple authentic documents presenting conflicting information on the topic of climate change and responded to verification tasks assessing their superficial as well as their deeper-level within- and across-documents comprehension. Hierarchical multiple regression analyses showed that even after variance…

Deep ensemble learning of sparse regression models for brain disease diagnosis.

PubMed

Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

2017-04-01

Recent studies on brain imaging analysis witnessed the core roles of machine learning techniques in computer-assisted intervention for brain disease diagnosis. Of various machine-learning techniques, sparse regression models have proved their effectiveness in handling high-dimensional data but with a small number of training samples, especially in medical problems. In the meantime, deep learning methods have been making great successes by outperforming the state-of-the-art performances in various applications. In this paper, we propose a novel framework that combines the two conceptually different methods of sparse regression and deep learning for Alzheimer's disease/mild cognitive impairment diagnosis and prognosis. Specifically, we first train multiple sparse regression models, each of which is trained with different values of a regularization control parameter. Thus, our multiple sparse regression models potentially select different feature subsets from the original feature set; thereby they have different powers to predict the response values, i.e., clinical label and clinical scores in our work. By regarding the response values from our sparse regression models as target-level representations, we then build a deep convolutional neural network for clinical decision making, which thus we call 'Deep Ensemble Sparse Regression Network.' To our best knowledge, this is the first work that combines sparse regression models with deep neural network. In our experiments with the ADNI cohort, we validated the effectiveness of the proposed method by achieving the highest diagnostic accuracies in three classification tasks. We also rigorously analyzed our results and compared with the previous studies on the ADNI cohort in the literature. Copyright © 2017 Elsevier B.V. All rights reserved.

Deep ensemble learning of sparse regression models for brain disease diagnosis

PubMed Central

Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

2018-01-01

Recent studies on brain imaging analysis witnessed the core roles of machine learning techniques in computer-assisted intervention for brain disease diagnosis. Of various machine-learning techniques, sparse regression models have proved their effectiveness in handling high-dimensional data but with a small number of training samples, especially in medical problems. In the meantime, deep learning methods have been making great successes by outperforming the state-of-the-art performances in various applications. In this paper, we propose a novel framework that combines the two conceptually different methods of sparse regression and deep learning for Alzheimer’s disease/mild cognitive impairment diagnosis and prognosis. Specifically, we first train multiple sparse regression models, each of which is trained with different values of a regularization control parameter. Thus, our multiple sparse regression models potentially select different feature subsets from the original feature set; thereby they have different powers to predict the response values, i.e., clinical label and clinical scores in our work. By regarding the response values from our sparse regression models as target-level representations, we then build a deep convolutional neural network for clinical decision making, which thus we call ‘ Deep Ensemble Sparse Regression Network.’ To our best knowledge, this is the first work that combines sparse regression models with deep neural network. In our experiments with the ADNI cohort, we validated the effectiveness of the proposed method by achieving the highest diagnostic accuracies in three classification tasks. We also rigorously analyzed our results and compared with the previous studies on the ADNI cohort in the literature. PMID:28167394

Statistical Energy Analysis for Designers. Part 1. Basic Theory

DTIC Science & Technology

1974-09-01

deterministic system. That is a possible answer, but it may not be the most useful one. The most glaring deficiency of SEA is its inability to deal with...present whether this represents the "next logical step" in the chain that we spoke of, but it bears examination. 13 A second deficiency of SEA is its...undamped string, p = lineal density, r = 0, and A=-T(D/ax) 2. Thus, Eq. (2.3.2) becomes Tk2 = pw2, or k = ±w/c , (2.3.3) where c=VT- is the speed of

An Improved Analytic Model for Microdosimeter Response

NASA Technical Reports Server (NTRS)

Shinn, Judy L.; Wilson, John W.; Xapsos, Michael A.

2001-01-01

An analytic model used to predict energy deposition fluctuations in a microvolume by ions through direct events is improved to include indirect delta ray events. The new model can now account for the increase in flux at low lineal energy when the ions are of very high energy. Good agreement is obtained between the calculated results and available data for laboratory ion beams. Comparison of GCR (galactic cosmic ray) flux between Shuttle TEPC (tissue equivalent proportional counter) flight data and current calculations draws a different assessment of developmental work required for the GCR transport code (HZETRN) than previously concluded.

Application of Sampling Based Model Predictive Control to an Autonomous Underwater Vehicle

DTIC Science & Technology

2010-07-01

lineales y evita el mínimo local, el cual puede ocasionar que un vehículo quede inmóvil detrás de los obstáculos. El algoritmo SBMPC se aplica a un UAV...en un ambiente cargado de reflexiones de onda y a un UAV en un problema de mínimo común local. El algoritmo es luego usado en un modelo cinemático...tareas difíciles en ambientes atiborrados de reflexiones de onda tales como muelles y puertos. Sin embargo, dado que los UUVs tienen dinámicas áltamente

Quantile Regression in the Study of Developmental Sciences

PubMed Central

Petscher, Yaacov; Logan, Jessica A. R.

2014-01-01

Linear regression analysis is one of the most common techniques applied in developmental research, but only allows for an estimate of the average relations between the predictor(s) and the outcome. This study describes quantile regression, which provides estimates of the relations between the predictor(s) and outcome, but across multiple points of the outcome’s distribution. Using data from the High School and Beyond and U.S. Sustained Effects Study databases, quantile regression is demonstrated and contrasted with linear regression when considering models with: (a) one continuous predictor, (b) one dichotomous predictor, (c) a continuous and a dichotomous predictor, and (d) a longitudinal application. Results from each example exhibited the differential inferences which may be drawn using linear or quantile regression. PMID:24329596

Depressive disorder in pregnant Latin women: does intimate partner violence matter?

PubMed

Fonseca-Machado, Mariana de Oliveira; Alves, Lisiane Camargo; Monteiro, Juliana Cristina Dos Santos; Stefanello, Juliana; Nakano, Ana Márcia Spanó; Haas, Vanderlei José; Gomes-Sponholz, Flávia

2015-05-01

To identify the association of antenatal depressive symptoms with intimate partner violence during the current pregnancy in Brazilian women. Intimate partner violence is an important risk factor for antenatal depression. To the authors' knowledge, there has been no study to date that assessed the association between intimate partner violence during pregnancy and antenatal depressive symptoms among Brazilian women. Cross-sectional study. Three hundred and fifty-eight pregnant women were enrolled in the study. The Edinburgh Postnatal Depression Scale and an adapted version of the instrument used in the World Health Organization Multi-country Study on Women's Health and Domestic Violence were used to measure antenatal depressive symptoms and psychological, physical and sexual acts of intimate partner violence during the current pregnancy respectively. Multiple logistic regression and multiple linear regression were used for data analysis. The prevalence of antenatal depressive symptoms, as determined by the cut-off score of 12 in the Edinburgh Postnatal Depression Scale, was 28·2% (101). Of the participants, 63 (17·6%) reported some type of intimate partner violence during pregnancy. Among them, 60 (95·2%) reported suffering psychological violence, 23 (36·5%) physical violence and one (1·6%) sexual violence. Multiple logistic regression and multiple linear regression indicated that antenatal depressive symptoms are extremely associated with intimate partner violence during pregnancy. Among Brazilian women, exposure to intimate partner violence during pregnancy increases the chances of experiencing antenatal depressive symptoms. Clinical nurses and nurses midwifes should pay attention to the particularities of Brazilian women, especially with regard to the occurrence of intimate partner violence, whose impacts on the mental health of this population are extremely significant, both during the gestational period and postpartum. © 2015 John Wiley & Sons Ltd.

Simple to complex modeling of breathing volume using a motion sensor.

PubMed

John, Dinesh; Staudenmayer, John; Freedson, Patty

2013-06-01

To compare simple and complex modeling techniques to estimate categories of low, medium, and high ventilation (VE) from ActiGraph™ activity counts. Vertical axis ActiGraph™ GT1M activity counts, oxygen consumption and VE were measured during treadmill walking and running, sports, household chores and labor-intensive employment activities. Categories of low (<19.3 l/min), medium (19.3 to 35.4 l/min) and high (>35.4 l/min) VEs were derived from activity intensity classifications (light <2.9 METs, moderate 3.0 to 5.9 METs and vigorous >6.0 METs). We examined the accuracy of two simple techniques (multiple regression and activity count cut-point analyses) and one complex (random forest technique) modeling technique in predicting VE from activity counts. Prediction accuracy of the complex random forest technique was marginally better than the simple multiple regression method. Both techniques accurately predicted VE categories almost 80% of the time. The multiple regression and random forest techniques were more accurate (85 to 88%) in predicting medium VE. Both techniques predicted the high VE (70 to 73%) with greater accuracy than low VE (57 to 60%). Actigraph™ cut-points for light, medium and high VEs were <1381, 1381 to 3660 and >3660 cpm. There were minor differences in prediction accuracy between the multiple regression and the random forest technique. This study provides methods to objectively estimate VE categories using activity monitors that can easily be deployed in the field. Objective estimates of VE should provide a better understanding of the dose-response relationship between internal exposure to pollutants and disease. Copyright © 2013 Elsevier B.V. All rights reserved.

Functional capacity following univentricular repair--midterm outcome.

PubMed

Sen, Supratim; Bandyopadhyay, Biswajit; Eriksson, Peter; Chattopadhyay, Amitabha

2012-01-01

Previous studies have seldom compared functional capacity in children following Fontan procedure alongside those with Glenn operation as destination therapy. We hypothesized that Fontan circulation enables better midterm submaximal exercise capacity as compared to Glenn physiology and evaluated this using the 6-minute walk test. Fifty-seven children aged 5-18 years with Glenn (44) or Fontan (13) operations were evaluated with standard 6-minute walk protocols. Baseline SpO(2) was significantly lower in Glenn patients younger than 10 years compared to Fontan counterparts and similar in the two groups in older children. Postexercise SpO(2) fell significantly in Glenn patients compared to the Fontan group. There was no statistically significant difference in baseline, postexercise, or postrecovery heart rates (HRs), or 6-minute walk distances in the two groups. Multiple regression analysis revealed lower resting HR, higher resting SpO(2) , and younger age at latest operation to be significant determinants of longer 6-minute walk distance. Multiple regression analysis also established that younger age at operation, higher resting SpO(2) , Fontan operation, lower resting HR, and lower postexercise HR were significant determinants of higher postexercise SpO(2) . Younger age at operation and exercise, lower resting HR and postexercise HR, higher resting SpO(2) and postexercise SpO(2) , and dominant ventricular morphology being left ventricular or indeterminate/mixed had significant association with better 6-minute work on multiple regression analysis. Lower resting HR had linear association with longer 6-minute walk distances in the Glenn patients. Compared to Glenn physiology, Fontan operation did not have better submaximal exercise capacity assessed by walk distance or work on multiple regression analysis. Lower resting HR, higher resting SpO(2) , and younger age at operation were factors uniformly associated with better submaximal exercise capacity. © 2012 Wiley Periodicals, Inc.

Multiple regression and Artificial Neural Network for long-term rainfall forecasting using large scale climate modes

NASA Astrophysics Data System (ADS)

Mekanik, F.; Imteaz, M. A.; Gato-Trinidad, S.; Elmahdi, A.

2013-10-01

In this study, the application of Artificial Neural Networks (ANN) and Multiple regression analysis (MR) to forecast long-term seasonal spring rainfall in Victoria, Australia was investigated using lagged El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) as potential predictors. The use of dual (combined lagged ENSO-IOD) input sets for calibrating and validating ANN and MR Models is proposed to investigate the simultaneous effect of past values of these two major climate modes on long-term spring rainfall prediction. The MR models that did not violate the limits of statistical significance and multicollinearity were selected for future spring rainfall forecast. The ANN was developed in the form of multilayer perceptron using Levenberg-Marquardt algorithm. Both MR and ANN modelling were assessed statistically using mean square error (MSE), mean absolute error (MAE), Pearson correlation (r) and Willmott index of agreement (d). The developed MR and ANN models were tested on out-of-sample test sets; the MR models showed very poor generalisation ability for east Victoria with correlation coefficients of -0.99 to -0.90 compared to ANN with correlation coefficients of 0.42-0.93; ANN models also showed better generalisation ability for central and west Victoria with correlation coefficients of 0.68-0.85 and 0.58-0.97 respectively. The ability of multiple regression models to forecast out-of-sample sets is compatible with ANN for Daylesford in central Victoria and Kaniva in west Victoria (r = 0.92 and 0.67 respectively). The errors of the testing sets for ANN models are generally lower compared to multiple regression models. The statistical analysis suggest the potential of ANN over MR models for rainfall forecasting using large scale climate modes.

HRCT findings of collagen vascular disease-related interstitial pneumonia (CVD-IP): a comparative study among individual underlying diseases.

PubMed

Tanaka, N; Kunihiro, Y; Kubo, M; Kawano, R; Oishi, K; Ueda, K; Gondo, T

2018-05-29

To identify characteristic high-resolution computed tomography (CT) findings for individual collagen vascular disease (CVD)-related interstitial pneumonias (IPs). The HRCT findings of 187 patients with CVD, including 55 patients with rheumatoid arthritis (RA), 50 with systemic sclerosis (SSc), 46 with polymyositis/dermatomyositis (PM/DM), 15 with mixed connective tissue disease, 11 with primary Sjögren's syndrome, and 10 with systemic lupus erythematosus, were evaluated. Lung parenchymal abnormalities were compared among CVDs using χ 2 test, Kruskal-Wallis test, and multiple logistic regression analysis. A CT-pathology correlation was performed in 23 patients. In RA-IP, honeycombing was identified as the significant indicator based on multiple logistic regression analyses. Traction bronchiectasis (81.8%) was further identified as the most frequent finding based on χ 2 test. In SSc IP, lymph node enlargement and oesophageal dilatation were identified as the indicators based on multiple logistic regression analyses, and ground-glass opacity (GGO) was the most extensive based on Kruskal-Wallis test, which reflects the higher frequency of the pathological nonspecific interstitial pneumonia (NSIP) pattern present in the CT-pathology correlation. In PM/DM IP, airspace consolidation and the absence of honeycombing were identified as the indicators based on multiple logistic regression analyses, and predominance of consolidation over GGO (32.6%) and predominant subpleural distribution of GGO/consolidation (41.3%) were further identified as the most frequent findings based on χ 2 test, which reflects the higher frequency of the pathological NSIP and/or the organising pneumonia patterns present in the CT-pathology correlation. Several characteristic high-resolution CT findings with utility for estimating underlying CVD were identified. Copyright © 2018 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Construction of multiple linear regression models using blood biomarkers for selecting against abdominal fat traits in broilers.

PubMed

Dong, J Q; Zhang, X Y; Wang, S Z; Jiang, X F; Zhang, K; Ma, G W; Wu, M Q; Li, H; Zhang, H

2018-01-01

Plasma very low-density lipoprotein (VLDL) can be used to select for low body fat or abdominal fat (AF) in broilers, but its correlation with AF is limited. We investigated whether any other biochemical indicator can be used in combination with VLDL for a better selective effect. Nineteen plasma biochemical indicators were measured in male chickens from the Northeast Agricultural University broiler lines divergently selected for AF content (NEAUHLF) in the fed state at 46 and 48 d of age. The average concentration of every parameter for the 2 d was used for statistical analysis. Levels of these 19 plasma biochemical parameters were compared between the lean and fat lines. The phenotypic correlations between these plasma biochemical indicators and AF traits were analyzed. Then, multiple linear regression models were constructed to select the best model used for selecting against AF content. and the heritabilities of plasma indicators contained in the best models were estimated. The results showed that 11 plasma biochemical indicators (triglycerides, total bile acid, total protein, globulin, albumin/globulin, aspartate transaminase, alanine transaminase, gamma-glutamyl transpeptidase, uric acid, creatinine, and VLDL) differed significantly between the lean and fat lines (P < 0.01), and correlated significantly with AF traits (P < 0.05). The best multiple linear regression models based on albumin/globulin, VLDL, triglycerides, globulin, total bile acid, and uric acid, had higher R2 (0.73) than the model based only on VLDL (0.21). The plasma parameters included in the best models had moderate heritability estimates (0.21 ≤ h2 ≤ 0.43). These results indicate that these multiple linear regression models can be used to select for lean broiler chickens. © 2017 Poultry Science Association Inc.

Automating approximate Bayesian computation by local linear regression.

PubMed

Thornton, Kevin R

2009-07-07

In several biological contexts, parameter inference often relies on computationally-intensive techniques. "Approximate Bayesian Computation", or ABC, methods based on summary statistics have become increasingly popular. A particular flavor of ABC based on using a linear regression to approximate the posterior distribution of the parameters, conditional on the summary statistics, is computationally appealing, yet no standalone tool exists to automate the procedure. Here, I describe a program to implement the method. The software package ABCreg implements the local linear-regression approach to ABC. The advantages are: 1. The code is standalone, and fully-documented. 2. The program will automatically process multiple data sets, and create unique output files for each (which may be processed immediately in R), facilitating the testing of inference procedures on simulated data, or the analysis of multiple data sets. 3. The program implements two different transformation methods for the regression step. 4. Analysis options are controlled on the command line by the user, and the program is designed to output warnings for cases where the regression fails. 5. The program does not depend on any particular simulation machinery (coalescent, forward-time, etc.), and therefore is a general tool for processing the results from any simulation. 6. The code is open-source, and modular.Examples of applying the software to empirical data from Drosophila melanogaster, and testing the procedure on simulated data, are shown. In practice, the ABCreg simplifies implementing ABC based on local-linear regression.

Association between response rates and survival outcomes in patients with newly diagnosed multiple myeloma. A systematic review and meta-regression analysis.

PubMed

Mainou, Maria; Madenidou, Anastasia-Vasiliki; Liakos, Aris; Paschos, Paschalis; Karagiannis, Thomas; Bekiari, Eleni; Vlachaki, Efthymia; Wang, Zhen; Murad, Mohammad Hassan; Kumar, Shaji; Tsapas, Apostolos

2017-06-01

We performed a systematic review and meta-regression analysis of randomized control trials to investigate the association between response to initial treatment and survival outcomes in patients with newly diagnosed multiple myeloma (MM). Response outcomes included complete response (CR) and the combined outcome of CR or very good partial response (VGPR), while survival outcomes were overall survival (OS) and progression-free survival (PFS). We used random-effect meta-regression models and conducted sensitivity analyses based on definition of CR and study quality. Seventy-two trials were included in the systematic review, 63 of which contributed data in meta-regression analyses. There was no association between OS and CR in patients without autologous stem cell transplant (ASCT) (regression coefficient: .02, 95% confidence interval [CI] -0.06, 0.10), in patients undergoing ASCT (-.11, 95% CI -0.44, 0.22) and in trials comparing ASCT with non-ASCT patients (.04, 95% CI -0.29, 0.38). Similarly, OS did not correlate with the combined metric of CR or VGPR, and no association was evident between response outcomes and PFS. Sensitivity analyses yielded similar results. This meta-regression analysis suggests that there is no association between conventional response outcomes and survival in patients with newly diagnosed MM. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

A non-linear regression analysis program for describing electrophysiological data with multiple functions using Microsoft Excel.

PubMed

Brown, Angus M

2006-04-01

The objective of this present study was to demonstrate a method for fitting complex electrophysiological data with multiple functions using the SOLVER add-in of the ubiquitous spreadsheet Microsoft Excel. SOLVER minimizes the difference between the sum of the squares of the data to be fit and the function(s) describing the data using an iterative generalized reduced gradient method. While it is a straightforward procedure to fit data with linear functions, and we have previously demonstrated a method of non-linear regression analysis of experimental data based upon a single function, it is more complex to fit data with multiple functions, usually requiring specialized expensive computer software. In this paper we describe an easily understood program for fitting experimentally acquired data, in this case the stimulus-evoked compound action potential from the mouse optic nerve, with multiple Gaussian functions. The program is flexible and can be applied to describe data with a wide variety of user-input functions.

Regression-based pediatric norms for the brief visuospatial memory test: revised and the symbol digit modalities test.

PubMed

Smerbeck, A M; Parrish, J; Yeh, E A; Hoogs, M; Krupp, Lauren B; Weinstock-Guttman, B; Benedict, R H B

2011-04-01

The Brief Visuospatial Memory Test - Revised (BVMTR) and the Symbol Digit Modalities Test (SDMT) oral-only administration are known to be sensitive to cerebral disease in adult samples, but pediatric norms are not available. A demographically balanced sample of healthy control children (N = 92) ages 6-17 was tested with the BVMTR and SDMT. Multiple regression analysis (MRA) was used to develop demographically controlled normative equations. This analysis provided equations that were then used to construct demographically adjusted z-scores for the BVMTR Trial 1, Trial 2, Trial 3, Total Learning, and Delayed Recall indices, as well as the SDMT total correct score. To demonstrate the utility of this approach, a comparison group of children with acute disseminated encephalomyelitis (ADEM) or multiple sclerosis (MS) were also assessed. We find that these visual processing tests discriminate neurological patients from controls. As the tests are validated in adult multiple sclerosis, they are likely to be useful in monitoring pediatric onset multiple sclerosis patients as they transition into adulthood.

Confidence Intervals for Squared Semipartial Correlation Coefficients: The Effect of Nonnormality

ERIC Educational Resources Information Center

Algina, James; Keselman, H. J.; Penfield, Randall D.

2010-01-01

The increase in the squared multiple correlation coefficient ([delta]R[superscript 2]) associated with a variable in a regression equation is a commonly used measure of importance in regression analysis. Algina, Keselman, and Penfield found that intervals based on asymptotic principles were typically very inaccurate, even though the sample size…

Generalized and synthetic regression estimators for randomized branch sampling

Treesearch

David L. R. Affleck; Timothy G. Gregoire

2015-01-01

In felled-tree studies, ratio and regression estimators are commonly used to convert more readily measured branch characteristics to dry crown mass estimates. In some cases, data from multiple trees are pooled to form these estimates. This research evaluates the utility of both tactics in the estimation of crown biomass following randomized branch sampling (...

Does Bootstrap Procedure Provide Biased Estimates? An Empirical Examination for a Case of Multiple Regression.

ERIC Educational Resources Information Center

Fan, Xitao

This paper empirically and systematically assessed the performance of bootstrap resampling procedure as it was applied to a regression model. Parameter estimates from Monte Carlo experiments (repeated sampling from population) and bootstrap experiments (repeated resampling from one original bootstrap sample) were generated and compared. Sample…

Progressive and Regressive Aspects of Information Technology in Society: A Third Sector Perspective

ERIC Educational Resources Information Center

Miller, Kandace R.

2009-01-01

This dissertation explores the impact of information technology on progressive and regressive values in society from the perspective of one international foundation and four of its technology-related programs. Through a critical interpretive approach employing an instrumental multiple-case method, a framework to help explain the influence of…

Correlation Weights in Multiple Regression

ERIC Educational Resources Information Center

Waller, Niels G.; Jones, Jeff A.

2010-01-01

A general theory on the use of correlation weights in linear prediction has yet to be proposed. In this paper we take initial steps in developing such a theory by describing the conditions under which correlation weights perform well in population regression models. Using OLS weights as a comparison, we define cases in which the two weighting…

No Evidence of Reaction Time Slowing in Autism Spectrum Disorder

ERIC Educational Resources Information Center

Ferraro, F. Richard

2016-01-01

A total of 32 studies comprising 238 simple reaction time and choice reaction time conditions were examined in individuals with autism spectrum disorder (n?=?964) and controls (n?=?1032). A Brinley plot/multiple regression analysis was performed on mean reaction times, regressing autism spectrum disorder performance onto the control performance as…

Criteria for the use of regression analysis for remote sensing of sediment and pollutants

NASA Technical Reports Server (NTRS)

Whitlock, C. H.; Kuo, C. Y.; Lecroy, S. R. (Principal Investigator)

1982-01-01

Data analysis procedures for quantification of water quality parameters that are already identified and are known to exist within the water body are considered. The liner multiple-regression technique was examined as a procedure for defining and calibrating data analysis algorithms for such instruments as spectrometers and multispectral scanners.

An Empirical Study of Eight Nonparametric Tests in Hierarchical Regression.

ERIC Educational Resources Information Center

Harwell, Michael; Serlin, Ronald C.

When normality does not hold, nonparametric tests represent an important data-analytic alternative to parametric tests. However, the use of nonparametric tests in educational research has been limited by the absence of easily performed tests for complex experimental designs and analyses, such as factorial designs and multiple regression analyses,…

Multiple Logistic Regression Analysis of Cigarette Use among High School Students

ERIC Educational Resources Information Center

Adwere-Boamah, Joseph

2011-01-01

A binary logistic regression analysis was performed to predict high school students' cigarette smoking behavior from selected predictors from 2009 CDC Youth Risk Behavior Surveillance Survey. The specific target student behavior of interest was frequent cigarette use. Five predictor variables included in the model were: a) race, b) frequency of…

The Development and Demonstration of Multiple Regression Models for Operant Conditioning Questions.

ERIC Educational Resources Information Center

Fanning, Fred; Newman, Isadore

Based on the assumption that inferential statistics can make the operant conditioner more sensitive to possible significant relationships, regressions models were developed to test the statistical significance between slopes and Y intercepts of the experimental and control group subjects. These results were then compared to the traditional operant…

How Many Subjects Does It Take to Do a Regression Analysis?

ERIC Educational Resources Information Center

Green, Samuel B.

1991-01-01

An evaluation of the rules-of-thumb used to determine the minimum number of subjects required to conduct multiple regression analyses suggests that researchers who use a rule of thumb rather than power analyses trade simplicity of use for accuracy and specificity of response. Insufficient power is likely to result. (SLD)

Hierarchical Multiple Regression in Counseling Research: Common Problems and Possible Remedies.

ERIC Educational Resources Information Center

Petrocelli, John V.

2003-01-01

A brief content analysis was conducted on the use of hierarchical regression in counseling research published in the "Journal of Counseling Psychology" and the "Journal of Counseling & Development" during the years 1997-2001. Common problems are cited and possible remedies are described. (Contains 43 references and 3 tables.) (Author)

Assistive Technologies for Second-Year Statistics Students Who Are Blind

ERIC Educational Resources Information Center

Erhardt, Robert J.; Shuman, Michael P.

2015-01-01

At Wake Forest University, a student who is blind enrolled in a second course in statistics. The course covered simple and multiple regression, model diagnostics, model selection, data visualization, and elementary logistic regression. These topics required that the student both interpret and produce three sets of materials: mathematical writing,…

Testing a single regression coefficient in high dimensional linear models

PubMed Central

Zhong, Ping-Shou; Li, Runze; Wang, Hansheng; Tsai, Chih-Ling

2017-01-01

In linear regression models with high dimensional data, the classical z-test (or t-test) for testing the significance of each single regression coefficient is no longer applicable. This is mainly because the number of covariates exceeds the sample size. In this paper, we propose a simple and novel alternative by introducing the Correlated Predictors Screening (CPS) method to control for predictors that are highly correlated with the target covariate. Accordingly, the classical ordinary least squares approach can be employed to estimate the regression coefficient associated with the target covariate. In addition, we demonstrate that the resulting estimator is consistent and asymptotically normal even if the random errors are heteroscedastic. This enables us to apply the z-test to assess the significance of each covariate. Based on the p-value obtained from testing the significance of each covariate, we further conduct multiple hypothesis testing by controlling the false discovery rate at the nominal level. Then, we show that the multiple hypothesis testing achieves consistent model selection. Simulation studies and empirical examples are presented to illustrate the finite sample performance and the usefulness of the proposed method, respectively. PMID:28663668

Testing a single regression coefficient in high dimensional linear models.

PubMed

Lan, Wei; Zhong, Ping-Shou; Li, Runze; Wang, Hansheng; Tsai, Chih-Ling

2016-11-01

In linear regression models with high dimensional data, the classical z -test (or t -test) for testing the significance of each single regression coefficient is no longer applicable. This is mainly because the number of covariates exceeds the sample size. In this paper, we propose a simple and novel alternative by introducing the Correlated Predictors Screening (CPS) method to control for predictors that are highly correlated with the target covariate. Accordingly, the classical ordinary least squares approach can be employed to estimate the regression coefficient associated with the target covariate. In addition, we demonstrate that the resulting estimator is consistent and asymptotically normal even if the random errors are heteroscedastic. This enables us to apply the z -test to assess the significance of each covariate. Based on the p -value obtained from testing the significance of each covariate, we further conduct multiple hypothesis testing by controlling the false discovery rate at the nominal level. Then, we show that the multiple hypothesis testing achieves consistent model selection. Simulation studies and empirical examples are presented to illustrate the finite sample performance and the usefulness of the proposed method, respectively.

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data.

PubMed

Spelman, Tim; Gray, Orla; Lucas, Robyn; Butzkueven, Helmut

2015-12-09

This report describes a novel Stata-based application of trigonometric regression modelling to 55 years of multiple sclerosis relapse data from 46 clinical centers across 20 countries located in both hemispheres. Central to the success of this method was the strategic use of plot analysis to guide and corroborate the statistical regression modelling. Initial plot analysis was necessary for establishing realistic hypotheses regarding the presence and structural form of seasonal and latitudinal influences on relapse probability and then testing the performance of the resultant models. Trigonometric regression was then necessary to quantify these relationships, adjust for important confounders and provide a measure of certainty as to how plausible these associations were. Synchronization of graphing techniques with regression modelling permitted a systematic refinement of models until best-fit convergence was achieved, enabling novel inferences to be made regarding the independent influence of both season and latitude in predicting relapse onset timing in MS. These methods have the potential for application across other complex disease and epidemiological phenomena suspected or known to vary systematically with season and/or geographic location.

Accounting for estimated IQ in neuropsychological test performance with regression-based techniques.

PubMed

Testa, S Marc; Winicki, Jessica M; Pearlson, Godfrey D; Gordon, Barry; Schretlen, David J

2009-11-01

Regression-based normative techniques account for variability in test performance associated with multiple predictor variables and generate expected scores based on algebraic equations. Using this approach, we show that estimated IQ, based on oral word reading, accounts for 1-9% of the variability beyond that explained by individual differences in age, sex, race, and years of education for most cognitive measures. These results confirm that adding estimated "premorbid" IQ to demographic predictors in multiple regression models can incrementally improve the accuracy with which regression-based norms (RBNs) benchmark expected neuropsychological test performance in healthy adults. It remains to be seen whether the incremental variance in test performance explained by estimated "premorbid" IQ translates to improved diagnostic accuracy in patient samples. We describe these methods, and illustrate the step-by-step application of RBNs with two cases. We also discuss the rationale, assumptions, and caveats of this approach. More broadly, we note that adjusting test scores for age and other characteristics might actually decrease the accuracy with which test performance predicts absolute criteria, such as the ability to drive or live independently.

Practical Session: Simple Linear Regression

NASA Astrophysics Data System (ADS)

Clausel, M.; Grégoire, G.

2014-12-01

Two exercises are proposed to illustrate the simple linear regression. The first one is based on the famous Galton's data set on heredity. We use the lm R command and get coefficients estimates, standard error of the error, R2, residuals …In the second example, devoted to data related to the vapor tension of mercury, we fit a simple linear regression, predict values, and anticipate on multiple linear regression. This pratical session is an excerpt from practical exercises proposed by A. Dalalyan at EPNC (see Exercises 1 and 2 of http://certis.enpc.fr/~dalalyan/Download/TP_ENPC_4.pdf).

Understanding logistic regression analysis.

PubMed

Sperandei, Sandro

2014-01-01

Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using examples to make it as simple as possible. After definition of the technique, the basic interpretation of the results is highlighted and then some special issues are discussed.

Two SPSS programs for interpreting multiple regression results.

PubMed

Lorenzo-Seva, Urbano; Ferrando, Pere J; Chico, Eliseo

2010-02-01

When multiple regression is used in explanation-oriented designs, it is very important to determine both the usefulness of the predictor variables and their relative importance. Standardized regression coefficients are routinely provided by commercial programs. However, they generally function rather poorly as indicators of relative importance, especially in the presence of substantially correlated predictors. We provide two user-friendly SPSS programs that implement currently recommended techniques and recent developments for assessing the relevance of the predictors. The programs also allow the user to take into account the effects of measurement error. The first program, MIMR-Corr.sps, uses a correlation matrix as input, whereas the second program, MIMR-Raw.sps, uses the raw data and computes bootstrap confidence intervals of different statistics. The SPSS syntax, a short manual, and data files related to this article are available as supplemental materials from http://brm.psychonomic-journals.org/content/supplemental.

Fast determination of total ginsenosides content in ginseng powder by near infrared reflectance spectroscopy

NASA Astrophysics Data System (ADS)

Chen, Hua-cai; Chen, Xing-dan; Lu, Yong-jun; Cao, Zhi-qiang

2006-01-01

Near infrared (NIR) reflectance spectroscopy was used to develop a fast determination method for total ginsenosides in Ginseng (Panax Ginseng) powder. The spectra were analyzed with multiplicative signal correction (MSC) correlation method. The best correlative spectra region with the total ginsenosides content was 1660 nm~1880 nm and 2230nm~2380 nm. The NIR calibration models of ginsenosides were built with multiple linear regression (MLR), principle component regression (PCR) and partial least squares (PLS) regression respectively. The results showed that the calibration model built with PLS combined with MSC and the optimal spectrum region was the best one. The correlation coefficient and the root mean square error of correction validation (RMSEC) of the best calibration model were 0.98 and 0.15% respectively. The optimal spectrum region for calibration was 1204nm~2014nm. The result suggested that using NIR to rapidly determinate the total ginsenosides content in ginseng powder were feasible.

Application of stepwise multiple regression techniques to inversion of Nimbus 'IRIS' observations.

NASA Technical Reports Server (NTRS)

Ohring, G.

1972-01-01

Exploratory studies with Nimbus-3 infrared interferometer-spectrometer (IRIS) data indicate that, in addition to temperature, such meteorological parameters as geopotential heights of pressure surfaces, tropopause pressure, and tropopause temperature can be inferred from the observed spectra with the use of simple regression equations. The technique of screening the IRIS spectral data by means of stepwise regression to obtain the best radiation predictors of meteorological parameters is validated. The simplicity of application of the technique and the simplicity of the derived linear regression equations - which contain only a few terms - suggest usefulness for this approach. Based upon the results obtained, suggestions are made for further development and exploitation of the stepwise regression analysis technique.

Parts-Per-Billion Mass Measurement Accuracy Achieved through the Combination of Multiple Linear Regression and Automatic Gain Control in a Fourier Transform Ion Cyclotron Resonance Mass Spectrometer

PubMed Central

Williams, D. Keith; Muddiman, David C.

2008-01-01

Fourier transform ion cyclotron resonance mass spectrometry has the ability to achieve unprecedented mass measurement accuracy (MMA); MMA is one of the most significant attributes of mass spectrometric measurements as it affords extraordinary molecular specificity. However, due to space-charge effects, the achievable MMA significantly depends on the total number of ions trapped in the ICR cell for a particular measurement. Even through the use of automatic gain control (AGC), the total ion population is not constant between spectra. Multiple linear regression calibration in conjunction with AGC is utilized in these experiments to formally account for the differences in total ion population in the ICR cell between the external calibration spectra and experimental spectra. This ability allows for the extension of dynamic range of the instrument while allowing mean MMA values to remain less than 1 ppm. In addition, multiple linear regression calibration is used to account for both differences in total ion population in the ICR cell as well as relative ion abundance of a given species, which also affords mean MMA values at the parts-per-billion level. PMID:17539605

Estimation of Finger Joint Angles Based on Electromechanical Sensing of Wrist Shape.

PubMed

Kawaguchi, Junki; Yoshimoto, Shunsuke; Kuroda, Yoshihiro; Oshiro, Osamu

2017-09-01

An approach to finger motion capture that places fewer restrictions on the usage environment and actions of the user is an important research topic in biomechanics and human-computer interaction. We proposed a system that electrically detects finger motion from the associated deformation of the wrist and estimates the finger joint angles using multiple regression models. A wrist-mounted sensing device with 16 electrodes detects deformation of the wrist from changes in electrical contact resistance at the skin. In this study, we experimentally investigated the accuracy of finger joint angle estimation, the adequacy of two multiple regression models, and the resolution of the estimation of total finger joint angles. In experiments, both the finger joint angles and the system output voltage were recorded as subjects performed flexion/extension of the fingers. These data were used for calibration using the least-squares method. The system was found to be capable of estimating the total finger joint angle with a root-mean-square error of 29-34 degrees. A multiple regression model with a second-order polynomial basis function was shown to be suitable for the estimation of all total finger joint angles, but not those of the thumb.

NIMEFI: gene regulatory network inference using multiple ensemble feature importance algorithms.

PubMed

Ruyssinck, Joeri; Huynh-Thu, Vân Anh; Geurts, Pierre; Dhaene, Tom; Demeester, Piet; Saeys, Yvan

2014-01-01

One of the long-standing open challenges in computational systems biology is the topology inference of gene regulatory networks from high-throughput omics data. Recently, two community-wide efforts, DREAM4 and DREAM5, have been established to benchmark network inference techniques using gene expression measurements. In these challenges the overall top performer was the GENIE3 algorithm. This method decomposes the network inference task into separate regression problems for each gene in the network in which the expression values of a particular target gene are predicted using all other genes as possible predictors. Next, using tree-based ensemble methods, an importance measure for each predictor gene is calculated with respect to the target gene and a high feature importance is considered as putative evidence of a regulatory link existing between both genes. The contribution of this work is twofold. First, we generalize the regression decomposition strategy of GENIE3 to other feature importance methods. We compare the performance of support vector regression, the elastic net, random forest regression, symbolic regression and their ensemble variants in this setting to the original GENIE3 algorithm. To create the ensemble variants, we propose a subsampling approach which allows us to cast any feature selection algorithm that produces a feature ranking into an ensemble feature importance algorithm. We demonstrate that the ensemble setting is key to the network inference task, as only ensemble variants achieve top performance. As second contribution, we explore the effect of using rankwise averaged predictions of multiple ensemble algorithms as opposed to only one. We name this approach NIMEFI (Network Inference using Multiple Ensemble Feature Importance algorithms) and show that this approach outperforms all individual methods in general, although on a specific network a single method can perform better. An implementation of NIMEFI has been made publicly available.

Abdominal girth, vertebral column length, and spread of spinal anesthesia in 30 minutes after plain bupivacaine 5 mg/mL.

PubMed

Zhou, Qing-he; Xiao, Wang-pin; Shen, Ying-yan

2014-07-01

The spread of spinal anesthesia is highly unpredictable. In patients with increased abdominal girth and short stature, a greater cephalad spread after a fixed amount of subarachnoidally administered plain bupivacaine is often observed. We hypothesized that there is a strong correlation between abdominal girth/vertebral column length and cephalad spread. Age, weight, height, body mass index, abdominal girth, and vertebral column length were recorded for 114 patients. The L3-L4 interspace was entered, and 3 mL of 0.5% plain bupivacaine was injected into the subarachnoid space. The cephalad spread (loss of temperature sensation and loss of pinprick discrimination) was assessed 30 minutes after intrathecal injection. Linear regression analysis was performed for age, weight, height, body mass index, abdominal girth, vertebral column length, and the spread of spinal anesthesia, and the combined linear contribution of age up to 55 years, weight, height, abdominal girth, and vertebral column length was tested by multiple regression analysis. Linear regression analysis showed that there was a significant univariate correlation among all 6 patient characteristics evaluated and the spread of spinal anesthesia (all P < 0.039) except for age and loss of temperature sensation (P > 0.068). Multiple regression analysis showed that abdominal girth and the vertebral column length were the key determinants for spinal anesthesia spread (both P < 0.0001), whereas age, weight, and height could be omitted without changing the results (all P > 0.059, all 95% confidence limits < 0.372). Multiple regression analysis revealed that the combination of a patient's 5 general characteristics, especially abdominal girth and vertebral column length, had a high predictive value for the spread of spinal anesthesia after a given dose of plain bupivacaine.

Stature estimation from the lengths of the growing foot-a study on North Indian adolescents.

PubMed

Krishan, Kewal; Kanchan, Tanuj; Passi, Neelam; DiMaggio, John A

2012-12-01

Stature estimation is considered as one of the basic parameters of the investigation process in unknown and commingled human remains in medico-legal case work. Race, age and sex are the other parameters which help in this process. Stature estimation is of the utmost importance as it completes the biological profile of a person along with the other three parameters of identification. The present research is intended to formulate standards for stature estimation from foot dimensions in adolescent males from North India and study the pattern of foot growth during the growing years. 154 male adolescents from the Northern part of India were included in the study. Besides stature, five anthropometric measurements that included the length of the foot from each toe (T1, T2, T3, T4, and T5 respectively) to pternion were measured on each foot. The data was analyzed statistically using Student's t-test, Pearson's correlation, linear and multiple regression analysis for estimation of stature and growth of foot during ages 13-18 years. Correlation coefficients between stature and all the foot measurements were found to be highly significant and positively correlated. Linear regression models and multiple regression models (with age as a co-variable) were derived for estimation of stature from the different measurements of the foot. Multiple regression models (with age as a co-variable) estimate stature with greater accuracy than the regression models for 13-18 years age group. The study shows the growth pattern of feet in North Indian adolescents and indicates that anthropometric measurements of the foot and its segments are valuable in estimation of stature in growing individuals of that population. Copyright © 2012 Elsevier Ltd. All rights reserved.

Novel Index (Hepatic Receptor: IHR) to Evaluate Hepatic Functional Reserve Using (99m)Tc-GSA Scintigraphy.

PubMed

Hasegawa, Daisuke; Onishi, Hideo; Matsutomo, Norikazu

2016-02-01

This study aimed to evaluate the novel index of hepatic receptor (IHR) on the regression analysis derived from time activity curve of the liver for hepatic functional reserve. Sixty patients had undergone (99m)Tc-galactosyl serum albumin ((99m)Tc-GSA) scintigraphy in the retrospective clinical study. Time activity curves for liver were obtained by region of interest (ROI) on the whole liver. A novel hepatic functional predictor was calculated with multiple regression analysis of time activity curves. In the multiple regression function, the objective variables were the indocyanine green (ICG) retention rate at 15 min, and the explanatory variables were the liver counts in 3-min intervals until end from beginning. Then, this result was defined by IHR, and we analyzed the correlation between IHR and ICG, uptake ratio of the heart at 15 minutes to that at 3 minutes (HH15), uptake ratio of the liver to the liver plus heart at 15 minutes (LHL15), and index of convexity (IOC). Regression function of IHR was derived as follows: IHR=0.025×L(6)-0.052×L(12)+0.027×L(27). The multiple regression analysis indicated that liver counts at 6 min, 12 min, and 27 min were significantly related to objective variables. The correlation coefficient between IHR and ICG was 0.774, and the correlation coefficient between ICG and conventional indices (HH15, LHL15, and IOC) were 0.837, 0.773, and 0.793, respectively. IHR had good correlation with HH15, LHL15, and IOC. The finding results suggested that IHR would provide clinical benefit for hepatic functional assessment in the (99m)Tc-GSA scintigraphy.

Which symptoms contribute the most to patients' perception of health in multiple sclerosis?

PubMed

Green, Rivka; Cutter, Gary; Friendly, Michael; Kister, Ilya

2017-01-01

Multiple sclerosis is a polysymptomatic disease. Little is known about relative contributions of the different multiple sclerosis symptoms to self-perception of health. To investigate the relationship between symptom severity in 11 domains affected by multiple sclerosis and self-rated health. Multiple sclerosis patients in two multiple sclerosis centers assessed self-rated health with a validated instrument and symptom burden with symptoMScreen, a validated battery of Likert scales for 11 domains commonly affected by multiple sclerosis. Pearson correlations and multivariate linear regressions were used to investigate the relationship between symptoMScreen scores and self-rated health. Among 1865 multiple sclerosis outpatients (68% women, 78% with relapsing-remitting multiple sclerosis, mean age 46.38 ± 12.47 years, disease duration 13.43 ± 10.04 years), average self-rated health score was 2.30 ('moderate to good'). Symptom burden (composite symptoMScreen score) highly correlated with self-rated health ( r  = 0.68, P  < 0.0001) as did each of the symptoMScreen domain subscores. In regression analysis, pain ( t  = 7.00), ambulation ( t  = 6.91), and fatigue ( t  = 5.85) contributed the highest amount of variance in self-rated health ( P  < 0.001). Pain contributed the most to multiple sclerosis outpatients' perception of health, followed by gait dysfunction and fatigue. These findings suggest that 'invisible disability' may be more important to patients' sense of wellbeing than physical disability, and challenge the notion that physical disability should be the primary outcome measure in multiple sclerosis.

Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression

PubMed Central

Dipnall, Joanna F.

2016-01-01

Background Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study. Methods The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009–2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators. Results After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (p<0.001). Conclusion The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and complex survey sampling methodology and was demonstrated to be a useful tool for detecting three biomarkers associated with depression for future hypothesis generation: red cell distribution width, serum glucose and total bilirubin. PMID:26848571

Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression.

PubMed

Dipnall, Joanna F; Pasco, Julie A; Berk, Michael; Williams, Lana J; Dodd, Seetal; Jacka, Felice N; Meyer, Denny

2016-01-01

Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study. The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009-2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators. After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (p<0.001). The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and complex survey sampling methodology and was demonstrated to be a useful tool for detecting three biomarkers associated with depression for future hypothesis generation: red cell distribution width, serum glucose and total bilirubin.

Partitioning sources of variation in vertebrate species richness

USGS Publications Warehouse

Boone, R.B.; Krohn, W.B.

2000-01-01

Aim: To explore biogeographic patterns of terrestrial vertebrates in Maine, USA using techniques that would describe local and spatial correlations with the environment. Location: Maine, USA. Methods: We delineated the ranges within Maine (86,156 km2) of 275 species using literature and expert review. Ranges were combined into species richness maps, and compared to geomorphology, climate, and woody plant distributions. Methods were adapted that compared richness of all vertebrate classes to each environmental correlate, rather than assessing a single explanatory theory. We partitioned variation in species richness into components using tree and multiple linear regression. Methods were used that allowed for useful comparisons between tree and linear regression results. For both methods we partitioned variation into broad-scale (spatially autocorrelated) and fine-scale (spatially uncorrelated) explained and unexplained components. By partitioning variance, and using both tree and linear regression in analyses, we explored the degree of variation in species richness for each vertebrate group that Could be explained by the relative contribution of each environmental variable. Results: In tree regression, climate variation explained richness better (92% of mean deviance explained for all species) than woody plant variation (87%) and geomorphology (86%). Reptiles were highly correlated with environmental variation (93%), followed by mammals, amphibians, and birds (each with 84-82% deviance explained). In multiple linear regression, climate was most closely associated with total vertebrate richness (78%), followed by woody plants (67%) and geomorphology (56%). Again, reptiles were closely correlated with the environment (95%), followed by mammals (73%), amphibians (63%) and birds (57%). Main conclusions: Comparing variation explained using tree and multiple linear regression quantified the importance of nonlinear relationships and local interactions between species richness and environmental variation, identifying the importance of linear relationships between reptiles and the environment, and nonlinear relationships between birds and woody plants, for example. Conservation planners should capture climatic variation in broad-scale designs; temperatures may shift during climate change, but the underlying correlations between the environment and species richness will presumably remain.

Estimation of streamflow, base flow, and nitrate-nitrogen loads in Iowa using multiple linear regression models

USGS Publications Warehouse

Schilling, K.E.; Wolter, C.F.

2005-01-01

Nineteen variables, including precipitation, soils and geology, land use, and basin morphologic characteristics, were evaluated to develop Iowa regression models to predict total streamflow (Q), base flow (Qb), storm flow (Qs) and base flow percentage (%Qb) in gauged and ungauged watersheds in the state. Discharge records from a set of 33 watersheds across the state for the 1980 to 2000 period were separated into Qb and Qs. Multiple linear regression found that 75.5 percent of long term average Q was explained by rainfall, sand content, and row crop percentage variables, whereas 88.5 percent of Qb was explained by these three variables plus permeability and floodplain area variables. Qs was explained by average rainfall and %Qb was a function of row crop percentage, permeability, and basin slope variables. Regional regression models developed for long term average Q and Qb were adapted to annual rainfall and showed good correlation between measured and predicted values. Combining the regression model for Q with an estimate of mean annual nitrate concentration, a map of potential nitrate loads in the state was produced. Results from this study have important implications for understanding geomorphic and land use controls on streamflow and base flow in Iowa watersheds and similar agriculture dominated watersheds in the glaciated Midwest. (JAWRA) (Copyright ?? 2005).

Forecasting Air Force Logistics Command Second Destination Transportation: An Application of Multiple Regression Analysis and Neural Networks

DTIC Science & Technology

1990-09-01

without the help from the DSXR staff. William Lyons, Charles Ramsey , and Martin Meeks went above and beyond to help complete this research. Special...develop a valid forecasting model that is significantly more accurate than the one presently used by DSXR and suggested the development and testing of a...method, Strom tested DSXR’s iterative linear regression forecasting technique by examining P1 in the simple regression equation to determine whether

[Perceived quality of life in the "healthy people" municipal community health promotion program in Ciudad Lineal-Madrid].

PubMed

Esteban-Peña, M; Gerechter-Fernández, S; Martínez-Simancas, A M; Zancada-González, J; Hernandez-Barrera, V; Jiménez-García, R

The measurement of the health-related quality of life (HRQoL) is a comprehensive way to estimate the health of both the individual and the community. The aim of this study was to assess changes in health and HRQoL using the intervention strategies «healthy people» and «health promotion programs» of the City Council of Madrid-Madrid Health. The study was conducted in the Municipal Health Centre of Ciudad Lineal, in 2014, and included promotion of exercise, healthy eating, smoking cessation, psycho-hygiene, memory training, and health education for the elderly, with group interventions. A before and after community trial, with the administration of questionnaire with COOP/WONCA HRQoL charts to 200 participants. The study population (n=87), included those who completed the first and second questionnaire. Positive changes were seen in the overall before and after COOP/WONCA scores, with a mean change from 23.16 to 21.94, with statistical significance, p≤0.002. In the psychology groups, it changed from 28.14 to 23.57 with a p≤0.05, and healthy eating from 22.81 to 20.85, with p≤0.03. In the health education groups it changed from 21 to 20.81 points, and in memory training from 23.31 to 22.45 points (both without significance). The significant improvement in scores reflects a positive change in self-perceived health of this community after the intervention and proper operation of programs. The areas directly related to health and interventions in psychology and nutrition-exercise, are those with the most significant changes. Copyright © 2016 Sociedad Española de Médicos de Atención Primaria (SEMERGEN). Publicado por Elsevier España, S.L.U. All rights reserved.

Determination of oligomers in virgin and recycled polyethylene terephthalate (PET) samples by UPLC-MS-QTOF.

PubMed

Ubeda, Sara; Aznar, Margarita; Nerín, Cristina

2018-03-01

An oligomer is a molecule that consists of a few monomer units. It can be formed during polymer manufacturing and also due to polymer degradation processes or even during use conditions. Since oligomers are not included in chemical databases, their identification is a complex process. In this work, the oligomers present in 20 different PET pellet samples have been determined. Two different sample treatment procedures, solvent extraction and total dissolution, were applied in order to select the most efficient one. The analyses were carried out by UPLC-MS-QTOF. The use of high resolution mass spectrometry allowed the structural elucidation of these compounds and their correct identification. The main oligomers identified were cyclic as well as lineal from the first, second, and third series. All of them were composed of terephthalic acid (TPA), diethylene glycol (DEG), and ethylene glycol (EG). Quantitative values were very different in both procedures. In total dissolution of PET samples, the concentration of oligomers was always, at least, 10 times higher than in solvent extraction; some of the compounds were only detected when total dissolution was used. Results showed that the oligomers with the highest concentration values were dimers and trimers, cyclic, as well as lineal, from the first and second series. The oligomer with the maximum concentration value was TPA 2 -EG-DEG that was found in all the samples in a concentration range from 2493 to 19,290 ng/g PET. No differences between virgin and recycled PET were found. Migration experiments were performed in two PET bottles, and results showed the transference of most of these oligomers to a fat food simulant (ethanol 95%). Graphical abstract Graphical abstract of the two procedures developd and optimized for identifying oligomers in PET pellets and in migration form PET bottles.

A novel variational Bayes multiple locus Z-statistic for genome-wide association studies with Bayesian model averaging

PubMed Central

Logsdon, Benjamin A.; Carty, Cara L.; Reiner, Alexander P.; Dai, James Y.; Kooperberg, Charles

2012-01-01

Motivation: For many complex traits, including height, the majority of variants identified by genome-wide association studies (GWAS) have small effects, leaving a significant proportion of the heritable variation unexplained. Although many penalized multiple regression methodologies have been proposed to increase the power to detect associations for complex genetic architectures, they generally lack mechanisms for false-positive control and diagnostics for model over-fitting. Our methodology is the first penalized multiple regression approach that explicitly controls Type I error rates and provide model over-fitting diagnostics through a novel normally distributed statistic defined for every marker within the GWAS, based on results from a variational Bayes spike regression algorithm. Results: We compare the performance of our method to the lasso and single marker analysis on simulated data and demonstrate that our approach has superior performance in terms of power and Type I error control. In addition, using the Women's Health Initiative (WHI) SNP Health Association Resource (SHARe) GWAS of African-Americans, we show that our method has power to detect additional novel associations with body height. These findings replicate by reaching a stringent cutoff of marginal association in a larger cohort. Availability: An R-package, including an implementation of our variational Bayes spike regression (vBsr) algorithm, is available at http://kooperberg.fhcrc.org/soft.html. Contact: blogsdon@fhcrc.org Supplementary information: Supplementary data are available at Bioinformatics online. PMID:22563072

Statistical research using the multiple regression analysis in areas of the cast hipereutectoid steel rolls manufacturing

NASA Astrophysics Data System (ADS)

Kiss, I.; Alexa, V.; Serban, S.; Rackov, M.; Čavić, M.

2018-01-01

The cast hipereutectoid steel (usually named Adamite) is a roll manufacturing destined material, having mechanical, chemical properties and Carbon [C] content of which stands between steelandiron, along-withitsalloyelements such as Nickel [Ni], Chrome [Cr], Molybdenum [Mo] and/or other alloy elements. Adamite Rolls are basically alloy steel rolls (a kind of high carbon steel) having hardness ranging from 40 to 55 degrees Shore C, with Carbon [C] percentage ranging from 1.35% until to 2% (usually between 1.2˜2.3%), the extra Carbon [C] and the special alloying element giving an extra wear resistance and strength. First of all the Adamite roll’s prominent feature is the small variation in hardness of the working surface, and has a good abrasion resistance and bite performance. This paper reviews key aspects of roll material properties and presents an analysis of the influences of chemical composition upon the mechanical properties (hardness) of the cast hipereutectoid steel rolls (Adamite). Using the multiple regression analysis (the double and triple regression equations), some mathematical correlations between the cast hipereutectoid steel rolls’ chemical composition and the obtained hardness are presented. In this work several results and evidence obtained by actual experiments are presented. Thus, several variation boundaries for the chemical composition of cast hipereutectoid steel rolls, in view the obtaining the proper values of the hardness, are revealed. For the multiple regression equations, correlation coefficients and graphical representations the software Matlab was used.

Probability of Corporal Punishment: Lack of Resources and Vulnerable Students

ERIC Educational Resources Information Center

Han, Seunghee

2011-01-01

The author examined corporal punishment practices in the United States based on data from 362 public school principals where corporal punishment is available. Results from multiple regression analyses show that schools with multiple student violence prevention programs and teacher training programs had fewer possibilities of use corporal…

Classification and regression tree analysis vs. multivariable linear and logistic regression methods as statistical tools for studying haemophilia.

PubMed

Henrard, S; Speybroeck, N; Hermans, C

2015-11-01

Haemophilia is a rare genetic haemorrhagic disease characterized by partial or complete deficiency of coagulation factor VIII, for haemophilia A, or IX, for haemophilia B. As in any other medical research domain, the field of haemophilia research is increasingly concerned with finding factors associated with binary or continuous outcomes through multivariable models. Traditional models include multiple logistic regressions, for binary outcomes, and multiple linear regressions for continuous outcomes. Yet these regression models are at times difficult to implement, especially for non-statisticians, and can be difficult to interpret. The present paper sought to didactically explain how, why, and when to use classification and regression tree (CART) analysis for haemophilia research. The CART method is non-parametric and non-linear, based on the repeated partitioning of a sample into subgroups based on a certain criterion. Breiman developed this method in 1984. Classification trees (CTs) are used to analyse categorical outcomes and regression trees (RTs) to analyse continuous ones. The CART methodology has become increasingly popular in the medical field, yet only a few examples of studies using this methodology specifically in haemophilia have to date been published. Two examples using CART analysis and previously published in this field are didactically explained in details. There is increasing interest in using CART analysis in the health domain, primarily due to its ease of implementation, use, and interpretation, thus facilitating medical decision-making. This method should be promoted for analysing continuous or categorical outcomes in haemophilia, when applicable. © 2015 John Wiley & Sons Ltd.

Evaluation and prediction of shrub cover in coastal Oregon forests (USA)

Treesearch

Becky K. Kerns; Janet L. Ohmann

2004-01-01

We used data from regional forest inventories and research programs, coupled with mapped climatic and topographic information, to explore relationships and develop multiple linear regression (MLR) and regression tree models for total and deciduous shrub cover in the Oregon coastal province. Results from both types of models indicate that forest structure variables were...

A Factor Analytic and Regression Approach to Functional Age: Potential Effects of Race.

ERIC Educational Resources Information Center

Colquitt, Alan L.; And Others

Factor analysis and multiple regression are two major approaches used to look at functional age, which takes account of the extensive variation in the rate of physiological and psychological maturation throughout life. To examine the role of racial or cultural influences on the measurement of functional age, a battery of 12 tests concentrating on…

Development and evaluation of habitat models for herpetofauna and small mammals

Treesearch

William M. Block; Michael L. Morrison; Peter E. Scott

1998-01-01

We evaluated the ability of discriminant analysis (DA), logistic regression (LR), and multiple regression (MR) to describe habitat use by amphibians, reptiles, and small mammals found in California oak woodlands. We also compared models derived from pitfall and live trapping data for several species. Habitat relations modeled by DA and LR produced similar results,...

Fiber length - fiber strength interrelationship for slash pine and its effect on pulp-sheet properties

Treesearch

F.F. Wangaard; George E. Woodson

1972-01-01

Based on a model developed for hardwood fiber strength-pulp property relationships, multiple-regression equations involving fiber strength, fiber length, and sheet density were determined to predict the properties of kraft pulps of slash pine (Pinus elliottii). Regressions for breaking length and burst factor accounted for 88 and 90 percent,...

Fiber length strength interrelationship for slash pine and its effect on pulp-sheet properties

Treesearch

F. G. Wangaard; G. E. Woodson

1973-01-01

Based on a model developed for hardwood fiber strength-pulp property relationships, multiple-regression equations involving fiber strength, fiber length, and sheet density were determined to predict the properties of kraft pulps of slash pine (Pinus elliottii). Regressions for breaking length and burst factor accounted for 88 and 90 percent,...

Anticipating Mathematics Performance: A Cross-Validation Comparison of AID3 and Regression. AIR 1988 Annual Forum Paper.

ERIC Educational Resources Information Center

Bloom, Allan M.; And Others

In response to the increasing importance of student performance in required classes, research was conducted to compare two prediction procedures, linear modeling using multiple regression and nonlinear modeling using AID3. Performance in the first college math course (College Mathematics, Calculus, or Business Calculus Matrices) was the dependent…

5 CFR 591.219 - How does OPM compute shelter price indexes?

Code of Federal Regulations, 2010 CFR

2010-01-01

... estimates in hedonic regressions (a type of multiple regression) to compute for each COLA survey area the... and rental equivalence prices and/or estimates, OPM obtains for each unit surveyed information about... survey area and the Washington, DC, area. [67 FR 22340, May 3, 2002, as amended at 69 FR 59763, Oct. 6...

Multiple linear regression analysis

NASA Technical Reports Server (NTRS)

Edwards, T. R.

1980-01-01

Program rapidly selects best-suited set of coefficients. User supplies only vectors of independent and dependent data and specifies confidence level required. Program uses stepwise statistical procedure for relating minimal set of variables to set of observations; final regression contains only most statistically significant coefficients. Program is written in FORTRAN IV for batch execution and has been implemented on NOVA 1200.

Cactus: An Introduction to Regression

ERIC Educational Resources Information Center

Hyde, Hartley

2008-01-01

When the author first used "VisiCalc," the author thought it a very useful tool when he had the formulas. But how could he design a spreadsheet if there was no known formula for the quantities he was trying to predict? A few months later, the author relates he learned to use multiple linear regression software and suddenly it all clicked into…

Multiple Regression with Varying Levels of Correlation among Predictors: Monte Carlo Sampling from Normal and Non-Normal Populations.

ERIC Educational Resources Information Center

Vasu, Ellen Storey

1978-01-01

The effects of the violation of the assumption of normality in the conditional distributions of the dependent variable, coupled with the condition of multicollinearity upon the outcome of testing the hypothesis that the regression coefficient equals zero, are investigated via a Monte Carlo study. (Author/JKS)

A Ten Year Study of Salary Differential by Sex through a Regression Methodology.

ERIC Educational Resources Information Center

Williams, John Delane; And Others

A 10-year study of salary differential by sex was undertaken at the University of North Dakota using a multiple regression methodology, with rank, discipline, degree, years in department, years in current rank, and sex as predictors. The sex variable evidenced lower salaries for women when controlling for the other variables throughout the study…

Using destination image to predict visitors' intention to revisit three Hudson River Valley, New York, communities

Treesearch

Rudy M. Schuster; Laura Sullivan; Duarte Morais; Diane Kuehn

2009-01-01

This analysis explores the differences in Affective and Cognitive Destination Image among three Hudson River Valley (New York) tourism communities. Multiple regressions were used with six dimensions of visitors' images to predict future intention to revisit. Two of the three regression models were significant. The only significantly contributing independent...

A New Sample Size Formula for Regression.

ERIC Educational Resources Information Center

Brooks, Gordon P.; Barcikowski, Robert S.

The focus of this research was to determine the efficacy of a new method of selecting sample sizes for multiple linear regression. A Monte Carlo simulation was used to study both empirical predictive power rates and empirical statistical power rates of the new method and seven other methods: those of C. N. Park and A. L. Dudycha (1974); J. Cohen…

Default Bayes Factors for Model Selection in Regression

ERIC Educational Resources Information Center

Rouder, Jeffrey N.; Morey, Richard D.

2012-01-01

In this article, we present a Bayes factor solution for inference in multiple regression. Bayes factors are principled measures of the relative evidence from data for various models or positions, including models that embed null hypotheses. In this regard, they may be used to state positive evidence for a lack of an effect, which is not possible…

Assessment of Risk Factors of Intrauterine Adhesions in Patients With Induced Abortion and the Curative Effect of Hysteroscopic Surgery.

PubMed

Mo, Xiaoliang; Qin, Guirong; Zhou, Zhoulin; Jiang, Xiaoli

2017-10-03

To explore the risk factors for intrauterine adhesions in patients with artificial abortion and clinical efficacy of hysteroscopic dissection. 1500 patients undergoing artificial abortion between January 2014 and June 2015 were enrolled into this study. The patients were divided into two groups with or without intrauterine adhesions. Univariate and Multiple logistic regression were conducted to assess the effects of multiple factors on the development of intrauterine adhesions following induced abortion. The incidence rate for intrauterine adhesions following induced abortion is 17.0%. Univariate showed that preoperative inflammation, multiple pregnancies and suction evacuation time are the influence risk factors of intrauterine adhesions. Multiple logistic regression demonstrates that multiple pregnancies, high intrauterine negative pressure, and long suction evacuation time are independent risk factors for the development of intrauterine adhesions following induced abortion. Additionally, intrauterine adhesions were observed in 105 mild, 80 moderate, and 70 severe cases. The cure rates for these three categories of intrauterine adhesions by hysteroscopic surgery were 100.0%, 93.8%, and 85.7%, respectively. Multiple pregnancies, high negative pressure suction evacuation and long suction evacuation time are independent risk factors for the development of intrauterine adhesions following induced abortions. Hysteroscopic surgery substantially improves the clinical outcomes of intrauterine adhesions.

Using an innovative multiple regression procedure in a cancer population (Part 1): detecting and probing relationships of common interacting symptoms (pain, fatigue/weakness, sleep problems) as a strategy to discover influential symptom pairs and clusters

PubMed Central

Francoeur, Richard B

2015-01-01

Background The majority of patients with advanced cancer experience symptom pairs or clusters among pain, fatigue, and insomnia. Improved methods are needed to detect and interpret interactions among symptoms or diesease markers to reveal influential pairs or clusters. In prior work, I developed and validated sequential residual centering (SRC), a method that improves the sensitivity of multiple regression to detect interactions among predictors, by conditioning for multicollinearity (shared variation) among interactions and component predictors. Materials and methods Using a hypothetical three-way interaction among pain, fatigue, and sleep to predict depressive affect, I derive and explain SRC multiple regression. Subsequently, I estimate raw and SRC multiple regressions using real data for these symptoms from 268 palliative radiation outpatients. Results Unlike raw regression, SRC reveals that the three-way interaction (pain × fatigue/weakness × sleep problems) is statistically significant. In follow-up analyses, the relationship between pain and depressive affect is aggravated (magnified) within two partial ranges: 1) complete-to-some control over fatigue/weakness when there is complete control over sleep problems (ie, a subset of the pain–fatigue/weakness symptom pair), and 2) no control over fatigue/weakness when there is some-to-no control over sleep problems (ie, a subset of the pain–fatigue/weakness–sleep problems symptom cluster). Otherwise, the relationship weakens (buffering) as control over fatigue/weakness or sleep problems diminishes. Conclusion By reducing the standard error, SRC unmasks a three-way interaction comprising a symptom pair and cluster. Low-to-moderate levels of the moderator variable for fatigue/weakness magnify the relationship between pain and depressive affect. However, when the comoderator variable for sleep problems accompanies fatigue/weakness, only frequent or unrelenting levels of both symptoms magnify the relationship. These findings suggest that a countervailing mechanism involving depressive affect could account for the effectiveness of a cognitive behavioral intervention to reduce the severity of a pain, fatigue, and sleep disturbance cluster in a previous randomized trial. PMID:25565865

Using an innovative multiple regression procedure in a cancer population (Part 1): detecting and probing relationships of common interacting symptoms (pain, fatigue/weakness, sleep problems) as a strategy to discover influential symptom pairs and clusters.

PubMed

Francoeur, Richard B

2015-01-01

The majority of patients with advanced cancer experience symptom pairs or clusters among pain, fatigue, and insomnia. Improved methods are needed to detect and interpret interactions among symptoms or diesease markers to reveal influential pairs or clusters. In prior work, I developed and validated sequential residual centering (SRC), a method that improves the sensitivity of multiple regression to detect interactions among predictors, by conditioning for multicollinearity (shared variation) among interactions and component predictors. Using a hypothetical three-way interaction among pain, fatigue, and sleep to predict depressive affect, I derive and explain SRC multiple regression. Subsequently, I estimate raw and SRC multiple regressions using real data for these symptoms from 268 palliative radiation outpatients. Unlike raw regression, SRC reveals that the three-way interaction (pain × fatigue/weakness × sleep problems) is statistically significant. In follow-up analyses, the relationship between pain and depressive affect is aggravated (magnified) within two partial ranges: 1) complete-to-some control over fatigue/weakness when there is complete control over sleep problems (ie, a subset of the pain-fatigue/weakness symptom pair), and 2) no control over fatigue/weakness when there is some-to-no control over sleep problems (ie, a subset of the pain-fatigue/weakness-sleep problems symptom cluster). Otherwise, the relationship weakens (buffering) as control over fatigue/weakness or sleep problems diminishes. By reducing the standard error, SRC unmasks a three-way interaction comprising a symptom pair and cluster. Low-to-moderate levels of the moderator variable for fatigue/weakness magnify the relationship between pain and depressive affect. However, when the comoderator variable for sleep problems accompanies fatigue/weakness, only frequent or unrelenting levels of both symptoms magnify the relationship. These findings suggest that a countervailing mechanism involving depressive affect could account for the effectiveness of a cognitive behavioral intervention to reduce the severity of a pain, fatigue, and sleep disturbance cluster in a previous randomized trial.

Do clinical data and human papilloma virus genotype influence spontaneous regression in grade I cervical intraepithelial neoplasia?

PubMed

Cortés-Alaguero, Caterina; González-Mirasol, Esteban; Morales-Roselló, José; Poblet-Martinez, Enrique

2017-03-15

To determine whether medical history, clinical examination and human papilloma virus (HPV) genotype influence spontaneous regression in cervical intraepithelial neoplasia grade I (CIN-I). We retrospectively evaluated 232 women who were histologically diagnosed as have CIN-I by means of Kaplan-Meier curves, the pattern of spontaneous regression according to the medical history, clinical examination, and HPV genotype. Spontaneous regression occurred in most patients and was influenced by the presence of multiple HPV genotypes but not by the HPV genotype itself. In addition, regression frequency was diminished when more than 50% of the cervix surface was affected or when an abnormal cytology was present at the beginning of follow-up. The frequency of regression in CIN-I is high, making long-term follow-up and conservative management advisable. Data from clinical examination and HPV genotyping might help to anticipate which lesions will regress.

The effects of multiple interpersonal traumas on psychological maladjustment of sexually abused children in Korea.

PubMed

Choi, Ji Young; Oh, Kyung Ja

2013-02-01

The purpose of the present study was to explore the effects of multiple interpersonal traumas on psychiatric diagnosis and behavior problems of sexually abused children in Korea. With 495 children (ages 4-13 years) referred to a public counseling center for sexual abuse in Korea, we found significant differences in the rate of psychiatric diagnoses (r = .23) and severity of behavioral problems (internalizing d = 0.49, externalizing d = 0.40, total d = 0.52) between children who were victims of sexual abuse only (n = 362) and youth who were victims of interpersonal trauma experiences in addition to sexual abuse (n = 133). The effects of multiple interpersonal trauma experiences on single versus multiple diagnoses remained significant in the logistic regression analysis where demographic variables, family environmental factors, sexual abuse characteristics, and postincident factors were considered together, odds ratio (OR) = 0.44, 95% confidence interval (CI) = [0.25, 0.77], p < .01. Similarly, multiple regression analyses revealed a significant effect of multiple interpersonal trauma experiences on severity of behavioral problems above and beyond all aforementioned variables (internalizing β =.12, p = .019, externalizing β = .11, p = .036, total β = .14, p =.008). The results suggested that children with multiple interpersonal traumas are clearly at a greater risk for negative consequences following sexual abuse. Copyright © 2013 International Society for Traumatic Stress Studies.

Construction of mathematical model for measuring material concentration by colorimetric method

NASA Astrophysics Data System (ADS)

Liu, Bing; Gao, Lingceng; Yu, Kairong; Tan, Xianghua

2018-06-01

This paper use the method of multiple linear regression to discuss the data of C problem of mathematical modeling in 2017. First, we have established a regression model for the concentration of 5 substances. But only the regression model of the substance concentration of urea in milk can pass through the significance test. The regression model established by the second sets of data can pass the significance test. But this model exists serious multicollinearity. We have improved the model by principal component analysis. The improved model is used to control the system so that it is possible to measure the concentration of material by direct colorimetric method.

On using summary statistics from an external calibration sample to correct for covariate measurement error.

PubMed

Guo, Ying; Little, Roderick J; McConnell, Daniel S

2012-01-01

Covariate measurement error is common in epidemiologic studies. Current methods for correcting measurement error with information from external calibration samples are insufficient to provide valid adjusted inferences. We consider the problem of estimating the regression of an outcome Y on covariates X and Z, where Y and Z are observed, X is unobserved, but a variable W that measures X with error is observed. Information about measurement error is provided in an external calibration sample where data on X and W (but not Y and Z) are recorded. We describe a method that uses summary statistics from the calibration sample to create multiple imputations of the missing values of X in the regression sample, so that the regression coefficients of Y on X and Z and associated standard errors can be estimated using simple multiple imputation combining rules, yielding valid statistical inferences under the assumption of a multivariate normal distribution. The proposed method is shown by simulation to provide better inferences than existing methods, namely the naive method, classical calibration, and regression calibration, particularly for correction for bias and achieving nominal confidence levels. We also illustrate our method with an example using linear regression to examine the relation between serum reproductive hormone concentrations and bone mineral density loss in midlife women in the Michigan Bone Health and Metabolism Study. Existing methods fail to adjust appropriately for bias due to measurement error in the regression setting, particularly when measurement error is substantial. The proposed method corrects this deficiency.

Cox regression analysis with missing covariates via nonparametric multiple imputation.

PubMed

Hsu, Chiu-Hsieh; Yu, Mandi

2018-01-01

We consider the situation of estimating Cox regression in which some covariates are subject to missing, and there exists additional information (including observed event time, censoring indicator and fully observed covariates) which may be predictive of the missing covariates. We propose to use two working regression models: one for predicting the missing covariates and the other for predicting the missing probabilities. For each missing covariate observation, these two working models are used to define a nearest neighbor imputing set. This set is then used to non-parametrically impute covariate values for the missing observation. Upon the completion of imputation, Cox regression is performed on the multiply imputed datasets to estimate the regression coefficients. In a simulation study, we compare the nonparametric multiple imputation approach with the augmented inverse probability weighted (AIPW) method, which directly incorporates the two working models into estimation of Cox regression, and the predictive mean matching imputation (PMM) method. We show that all approaches can reduce bias due to non-ignorable missing mechanism. The proposed nonparametric imputation method is robust to mis-specification of either one of the two working models and robust to mis-specification of the link function of the two working models. In contrast, the PMM method is sensitive to misspecification of the covariates included in imputation. The AIPW method is sensitive to the selection probability. We apply the approaches to a breast cancer dataset from Surveillance, Epidemiology and End Results (SEER) Program.

The multiple imputation method: a case study involving secondary data analysis.

PubMed

Walani, Salimah R; Cleland, Charles M

2015-05-01

To illustrate with the example of a secondary data analysis study the use of the multiple imputation method to replace missing data. Most large public datasets have missing data, which need to be handled by researchers conducting secondary data analysis studies. Multiple imputation is a technique widely used to replace missing values while preserving the sample size and sampling variability of the data. The 2004 National Sample Survey of Registered Nurses. The authors created a model to impute missing values using the chained equation method. They used imputation diagnostics procedures and conducted regression analysis of imputed data to determine the differences between the log hourly wages of internationally educated and US-educated registered nurses. The authors used multiple imputation procedures to replace missing values in a large dataset with 29,059 observations. Five multiple imputed datasets were created. Imputation diagnostics using time series and density plots showed that imputation was successful. The authors also present an example of the use of multiple imputed datasets to conduct regression analysis to answer a substantive research question. Multiple imputation is a powerful technique for imputing missing values in large datasets while preserving the sample size and variance of the data. Even though the chained equation method involves complex statistical computations, recent innovations in software and computation have made it possible for researchers to conduct this technique on large datasets. The authors recommend nurse researchers use multiple imputation methods for handling missing data to improve the statistical power and external validity of their studies.

Penalized nonparametric scalar-on-function regression via principal coordinates

PubMed Central

Reiss, Philip T.; Miller, David L.; Wu, Pei-Shien; Hua, Wen-Yu

2016-01-01

A number of classical approaches to nonparametric regression have recently been extended to the case of functional predictors. This paper introduces a new method of this type, which extends intermediate-rank penalized smoothing to scalar-on-function regression. In the proposed method, which we call principal coordinate ridge regression, one regresses the response on leading principal coordinates defined by a relevant distance among the functional predictors, while applying a ridge penalty. Our publicly available implementation, based on generalized additive modeling software, allows for fast optimal tuning parameter selection and for extensions to multiple functional predictors, exponential family-valued responses, and mixed-effects models. In an application to signature verification data, principal coordinate ridge regression, with dynamic time warping distance used to define the principal coordinates, is shown to outperform a functional generalized linear model. PMID:29217963

Staging memory for massively parallel processor

NASA Technical Reports Server (NTRS)

Batcher, Kenneth E. (Inventor)

1988-01-01

The invention herein relates to a computer organization capable of rapidly processing extremely large volumes of data. A staging memory is provided having a main stager portion consisting of a large number of memory banks which are accessed in parallel to receive, store, and transfer data words simultaneous with each other. Substager portions interconnect with the main stager portion to match input and output data formats with the data format of the main stager portion. An address generator is coded for accessing the data banks for receiving or transferring the appropriate words. Input and output permutation networks arrange the lineal order of data into and out of the memory banks.

Process Of Bonding Copper And Tungsten

DOEpatents

Slattery, Kevin T.; Driemeyer, Daniel E.; Davis, John W.

2000-07-18

Process for bonding a copper substrate to a tungsten substrate by providing a thin metallic adhesion promoting film bonded to a tungsten substrate and a functionally graded material (FGM) interlayer bonding the thin metallic adhesion promoting film to the copper substrate. The FGM interlayer is formed by sintering a stack of individual copper and tungsten powder blend layers having progressively higher copper content/tungsten content, by volume, ratio values in successive powder blend layers in a lineal direction extending from the tungsten substrate towards the copper substrate. The resulting copper to tungsten joint well accommodates the difference in the coefficient of thermal expansion of the materials.

Pearls in the ribbed mussel Aulacomya atra caused by polydorin polychaetes (Spionidae) infestation.

PubMed

Diez, M E; Vázquez, N; Cremonte, F

2016-10-01

Aulacomya atra populations of the San Jose gulf, Northern Patagonia, Southwestern Atlantic Ocean, are infested by two polydorin species, Polydora rickettsi and Dipolydora cf. giardi. The infestation by these boring polychaetes causes the formation of pearls which is evidenced by the presence of capsules containing polydorin tissue debris and the elemental composition of organic material inside the pearls. Moreover, a positive relationship between the abundance of perforations of polydorin polychaetes and abundance of pearls was found by applying generalized lineal model analysis. These results constitute the first evidence of pearls formation due to infestation by polychaete. Copyright © 2016 Elsevier Inc. All rights reserved.

Soluciones analiticas AL problema de jets con velocidad de eyeccion variable EN EL tiempo.

NASA Astrophysics Data System (ADS)

Canto, J.; Raga, A. C.; D'Alessio, P.

1998-11-01

Se presenta un nuevo metodo que permite resolver de manera exacta y analitica las ecuaciones que describen un jet hipersonico con velocidad de eyeccion variable en el tiempo. El metodo se basa en consideraciones sencillas de conservacion de momento para las superficies de trabajo que se forman en el interior del jet. Como ejemplo, se presentan soluciones para jets con variacion sinusoidal en la velocidad de eyeccion, y tambien para el caso de un incremento lineal en el tiempo. Estas soluciones analiticas tienen una clara aplicacion en la interpretacion de las observaciones de jets asociados a objetos Herbig-Haro.

Primary Factors Related to Multiple Placements for Children in Out-of-Home Care

ERIC Educational Resources Information Center

Eggertsen, Lars

2008-01-01

Using an ecological framework, this study identified which factors related to out-of-home placements significantly influenced multiple placements for children in Utah during 2000, 2001, and 2002. Multinomial logistic regression statistical procedures and a geographical information system (GIS) were used to analyze the data. The final model…

Source Evaluation, Comprehension, and Learning in Internet Science Inquiry Tasks

ERIC Educational Resources Information Center

Wiley, Jennifer; Goldman, Susan R.; Graesser, Arthur C.; Sanchez, Christopher A.; Ash, Ivan K.; Hemmerich, Joshua A.

2009-01-01

In two experiments, undergraduates' evaluation and use of multiple Internet sources during a science inquiry task were examined. In Experiment 1, undergraduates had the task of explaining what caused the eruption of Mt. St. Helens using the results of an Internet search. Multiple regression analyses indicated that source evaluation significantly…

Probing for the Multiplicative Term in Modern Expectancy-Value Theory: A Latent Interaction Modeling Study

ERIC Educational Resources Information Center

Trautwein, Ulrich; Marsh, Herbert W.; Nagengast, Benjamin; Ludtke, Oliver; Nagy, Gabriel; Jonkmann, Kathrin

2012-01-01

In modern expectancy-value theory (EVT) in educational psychology, expectancy and value beliefs additively predict performance, persistence, and task choice. In contrast to earlier formulations of EVT, the multiplicative term Expectancy x Value in regression-type models typically plays no major role in educational psychology. The present study…

Normalization Ridge Regression in Practice II: The Estimation of Multiple Feedback Linkages.

ERIC Educational Resources Information Center

Bulcock, J. W.

The use of the two-stage least squares (2 SLS) procedure for estimating nonrecursive social science models is often impractical when multiple feedback linkages are required. This is because 2 SLS is extremely sensitive to multicollinearity. The standard statistical solution to the multicollinearity problem is a biased, variance reduced procedure…

Effects of Verbal and Written Performance Feedback on Treatment Adherence: Practical Application of Two Delivery Formats

ERIC Educational Resources Information Center

Kaufman, Dahlia; Codding, Robin S.; Markus, Keith A.; Tryon, Georgiana Shick; Kyse, Eden Nagler

2013-01-01

Verbal and written performance feedback for improving preschool and kindergarten teachers' treatment integrity of behavior plans was compared using a combined multiple-baseline and multiple-treatment design across teacher-student dyads with order counterbalanced as within-series conditions. Supplemental generalized least square regression analyses…

Novel Applications of Multi-task Learning and Multiple Output Regression to Multiple Genetic Trait Prediction

USDA-ARS?s Scientific Manuscript database

Given a set of biallelic molecular markers, such as SNPs, with genotype values encoded numerically on a collection of plant, animal or human samples, the goal of genetic trait prediction is to predict the quantitative trait values by simultaneously modeling all marker effects. Genetic trait predicti...

The Effectiveness of Using a Multiple Gating Approach to Discriminate among ADHD Subtypes

ERIC Educational Resources Information Center

Simonsen, Brandi M.; Bullis, Michael D.

2007-01-01

This study explored the ability of Systematically Progressive Assessment (SPA), a multiple gating approach for assessing students with attention-deficit/hyperactivity disorder (ADHD), to discriminate between subtypes of ADHD. A total of 48 students with ADHD (ages 6-11) were evaluated with three "gates" of assessment. Logistic regression analysis…

Multifocal Epithelial Hyperplasia.

PubMed

Agnew, Caitlin; Alexander, Sherene; Prabhu, Neeta

2017-01-15

Multifocal epithelial hyperplasia is a rare disease associated with human papilloma virus types 13 and 32. Diagnosis is based on clinical and histopathological findings, and most lesions are asymptomatic and regress spontaneously with time. The purpose of this paper is to describe a five-year-old girl who presented with multiple intraoral lesions on the buccal mucosa and tongue, which regressed spontaneously in 15 months.

Emotional Reactions to Stress among Adolescent Boys and Girls: An Examination of the Mediating Mechanisms Proposed by General Strain Theory

ERIC Educational Resources Information Center

Sigfusdottir, Inga-Dora; Silver, Eric

2009-01-01

This study examines the effects of negative life events on anger and depressed mood among a sample of 7,758 Icelandic adolescents, measured as part of the National Survey of Icelandic Adolescents (Thorlindsson, Sigfusdottir, Bernburg, & Halldorsson, 1998). Using multiple linear regression and multinomial logit regression, we find that (a)…

Simultaneous spectrophotometric determination of salbutamol and bromhexine in tablets.

PubMed

Habib, I H I; Hassouna, M E M; Zaki, G A

2005-03-01

Typical anti-mucolytic drugs called salbutamol hydrochloride and bromhexine sulfate encountered in tablets were determined simultaneously either by using linear regression at zero-crossing wavelengths of the first derivation of UV-spectra or by application of multiple linear partial least squares regression method. The results obtained by the two proposed mathematical methods were compared with those obtained by the HPLC technique.

The Relative Contributions of the Cattell-Horn-Carroll Cognitive Abilities in Explaining Writing Achievement during Childhood and Adolescence

ERIC Educational Resources Information Center

Floyd, Randy G.; McGrew, Kevin S.; Evans, Jeffrey J.

2008-01-01

This study examined the relative contributions of measures of Cattell-Horn-Carroll (CHC) cognitive abilities in explaining writing achievement. Drawing from samples that covered the age range of 7 to 18 years, simultaneous multiple regression was used to regress scores from the Woodcock-Johnson III (WJ III; Woodcock, McGrew, & Mather, 2001) that…

A Generalized Logistic Regression Procedure to Detect Differential Item Functioning among Multiple Groups

ERIC Educational Resources Information Center

Magis, David; Raiche, Gilles; Beland, Sebastien; Gerard, Paul

2011-01-01

We present an extension of the logistic regression procedure to identify dichotomous differential item functioning (DIF) in the presence of more than two groups of respondents. Starting from the usual framework of a single focal group, we propose a general approach to estimate the item response functions in each group and to test for the presence…

Job Satisfaction of Female and Male Superintendents: The Influence of Job Facets and Contextual Variables as Potential Predictors

ERIC Educational Resources Information Center

Young, I. Phillip; Kowalski, Theodore J.; McCord, Robert S.; Petersen, George J.

2012-01-01

A descriptive multiple regression approach was used to assess the job satisfaction of female and male public school superintendents taking part in a decennial survey conducted by AASA. Self-reported job satisfaction of public school superintendents was regressed on their affective reactions to specific job facets (supervision, co-workers, and…

A Quantile Regression Approach to Understanding the Relations among Morphological Awareness, Vocabulary, and Reading Comprehension in Adult Basic Education Students

ERIC Educational Resources Information Center

Tighe, Elizabeth L.; Schatschneider, Christopher

2016-01-01

The purpose of this study was to investigate the joint and unique contributions of morphological awareness and vocabulary knowledge at five reading comprehension levels in adult basic education (ABE) students. We introduce the statistical technique of multiple quantile regression, which enabled us to assess the predictive utility of morphological…

High-throughput quantitative biochemical characterization of algal biomass by NIR spectroscopy; multiple linear regression and multivariate linear regression analysis.

PubMed

Laurens, L M L; Wolfrum, E J

2013-12-18

One of the challenges associated with microalgal biomass characterization and the comparison of microalgal strains and conversion processes is the rapid determination of the composition of algae. We have developed and applied a high-throughput screening technology based on near-infrared (NIR) spectroscopy for the rapid and accurate determination of algal biomass composition. We show that NIR spectroscopy can accurately predict the full composition using multivariate linear regression analysis of varying lipid, protein, and carbohydrate content of algal biomass samples from three strains. We also demonstrate a high quality of predictions of an independent validation set. A high-throughput 96-well configuration for spectroscopy gives equally good prediction relative to a ring-cup configuration, and thus, spectra can be obtained from as little as 10-20 mg of material. We found that lipids exhibit a dominant, distinct, and unique fingerprint in the NIR spectrum that allows for the use of single and multiple linear regression of respective wavelengths for the prediction of the biomass lipid content. This is not the case for carbohydrate and protein content, and thus, the use of multivariate statistical modeling approaches remains necessary.

Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA

USGS Publications Warehouse

Ohlmacher, G.C.; Davis, J.C.

2003-01-01

Landslides in the hilly terrain along the Kansas and Missouri rivers in northeastern Kansas have caused millions of dollars in property damage during the last decade. To address this problem, a statistical method called multiple logistic regression has been used to create a landslide-hazard map for Atchison, Kansas, and surrounding areas. Data included digitized geology, slopes, and landslides, manipulated using ArcView GIS. Logistic regression relates predictor variables to the occurrence or nonoccurrence of landslides within geographic cells and uses the relationship to produce a map showing the probability of future landslides, given local slopes and geologic units. Results indicated that slope is the most important variable for estimating landslide hazard in the study area. Geologic units consisting mostly of shale, siltstone, and sandstone were most susceptible to landslides. Soil type and aspect ratio were considered but excluded from the final analysis because these variables did not significantly add to the predictive power of the logistic regression. Soil types were highly correlated with the geologic units, and no significant relationships existed between landslides and slope aspect. ?? 2003 Elsevier Science B.V. All rights reserved.

Statistical methods and regression analysis of stratospheric ozone and meteorological variables in Isfahan

NASA Astrophysics Data System (ADS)

Hassanzadeh, S.; Hosseinibalam, F.; Omidvari, M.

2008-04-01

Data of seven meteorological variables (relative humidity, wet temperature, dry temperature, maximum temperature, minimum temperature, ground temperature and sun radiation time) and ozone values have been used for statistical analysis. Meteorological variables and ozone values were analyzed using both multiple linear regression and principal component methods. Data for the period 1999-2004 are analyzed jointly using both methods. For all periods, temperature dependent variables were highly correlated, but were all negatively correlated with relative humidity. Multiple regression analysis was used to fit the meteorological variables using the meteorological variables as predictors. A variable selection method based on high loading of varimax rotated principal components was used to obtain subsets of the predictor variables to be included in the linear regression model of the meteorological variables. In 1999, 2001 and 2002 one of the meteorological variables was weakly influenced predominantly by the ozone concentrations. However, the model did not predict that the meteorological variables for the year 2000 were not influenced predominantly by the ozone concentrations that point to variation in sun radiation. This could be due to other factors that were not explicitly considered in this study.

Computing mammographic density from a multiple regression model constructed with image-acquisition parameters from a full-field digital mammographic unit

PubMed Central

Lu, Lee-Jane W.; Nishino, Thomas K.; Khamapirad, Tuenchit; Grady, James J; Leonard, Morton H.; Brunder, Donald G.

2009-01-01

Breast density (the percentage of fibroglandular tissue in the breast) has been suggested to be a useful surrogate marker for breast cancer risk. It is conventionally measured using screen-film mammographic images by a labor intensive histogram segmentation method (HSM). We have adapted and modified the HSM for measuring breast density from raw digital mammograms acquired by full-field digital mammography. Multiple regression model analyses showed that many of the instrument parameters for acquiring the screening mammograms (e.g. breast compression thickness, radiological thickness, radiation dose, compression force, etc) and image pixel intensity statistics of the imaged breasts were strong predictors of the observed threshold values (model R2=0.93) and %density (R2=0.84). The intra-class correlation coefficient of the %-density for duplicate images was estimated to be 0.80, using the regression model-derived threshold values, and 0.94 if estimated directly from the parameter estimates of the %-density prediction regression model. Therefore, with additional research, these mathematical models could be used to compute breast density objectively, automatically bypassing the HSM step, and could greatly facilitate breast cancer research studies. PMID:17671343

Regression analysis for LED color detection of visual-MIMO system

NASA Astrophysics Data System (ADS)

Banik, Partha Pratim; Saha, Rappy; Kim, Ki-Doo

2018-04-01

Color detection from a light emitting diode (LED) array using a smartphone camera is very difficult in a visual multiple-input multiple-output (visual-MIMO) system. In this paper, we propose a method to determine the LED color using a smartphone camera by applying regression analysis. We employ a multivariate regression model to identify the LED color. After taking a picture of an LED array, we select the LED array region, and detect the LED using an image processing algorithm. We then apply the k-means clustering algorithm to determine the number of potential colors for feature extraction of each LED. Finally, we apply the multivariate regression model to predict the color of the transmitted LEDs. In this paper, we show our results for three types of environmental light condition: room environmental light, low environmental light (560 lux), and strong environmental light (2450 lux). We compare the results of our proposed algorithm from the analysis of training and test R-Square (%) values, percentage of closeness of transmitted and predicted colors, and we also mention about the number of distorted test data points from the analysis of distortion bar graph in CIE1931 color space.

Multiple regression technique for Pth degree polynominals with and without linear cross products

NASA Technical Reports Server (NTRS)

Davis, J. W.

1973-01-01

A multiple regression technique was developed by which the nonlinear behavior of specified independent variables can be related to a given dependent variable. The polynomial expression can be of Pth degree and can incorporate N independent variables. Two cases are treated such that mathematical models can be studied both with and without linear cross products. The resulting surface fits can be used to summarize trends for a given phenomenon and provide a mathematical relationship for subsequent analysis. To implement this technique, separate computer programs were developed for the case without linear cross products and for the case incorporating such cross products which evaluate the various constants in the model regression equation. In addition, the significance of the estimated regression equation is considered and the standard deviation, the F statistic, the maximum absolute percent error, and the average of the absolute values of the percent of error evaluated. The computer programs and their manner of utilization are described. Sample problems are included to illustrate the use and capability of the technique which show the output formats and typical plots comparing computer results to each set of input data.

A Study of the Effect of the Front-End Styling of Sport Utility Vehicles on Pedestrian Head Injuries

PubMed Central

Qin, Qin; Chen, Zheng; Bai, Zhonghao; Cao, Libo

2018-01-01

Background The number of sport utility vehicles (SUVs) on China market is continuously increasing. It is necessary to investigate the relationships between the front-end styling features of SUVs and head injuries at the styling design stage for improving the pedestrian protection performance and product development efficiency. Methods Styling feature parameters were extracted from the SUV side contour line. And simplified finite element models were established based on the 78 SUV side contour lines. Pedestrian headform impact simulations were performed and validated. The head injury criterion of 15 ms (HIC15) at four wrap-around distances was obtained. A multiple linear regression analysis method was employed to describe the relationships between the styling feature parameters and the HIC15 at each impact point. Results The relationship between the selected styling features and the HIC15 showed reasonable correlations, and the regression models and the selected independent variables showed statistical significance. Conclusions The regression equations obtained by multiple linear regression can be used to assess the performance of SUV styling in protecting pedestrians' heads and provide styling designers with technical guidance regarding their artistic creations.

Discrimination of relationships with the same degree of kinship using chromosomal sharing patterns estimated from high-density SNPs.

PubMed

Morimoto, Chie; Manabe, Sho; Fujimoto, Shuntaro; Hamano, Yuya; Tamaki, Keiji

2018-03-01

Distinguishing relationships with the same degree of kinship (e.g., uncle-nephew and grandfather-grandson) is generally difficult in forensic genetics by using the commonly employed short tandem repeat loci. In this study, we developed a new method for discerning such relationships between two individuals by examining the number of chromosomal shared segments estimated from high-density single nucleotide polymorphisms (SNPs). We computationally generated second-degree kinships (i.e., uncle-nephew and grandfather-grandson) and third-degree kinships (i.e., first cousins and great-grandfather-great-grandson) for 174,254 autosomal SNPs considering the effect of linkage disequilibrium and recombination for each SNP. We investigated shared chromosomal segments between two individuals that were estimated based on identity by state regions. We then counted the number of segments in each pair. Based on our results, the number of shared chromosomal segments in collateral relationships was larger than that in lineal relationships with both the second-degree and third-degree kinships. This was probably caused by differences involving chromosomal transitions and recombination between relationships. As we probabilistically evaluated the relationships between simulated pairs based on the number of shared segments using logistic regression, we could determine accurate relationships in >90% of second-degree relatives and >70% of third-degree relatives, using a probability criterion for the relationship ≥0.9. Furthermore, we could judge the true relationships of actual sample pairs from volunteers, as well as simulated data. Therefore, this method can be useful for discerning relationships between two individuals with the same degree of kinship. Copyright © 2017 Elsevier B.V. All rights reserved.

The relationship between sarcopenia,undernutrition, physical mobility and basic activities of daily living in a group of elderly women of Mexico City.

PubMed

Velázquez Alva, María del Consuelo; Irigoyen Camacho, Maria Esther; Delgadillo Velázquez, Jaime; Lazarevich, Irina

2013-01-01

Sarcopenia is a geriatric syndrome, which affects the functional status and mobility of individuals. To identify the prevalence of sarcopenia and undernutrition, and to assess the association between sarcopenia and mobility, and sarcopenia and basic activities of daily living (ADL) in a group of elderly women. A cross-sectional study was performed in patients attending a geriatric service at a government hospital in Mexico City. Sarcopenia was identified applying Baumgartner's equation by obtaining skeletal muscle mass index (SMI) and undernutrition was diagnosed using mini-nutritional assessment instrument (MNA). The SENECA questionnaire and Katz index were used to evaluate mobility and ADL, respectively. lineal regression models were constructed fitting SMI as a dependent variable and SENECA, and ADL scores as independent variables, adjusted for age. 90 women with a mean age of 78.2 (± 6.8) were studied. Undernutrition was identified in 15.5% of the patients. The prevalence of sarcopenia was 41.1%. Patients with sarcopenia presented a high prevalence of undernutrition (p < 0.001). Sarcopenia was associated with difficulty in climbing stairs (OR = 2.63, p = 0.03), adjusted for age. The mobility score was associated with sarcopenia, in the group without sarcopenia the mean score was 16.3, and with sarcopenia it was 18.3, (p = 0.04). Regarding ADL, 64.9% of the patients with intermediate independence and dependence had sarcopenia, while 35.1% with total independence presented sarcopenia (p < 0.001). The elder women with sarcopenia had a higher prevalence of undernutrition. Sarcopenia was associated with difficulties in mobility, particularly difficulties in climbing stairs. Copyright © AULA MEDICA EDICIONES 2013. Published by AULA MEDICA. All rights reserved.

Cancer Patients Enrolled in a Smoking Cessation Clinical Trial: Characteristics and Correlates of Smoking Rate and Nicotine Dependence.

PubMed

Miele, Andrew; Thompson, Morgan; Jao, Nancy C; Kalhan, Ravi; Leone, Frank; Hogarth, Lee; Hitsman, Brian; Schnoll, Robert

2018-01-01

A substantial proportion of cancer patients continue to smoke after their diagnosis but few studies have evaluated correlates of nicotine dependence and smoking rate in this population, which could help guide smoking cessation interventions. This study evaluated correlates of smoking rate and nicotine dependence among 207 cancer patients. A cross-sectional analysis using multiple linear regression evaluated disease, demographic, affective, and tobacco-seeking correlates of smoking rate and nicotine dependence. Smoking rate was assessed using a timeline follow-back method. The Fagerström Test for Nicotine Dependence measured levels of nicotine dependence. A multiple linear regression predicting nicotine dependence showed an association with smoking to alleviate a sense of addiction from the Reasons for Smoking scale and tobacco-seeking behavior from the concurrent choice task ( p < .05), but not with affect measured by the HADS and PANAS ( p > .05). Multiple linear regression predicting prequit showed an association with smoking to alleviate addiction ( p < .05). ANOVA showed that Caucasian participants reported greater rates of smoking compared to other races. The results suggest that behavioral smoking cessation interventions that focus on helping patients to manage tobacco-seeking behavior, rather than mood management interventions, could help cancer patients quit smoking.

Identification of molecular markers associated with mite resistance in coconut (Cocos nucifera L.).

PubMed

Shalini, K V; Manjunatha, S; Lebrun, P; Berger, A; Baudouin, L; Pirany, N; Ranganath, R M; Prasad, D Theertha

2007-01-01

Coconut mite (Aceria guerreronis 'Keifer') has become a major threat to Indian coconut (Coçcos nucifera L.) cultivators and the processing industry. Chemical and biological control measures have proved to be costly, ineffective, and ecologically undesirable. Planting mite-resistant coconut cultivars is the most effective method of preventing yield loss and should form a major component of any integrated pest management stratagem. Coconut genotypes, and mite-resistant and -susceptible accessions were collected from different parts of South India. Thirty-two simple sequence repeat (SSR) and 7 RAPD primers were used for molecular analyses. In single-marker analysis, 9 SSR and 4 RAPD markers associated with mite resistance were identified. In stepwise multiple regression analysis of SSRs, a combination of 6 markers showed 100% association with mite infestation. Stepwise multiple regression analysis for RAPD data revealed that a combination of 3 markers accounted for 83.86% of mite resistance in the selected materials. Combined stepwise multiple regression analysis of RAPD and SSR data showed that a combination of 5 markers explained 100% of the association with mite resistance in coconut. Markers associated with mite resistance are important in coconut breeding programs and will facilitate the selection of mite-resistant plants at an early stage as well as mother plants for breeding programs.

Multiple regression analysis of anthropometric measurements influencing the cephalic index of male Japanese university students.

PubMed

Hossain, Md Golam; Saw, Aik; Alam, Rashidul; Ohtsuki, Fumio; Kamarul, Tunku

2013-09-01

Cephalic index (CI), the ratio of head breadth to head length, is widely used to categorise human populations. The aim of this study was to access the impact of anthropometric measurements on the CI of male Japanese university students. This study included 1,215 male university students from Tokyo and Kyoto, selected using convenient sampling. Multiple regression analysis was used to determine the effect of anthropometric measurements on CI. The variance inflation factor (VIF) showed no evidence of a multicollinearity problem among independent variables. The coefficients of the regression line demonstrated a significant positive relationship between CI and minimum frontal breadth (p < 0.01), bizygomatic breadth (p < 0.01) and head height (p < 0.05), and a negative relationship between CI and morphological facial height (p < 0.01) and head circumference (p < 0.01). Moreover, the coefficient and odds ratio of logistic regression analysis showed a greater likelihood for minimum frontal breadth (p < 0.01) and bizygomatic breadth (p < 0.01) to predict round-headedness, and morphological facial height (p < 0.05) and head circumference (p < 0.01) to predict long-headedness. Stepwise regression analysis revealed bizygomatic breadth, head circumference, minimum frontal breadth, head height and morphological facial height to be the best predictor craniofacial measurements with respect to CI. The results suggest that most of the variables considered in this study appear to influence the CI of adult male Japanese students.

Models for predicting the mass of lime fruits by some engineering properties.

PubMed

Miraei Ashtiani, Seyed-Hassan; Baradaran Motie, Jalal; Emadi, Bagher; Aghkhani, Mohammad-Hosein

2014-11-01

Grading fruits based on mass is important in packaging and reduces the waste, also increases the marketing value of agricultural produce. The aim of this study was mass modeling of two major cultivars of Iranian limes based on engineering attributes. Models were classified into three: 1-Single and multiple variable regressions of lime mass and dimensional characteristics. 2-Single and multiple variable regressions of lime mass and projected areas. 3-Single regression of lime mass based on its actual volume and calculated volume assumed as ellipsoid and prolate spheroid shapes. All properties considered in the current study were found to be statistically significant (ρ < 0.01). The results indicated that mass modeling of lime based on minor diameter and first projected area are the most appropriate models in the first and the second classifications, respectively. In third classification, the best model was obtained on the basis of the prolate spheroid volume. It was finally concluded that the suitable grading system of lime mass is based on prolate spheroid volume.

Energy expenditure estimation during daily military routine with body-fixed sensors.

PubMed

Wyss, Thomas; Mäder, Urs

2011-05-01

The purpose of this study was to develop and validate an algorithm for estimating energy expenditure during the daily military routine on the basis of data collected using body-fixed sensors. First, 8 volunteers completed isolated physical activities according to an established protocol, and the resulting data were used to develop activity-class-specific multiple linear regressions for physical activity energy expenditure on the basis of hip acceleration, heart rate, and body mass as independent variables. Second, the validity of these linear regressions was tested during the daily military routine using indirect calorimetry (n = 12). Volunteers' mean estimated energy expenditure did not significantly differ from the energy expenditure measured with indirect calorimetry (p = 0.898, 95% confidence interval = -1.97 to 1.75 kJ/min). We conclude that the developed activity-class-specific multiple linear regressions applied to the acceleration and heart rate data allow estimation of energy expenditure in 1-minute intervals during daily military routine, with accuracy equal to indirect calorimetry.

[Quantitative structure-gas chromatographic retention relationship of polycyclic aromatic sulfur heterocycles using molecular electronegativity-distance vector].

PubMed

Li, Zhenghua; Cheng, Fansheng; Xia, Zhining

2011-01-01

The chemical structures of 114 polycyclic aromatic sulfur heterocycles (PASHs) have been studied by molecular electronegativity-distance vector (MEDV). The linear relationships between gas chromatographic retention index and the MEDV have been established by a multiple linear regression (MLR) model. The results of variable selection by stepwise multiple regression (SMR) and the powerful predictive abilities of the optimization model appraised by leave-one-out cross-validation showed that the optimization model with the correlation coefficient (R) of 0.994 7 and the cross-validated correlation coefficient (Rcv) of 0.994 0 possessed the best statistical quality. Furthermore, when the 114 PASHs compounds were divided into calibration and test sets in the ratio of 2:1, the statistical analysis showed our models possesses almost equal statistical quality, the very similar regression coefficients and the good robustness. The quantitative structure-retention relationship (QSRR) model established may provide a convenient and powerful method for predicting the gas chromatographic retention of PASHs.

A psycholinguistic database for traditional Chinese character naming.

PubMed

Chang, Ya-Ning; Hsu, Chun-Hsien; Tsai, Jie-Li; Chen, Chien-Liang; Lee, Chia-Ying

2016-03-01

In this study, we aimed to provide a large-scale set of psycholinguistic norms for 3,314 traditional Chinese characters, along with their naming reaction times (RTs), collected from 140 Chinese speakers. The lexical and semantic variables in the database include frequency, regularity, familiarity, consistency, number of strokes, homophone density, semantic ambiguity rating, phonetic combinability, semantic combinability, and the number of disyllabic compound words formed by a character. Multiple regression analyses were conducted to examine the predictive powers of these variables for the naming RTs. The results demonstrated that these variables could account for a significant portion of variance (55.8%) in the naming RTs. An additional multiple regression analysis was conducted to demonstrate the effects of consistency and character frequency. Overall, the regression results were consistent with the findings of previous studies on Chinese character naming. This database should be useful for research into Chinese language processing, Chinese education, or cross-linguistic comparisons. The database can be accessed via an online inquiry system (http://ball.ling.sinica.edu.tw/namingdatabase/index.html).

A new multiple regression model to identify multi-family houses with a high prevalence of sick building symptoms "SBS", within the healthy sustainable house study in Stockholm (3H).

PubMed

Engvall, Karin; Hult, M; Corner, R; Lampa, E; Norbäck, D; Emenius, G

2010-01-01

The aim was to develop a new model to identify residential buildings with higher frequencies of "SBS" than expected, "risk buildings". In 2005, 481 multi-family buildings with 10,506 dwellings in Stockholm were studied by a new stratified random sampling. A standardised self-administered questionnaire was used to assess "SBS", atopy and personal factors. The response rate was 73%. Statistical analysis was performed by multiple logistic regressions. Dwellers owning their building reported less "SBS" than those renting. There was a strong relationship between socio-economic factors and ownership. The regression model, ended up with high explanatory values for age, gender, atopy and ownership. Applying our model, 9% of all residential buildings in Stockholm were classified as "risk buildings" with the highest proportion in houses built 1961-1975 (26%) and lowest in houses built 1985-1990 (4%). To identify "risk buildings", it is necessary to adjust for ownership and population characteristics.

Adherence to preferable behavior for lipid control by high-risk dyslipidemic Japanese patients under pravastatin treatment: the APPROACH-J study.

PubMed

Kitagawa, Yasuhisa; Teramoto, Tamio; Daida, Hiroyuki

2012-01-01

We evaluated the impact of adherence to preferable behavior on serum lipid control assessed by a self-reported questionnaire in high-risk patients taking pravastatin for primary prevention of coronary artery disease. High-risk patients taking pravastatin were followed for 2 years. Questionnaire surveys comprising 21 questions, including 18 questions concerning awareness of health, and current status of diet, exercise, and drug therapy, were conducted at baseline and after 1 year. Potential domains were established by factor analysis from the results of questionnaires, and adherence scores were calculated in each domain. The relationship between adherence scores and lipid values during the 1-year treatment period was analyzed by each domain using multiple regression analysis. A total of 5,792 patients taking pravastatin were included in the analysis. Multiple regression analysis showed a significant correlation in terms of "Intake of high fat/cholesterol/sugar foods" (regression coefficient -0.58, p=0.0105) and "Adherence to instructions for drug therapy" (regression coefficient -6.61, p<0.0001). Low-density lipoprotein cholesterol (LDL-C) values were significantly lower in patients who had an increase in the adherence score in the "Awareness of health" domain compared with those with a decreased score. There was a significant correlation between high-density lipoprotein (HDL-C) values and "Awareness of health" (regression coefficient 0.26; p= 0.0037), "Preferable dietary behaviors" (regression coefficient 0.75; p<0.0001), and "Exercise" (regression coefficient 0.73; p= 0.0002). Similar relations were seen with triglycerides. In patients who have a high awareness of their health, a positive attitude toward lipid-lowering treatment including diet, exercise, and high adherence to drug therapy, is related with favorable overall lipid control even in patients under treatment with pravastatin.

Prediction of random-regression coefficient for daily milk yield after 305 days in milk by using the regression-coefficient estimates from the first 305 days.

PubMed

Yamazaki, Takeshi; Takeda, Hisato; Hagiya, Koichi; Yamaguchi, Satoshi; Sasaki, Osamu

2018-03-13

Because lactation periods in dairy cows lengthen with increasing total milk production, it is important to predict individual productivities after 305 days in milk (DIM) to determine the optimal lactation period. We therefore examined whether the random regression (RR) coefficient from 306 to 450 DIM (M2) can be predicted from those during the first 305 DIM (M1) by using a random regression model. We analyzed test-day milk records from 85690 Holstein cows in their first lactations and 131727 cows in their later (second to fifth) lactations. Data in M1 and M2 were analyzed separately by using different single-trait RR animal models. We then performed a multiple regression analysis of the RR coefficients of M2 on those of M1 during the first and later lactations. The first-order Legendre polynomials were practical covariates of random regression for the milk yields of M2. All RR coefficients for the additive genetic (AG) effect and the intercept for the permanent environmental (PE) effect of M2 had moderate to strong correlations with the intercept for the AG effect of M1. The coefficients of determination for multiple regression of the combined intercepts for the AG and PE effects of M2 on the coefficients for the AG effect of M1 were moderate to high. The daily milk yields of M2 predicted by using the RR coefficients for the AG effect of M1 were highly correlated with those obtained by using the coefficients of M2. Milk production after 305 DIM can be predicted by using the RR coefficient estimates of the AG effect during the first 305 DIM.

Simultaneous estimation of transcutaneous bilirubin, hemoglobin, and melanin based on diffuse reflectance spectroscopy

NASA Astrophysics Data System (ADS)

Nishidate, Izumi; Abdul, Wares MD.; Ohtsu, Mizuki; Nakano, Kazuya; Haneishi, Hideaki

2018-02-01

We propose a method to estimate transcutaneous bilirubin, hemoglobin, and melanin based on the diffuse reflectance spectroscopy. In the proposed method, the Monte Carlo simulation-based multiple regression analysis for an absorbance spectrum in the visible wavelength region (460-590 nm) is used to specify the concentrations of bilirubin (Cbil), oxygenated hemoglobin (Coh), deoxygenated hemoglobin (Cdh), and melanin (Cm). Using the absorbance spectrum calculated from the measured diffuse reflectance spectrum as a response variable and the extinction coefficients of bilirubin, oxygenated hemoglobin, deoxygenated hemoglobin, and melanin, as predictor variables, multiple regression analysis provides regression coefficients. Concentrations of bilirubin, oxygenated hemoglobin, deoxygenated hemoglobin, and melanin, are then determined from the regression coefficients using conversion vectors that are numerically deduced in advance by the Monte Carlo simulations for light transport in skin. Total hemoglobin concentration (Cth) and tissue oxygen saturation (StO2) are simply calculated from the oxygenated hemoglobin and deoxygenated hemoglobin. In vivo animal experiments with bile duct ligation in rats demonstrated that the estimated Cbil is increased after ligation of bile duct and reaches to around 20 mg/dl at 72 h after the onset of the ligation, which corresponds to the reference value of Cbil measured by a commercially available transcutaneous bilirubin meter. We also performed in vivo experiments with rats while varying the fraction of inspired oxygen (FiO2). Coh and Cdh decreased and increased, respectively, as FiO2 decreased. Consequently, StO2 was dramatically decreased. The results in this study indicate potential of the method for simultaneous evaluation of multiple chromophores in skin tissue.

Do climate variables and human density affect Achatina fulica (Bowditch) (Gastropoda: Pulmonata) shell length, total weight and condition factor?

PubMed

Albuquerque, F S; Peso-Aguiar, M C; Assunção-Albuquerque, M J T; Gálvez, L

2009-08-01

The length-weight relationship and condition factor have been broadly investigated in snails to obtain the index of physical condition of populations and evaluate habitat quality. Herein, our goal was to describe the best predictors that explain Achatina fulica biometrical parameters and well being in a recently introduced population. From November 2001 to November 2002, monthly snail samples were collected in Lauro de Freitas City, Bahia, Brazil. Shell length and total weight were measured in the laboratory and the potential curve and condition factor were calculated. Five environmental variables were considered: temperature range, mean temperature, humidity, precipitation and human density. Multiple regressions were used to generate models including multiple predictors, via model selection approach, and then ranked with AIC criteria. Partial regressions were used to obtain the separated coefficients of determination of climate and human density models. A total of 1.460 individuals were collected, presenting a shell length range between 4.8 to 102.5 mm (mean: 42.18 mm). The relationship between total length and total weight revealed that Achatina fulica presented a negative allometric growth. Simple regression indicated that humidity has a significant influence on A. fulica total length and weight. Temperature range was the main variable that influenced the condition factor. Multiple regressions showed that climatic and human variables explain a small proportion of the variance in shell length and total weight, but may explain up to 55.7% of the condition factor variance. Consequently, we believe that the well being and biometric parameters of A. fulica can be influenced by climatic and human density factors.

Hierarchical multiple regression modelling on predictors of behavior and sexual practices at Takoradi Polytechnic, Ghana.

PubMed

Turkson, Anthony Joe; Otchey, James Eric

2015-01-14

Various psychosocial studies on health related lifestyles lay emphasis on the fact that the perception one has of himself as being at risk of HIV/AIDS infection was a necessary condition for preventive behaviors to be adopted. Hierarchical Multiple Regression models was used to examine the relationship between eight independent variables and one dependent variable to isolate predictors which have significant influence on behavior and sexual practices. A Cross-sectional design was used for the study. Structured close-ended interviewer-administered questionnaire was used to collect primary data. Multistage stratified technique was used to sample views from 380 students from Takoradi Polytechnic, Ghana. A Hierarchical multiple regression model was used to ascertain the significance of certain predictors of sexual behavior and practices. The variables that were extracted from the multiple regression were; for the constant; Beta=14.202, t=2.279, p=0.023, variable is significant; for the marital status; Beta=0.092, t=1.996, p<0.05, variable is significant; for the knowledge on AIDs; Beta=0.090, t=1.996, p<0.05, variable is significant; for the attitude towards HIV/AIDs; =0.486, t=10.575, p<0.001, variable is highly significant. Thus, the best fitting model for predicting behavior and sexual practices was a linear combination of the constant, one's marital status, knowledge on HIV/AIDs and Attitude towards HIV/AIDs., Y(Behavior and sexual practies)= Beta0+Beta1(Marital status)+Beta2(Knowledge on HIV/AIDs issues)+Beta3(Attitude towards HIV/AIDs issues) Beta0, Beta1, Beta2 and Beta3 are respectively 14.201, 2.038, 0.148 and 0.486; the higher the better. Attitude and behavior change education on HIV/AIDs should be intensified in the institution so that students could adopt better lifestyles.

Locomotive syndrome is associated not only with physical capacity but also degree of depression.

PubMed

Ikemoto, Tatsunori; Inoue, Masayuki; Nakata, Masatoshi; Miyagawa, Hirofumi; Shimo, Kazuhiro; Wakabayashi, Toshiko; Arai, Young-Chang P; Ushida, Takahiro

2016-05-01

Reports of locomotive syndrome (LS) have recently been increasing. Although physical performance measures for LS have been well investigated to date, studies including psychiatric assessment are still scarce. Hence, the aim of this study was to investigate both physical and mental parameters in relation to presence and severity of LS using a 25-question geriatric locomotive function scale (GLFS-25) questionnaire. 150 elderly people aged over 60 years who were members of our physical-fitness center and displayed well-being were enrolled in this study. Firstly, using the previously determined GLFS-25 cutoff value (=16 points), subjects were divided into two groups accordingly: an LS and non-LS group in order to compare each parameter (age, grip strength, timed-up-and-go test (TUG), one-leg standing with eye open, back muscle and leg muscle strength, degree of depression and cognitive impairment) between the groups using the Mann-Whitney U-test followed by multiple logistic regression analysis. Secondly, a multiple linear regression was conducted to determine which variables showed the strongest correlation with severity of LS. We confirmed 110 people for non-LS (73%) and 40 people for LS using the GLFS-25 cutoff value. Comparative analysis between LS and non-LS revealed significant differences in parameters in age, grip strength, TUG, one-leg standing, back muscle strength and degree of depression (p < 0.006, after Bonferroni correction). Multiple logistic regression revealed that functional decline in grip strength, TUG and one-leg standing and degree of depression were significantly associated with LS. On the other hand, we observed that the significant contributors towards the GLFS-25 score were TUG and degree of depression in multiple linear regression analysis. The results indicate that LS is associated with not only the capacity of physical performance but also the degree of depression although most participants fell under the criteria of LS. Copyright © 2016 The Japanese Orthopaedic Association. Published by Elsevier B.V. All rights reserved.

Bankfull characteristics of Ohio streams and their relation to peak streamflows

USGS Publications Warehouse

Sherwood, James M.; Huitger, Carrie A.

2005-01-01

Regional curves, simple-regression equations, and multiple-regression equations were developed to estimate bankfull width, bankfull mean depth, bankfull cross-sectional area, and bankfull discharge of rural, unregulated streams in Ohio. The methods are based on geomorphic, basin, and flood-frequency data collected at 50 study sites on unregulated natural alluvial streams in Ohio, of which 40 sites are near streamflow-gaging stations. The regional curves and simple-regression equations relate the bankfull characteristics to drainage area. The multiple-regression equations relate the bankfull characteristics to drainage area, main-channel slope, main-channel elevation index, median bed-material particle size, bankfull cross-sectional area, and local-channel slope. Average standard errors of prediction for bankfull width equations range from 20.6 to 24.8 percent; for bankfull mean depth, 18.8 to 20.6 percent; for bankfull cross-sectional area, 25.4 to 30.6 percent; and for bankfull discharge, 27.0 to 78.7 percent. The simple-regression (drainage-area only) equations have the highest average standard errors of prediction. The multiple-regression equations in which the explanatory variables included drainage area, main-channel slope, main-channel elevation index, median bed-material particle size, bankfull cross-sectional area, and local-channel slope have the lowest average standard errors of prediction. Field surveys were done at each of the 50 study sites to collect the geomorphic data. Bankfull indicators were identified and evaluated, cross-section and longitudinal profiles were surveyed, and bed- and bank-material were sampled. Field data were analyzed to determine various geomorphic characteristics such as bankfull width, bankfull mean depth, bankfull cross-sectional area, bankfull discharge, streambed slope, and bed- and bank-material particle-size distribution. The various geomorphic characteristics were analyzed by means of a combination of graphical and statistical techniques. The logarithms of the annual peak discharges for the 40 gaged study sites were fit by a Pearson Type III frequency distribution to develop flood-peak discharges associated with recurrence intervals of 2, 5, 10, 25, 50, and 100 years. The peak-frequency data were related to geomorphic, basin, and climatic variables by multiple-regression analysis. Simple-regression equations were developed to estimate 2-, 5-, 10-, 25-, 50-, and 100-year flood-peak discharges of rural, unregulated streams in Ohio from bankfull channel cross-sectional area. The average standard errors of prediction are 31.6, 32.6, 35.9, 41.5, 46.2, and 51.2 percent, respectively. The study and methods developed are intended to improve understanding of the relations between geomorphic, basin, and flood characteristics of streams in Ohio and to aid in the design of hydraulic structures, such as culverts and bridges, where stability of the stream and structure is an important element of the design criteria. The study was done in cooperation with the Ohio Department of Transportation and the U.S. Department of Transportation, Federal Highway Administration.

Survival Data and Regression Models

NASA Astrophysics Data System (ADS)

Grégoire, G.

2014-12-01

We start this chapter by introducing some basic elements for the analysis of censored survival data. Then we focus on right censored data and develop two types of regression models. The first one concerns the so-called accelerated failure time models (AFT), which are parametric models where a function of a parameter depends linearly on the covariables. The second one is a semiparametric model, where the covariables enter in a multiplicative form in the expression of the hazard rate function. The main statistical tool for analysing these regression models is the maximum likelihood methodology and, in spite we recall some essential results about the ML theory, we refer to the chapter "Logistic Regression" for a more detailed presentation.

Developing a predictive tropospheric ozone model for Tabriz

NASA Astrophysics Data System (ADS)

Khatibi, Rahman; Naghipour, Leila; Ghorbani, Mohammad A.; Smith, Michael S.; Karimi, Vahid; Farhoudi, Reza; Delafrouz, Hadi; Arvanaghi, Hadi

2013-04-01

Predictive ozone models are becoming indispensable tools by providing a capability for pollution alerts to serve people who are vulnerable to the risks. We have developed a tropospheric ozone prediction capability for Tabriz, Iran, by using the following five modeling strategies: three regression-type methods: Multiple Linear Regression (MLR), Artificial Neural Networks (ANNs), and Gene Expression Programming (GEP); and two auto-regression-type models: Nonlinear Local Prediction (NLP) to implement chaos theory and Auto-Regressive Integrated Moving Average (ARIMA) models. The regression-type modeling strategies explain the data in terms of: temperature, solar radiation, dew point temperature, and wind speed, by regressing present ozone values to their past values. The ozone time series are available at various time intervals, including hourly intervals, from August 2010 to March 2011. The results for MLR, ANN and GEP models are not overly good but those produced by NLP and ARIMA are promising for the establishing a forecasting capability.

Small-Sample Adjustments for Tests of Moderators and Model Fit in Robust Variance Estimation in Meta-Regression

ERIC Educational Resources Information Center

Tipton, Elizabeth; Pustejovsky, James E.

2015-01-01

Randomized experiments are commonly used to evaluate the effectiveness of educational interventions. The goal of the present investigation is to develop small-sample corrections for multiple contrast hypothesis tests (i.e., F-tests) such as the omnibus test of meta-regression fit or a test for equality of three or more levels of a categorical…

Regression modeling and mapping of coniferous forest basal area and tree density from discrete-return lidar and multispectral data

Treesearch

Andrew T. Hudak; Nicholas L. Crookston; Jeffrey S. Evans; Michael K. Falkowski; Alistair M. S. Smith; Paul E. Gessler; Penelope Morgan

2006-01-01

We compared the utility of discrete-return light detection and ranging (lidar) data and multispectral satellite imagery, and their integration, for modeling and mapping basal area and tree density across two diverse coniferous forest landscapes in north-central Idaho. We applied multiple linear regression models subset from a suite of 26 predictor variables derived...

A regression tree for identifying combinations of fall risk factors associated to recurrent falling: a cross-sectional elderly population-based study.

PubMed

Kabeshova, A; Annweiler, C; Fantino, B; Philip, T; Gromov, V A; Launay, C P; Beauchet, O

2014-06-01

Regression tree (RT) analyses are particularly adapted to explore the risk of recurrent falling according to various combinations of fall risk factors compared to logistic regression models. The aims of this study were (1) to determine which combinations of fall risk factors were associated with the occurrence of recurrent falls in older community-dwellers, and (2) to compare the efficacy of RT and multiple logistic regression model for the identification of recurrent falls. A total of 1,760 community-dwelling volunteers (mean age ± standard deviation, 71.0 ± 5.1 years; 49.4 % female) were recruited prospectively in this cross-sectional study. Age, gender, polypharmacy, use of psychoactive drugs, fear of falling (FOF), cognitive disorders and sad mood were recorded. In addition, the history of falls within the past year was recorded using a standardized questionnaire. Among 1,760 participants, 19.7 % (n = 346) were recurrent fallers. The RT identified 14 nodes groups and 8 end nodes with FOF as the first major split. Among participants with FOF, those who had sad mood and polypharmacy formed the end node with the greatest OR for recurrent falls (OR = 6.06 with p < 0.001). Among participants without FOF, those who were male and not sad had the lowest OR for recurrent falls (OR = 0.25 with p < 0.001). The RT correctly classified 1,356 from 1,414 non-recurrent fallers (specificity = 95.6 %), and 65 from 346 recurrent fallers (sensitivity = 18.8 %). The overall classification accuracy was 81.0 %. The multiple logistic regression correctly classified 1,372 from 1,414 non-recurrent fallers (specificity = 97.0 %), and 61 from 346 recurrent fallers (sensitivity = 17.6 %). The overall classification accuracy was 81.4 %. Our results show that RT may identify specific combinations of risk factors for recurrent falls, the combination most associated with recurrent falls involving FOF, sad mood and polypharmacy. The FOF emerged as the risk factor strongly associated with recurrent falls. In addition, RT and multiple logistic regression were not sensitive enough to identify the majority of recurrent fallers but appeared efficient in detecting individuals not at risk of recurrent falls.

Nonparametric Bayesian Multiple Imputation for Incomplete Categorical Variables in Large-Scale Assessment Surveys

ERIC Educational Resources Information Center

Si, Yajuan; Reiter, Jerome P.

2013-01-01

In many surveys, the data comprise a large number of categorical variables that suffer from item nonresponse. Standard methods for multiple imputation, like log-linear models or sequential regression imputation, can fail to capture complex dependencies and can be difficult to implement effectively in high dimensions. We present a fully Bayesian,…

Exercise, Diet, and Stress Management as Mediators between Functional Disability and Health-Related Quality of Life in Multiple Sclerosis

ERIC Educational Resources Information Center

Sung, Connie; Chiu, Chung-Yi; Lee, Eun-Jeong; Bezyak, Jill; Chan, Fong; Muller, Veronica

2013-01-01

The main objective of this study was to examine the mediational and moderational effect of exercise, diet, and stress management on the relationship between functional disability and health-related quality of life. Quantitative descriptive research design using multiple regression and correlation techniques was used. Participants were 215…

Information Scent Determines Attention Allocation and Link Selection among Multiple Information Patches on a Webpage

ERIC Educational Resources Information Center

Blackmon, Marilyn Hughes

2012-01-01

This paper draws from cognitive psychology and cognitive neuroscience to develop a preliminary similarity-choice theory of how people allocate attention among information patches on webpages while completing search tasks in complex informational websites. Study 1 applied stepwise multiple regression to a large dataset and showed that success rate…

Handling Missing Data: Analysis of a Challenging Data Set Using Multiple Imputation

ERIC Educational Resources Information Center

Pampaka, Maria; Hutcheson, Graeme; Williams, Julian

2016-01-01

Missing data is endemic in much educational research. However, practices such as step-wise regression common in the educational research literature have been shown to be dangerous when significant data are missing, and multiple imputation (MI) is generally recommended by statisticians. In this paper, we provide a review of these advances and their…

The Relationship of Selected Supply- and Demand-Side Factors to Forms of Perceived Discrimination among Adults with Multiple Sclerosis

ERIC Educational Resources Information Center

Roessler, Richard T.; Neath, Jeanne; McMahon, Brian T.; Rumrill, Phillip D.

2007-01-01

Single-predictor and stepwise multinomial logistic regression analyses and an external validation were completed on 3,082 allegations of employment discrimination by adults with multiple sclerosis. Women filed two thirds of the allegations, and individuals between 31 and 50 made the vast majority of discrimination charges (73%). Allegations…

Informal Social Networks of People with Profound Intellectual and Multiple Disabilities: Relationship with Age, Communicative Abilities and Current Living Arrangements

ERIC Educational Resources Information Center

Kamstra, A.; van der Putten, A. A. J.; Post, W. J.; Vlaskamp, C.

2015-01-01

Background: People with profound intellectual and multiple disabilities (PIMD) have limited informal social contacts. Research to determine the factors which can positively influence establishing sound informal social contacts is required. Materials and Methods: Regression analysis for 200 people with PIMD was used to analyse how age,…

Spatial interpolation schemes of daily precipitation for hydrologic modeling

USGS Publications Warehouse

Hwang, Y.; Clark, M.R.; Rajagopalan, B.; Leavesley, G.

2012-01-01

Distributed hydrologic models typically require spatial estimates of precipitation interpolated from sparsely located observational points to the specific grid points. We compare and contrast the performance of regression-based statistical methods for the spatial estimation of precipitation in two hydrologically different basins and confirmed that widely used regression-based estimation schemes fail to describe the realistic spatial variability of daily precipitation field. The methods assessed are: (1) inverse distance weighted average; (2) multiple linear regression (MLR); (3) climatological MLR; and (4) locally weighted polynomial regression (LWP). In order to improve the performance of the interpolations, the authors propose a two-step regression technique for effective daily precipitation estimation. In this simple two-step estimation process, precipitation occurrence is first generated via a logistic regression model before estimate the amount of precipitation separately on wet days. This process generated the precipitation occurrence, amount, and spatial correlation effectively. A distributed hydrologic model (PRMS) was used for the impact analysis in daily time step simulation. Multiple simulations suggested noticeable differences between the input alternatives generated by three different interpolation schemes. Differences are shown in overall simulation error against the observations, degree of explained variability, and seasonal volumes. Simulated streamflows also showed different characteristics in mean, maximum, minimum, and peak flows. Given the same parameter optimization technique, LWP input showed least streamflow error in Alapaha basin and CMLR input showed least error (still very close to LWP) in Animas basin. All of the two-step interpolation inputs resulted in lower streamflow error compared to the directly interpolated inputs. ?? 2011 Springer-Verlag.

Forecasting daily patient volumes in the emergency department.

PubMed

Jones, Spencer S; Thomas, Alun; Evans, R Scott; Welch, Shari J; Haug, Peter J; Snow, Gregory L

2008-02-01

Shifts in the supply of and demand for emergency department (ED) resources make the efficient allocation of ED resources increasingly important. Forecasting is a vital activity that guides decision-making in many areas of economic, industrial, and scientific planning, but has gained little traction in the health care industry. There are few studies that explore the use of forecasting methods to predict patient volumes in the ED. The goals of this study are to explore and evaluate the use of several statistical forecasting methods to predict daily ED patient volumes at three diverse hospital EDs and to compare the accuracy of these methods to the accuracy of a previously proposed forecasting method. Daily patient arrivals at three hospital EDs were collected for the period January 1, 2005, through March 31, 2007. The authors evaluated the use of seasonal autoregressive integrated moving average, time series regression, exponential smoothing, and artificial neural network models to forecast daily patient volumes at each facility. Forecasts were made for horizons ranging from 1 to 30 days in advance. The forecast accuracy achieved by the various forecasting methods was compared to the forecast accuracy achieved when using a benchmark forecasting method already available in the emergency medicine literature. All time series methods considered in this analysis provided improved in-sample model goodness of fit. However, post-sample analysis revealed that time series regression models that augment linear regression models by accounting for serial autocorrelation offered only small improvements in terms of post-sample forecast accuracy, relative to multiple linear regression models, while seasonal autoregressive integrated moving average, exponential smoothing, and artificial neural network forecasting models did not provide consistently accurate forecasts of daily ED volumes. This study confirms the widely held belief that daily demand for ED services is characterized by seasonal and weekly patterns. The authors compared several time series forecasting methods to a benchmark multiple linear regression model. The results suggest that the existing methodology proposed in the literature, multiple linear regression based on calendar variables, is a reasonable approach to forecasting daily patient volumes in the ED. However, the authors conclude that regression-based models that incorporate calendar variables, account for site-specific special-day effects, and allow for residual autocorrelation provide a more appropriate, informative, and consistently accurate approach to forecasting daily ED patient volumes.

Ca analysis: An Excel based program for the analysis of intracellular calcium transients including multiple, simultaneous regression analysis☆

PubMed Central

Greensmith, David J.

2014-01-01

Here I present an Excel based program for the analysis of intracellular Ca transients recorded using fluorescent indicators. The program can perform all the necessary steps which convert recorded raw voltage changes into meaningful physiological information. The program performs two fundamental processes. (1) It can prepare the raw signal by several methods. (2) It can then be used to analyze the prepared data to provide information such as absolute intracellular Ca levels. Also, the rates of change of Ca can be measured using multiple, simultaneous regression analysis. I demonstrate that this program performs equally well as commercially available software, but has numerous advantages, namely creating a simplified, self-contained analysis workflow. PMID:24125908

Food insecurity and CD4% Among HIV+ children in Gaborone, Botswana.

PubMed

Mendoza, Jason A; Matshaba, Mogomotsi; Makhanda, Jeremiah; Liu, Yan; Boitshwarelo, Matshwenyego; Anabwani, Gabriel M

2014-08-01

We investigated the association between household food insecurity (HFI) and CD4% among 2-6-year old HIV+ outpatients (n = 78) at the Botswana-Baylor Children's Clinical Center of Excellence in Gaborone, Botswana. HFI was assessed by a validated survey. CD4% data were abstracted from the medical record. We used multiple linear regression with CD4% (dependent variable), HFI (independent variable), and controlled for sociodemographic and clinical covariates. Multiple linear regression showed a significant main effect for HFI [beta = -0.6, 95% confidence interval (CI): -1.0 to -0.1] and child gender (beta = 5.6, 95% CI: 1.3 to 9.8). Alleviating food insecurity may improve pediatric HIV outcomes in Botswana and similar Sub-Saharan settings.

Relationship between single and multiple perpetrator rape perpetration in South Africa: A comparison of risk factors in a population-based sample.

PubMed

R, Jewkes; Y, Sikweyiya; K, Dunkle; R, Morrell

2015-07-07

Studies of rape of women seldom distinguish between men's participation in acts of single and multiple perpetrator rape. Multiple perpetrator rape (MPR) occurs globally with serious consequences for women. In South Africa it is a cultural practice with defined circumstances in which it commonly occurs. Prevention requires an understanding of whether it is a context specific intensification of single perpetrator rape, or a distinctly different practice of different men. This paper aims to address this question. We conducted a cross-sectional household study with a multi-stage, randomly selected sample of 1686 men aged 18-49 who completed a questionnaire administered using an Audio-enhanced Personal Digital Assistant. We attempted to fit an ordered logistic regression model for factors associated with rape perpetration. 27.6 % of men had raped and 8.8 % had perpetrated multiple perpetrator rape (MPR). Thus 31.9 % of men who had ever raped had done so with other perpetrators. An ordered regression model was fitted, showing that the same associated factors, albeit at higher prevalence, are associated with SPR and MPR. Multiple perpetrator rape appears as an intensified form of single perpetrator rape, rather than a different form of rape. Prevention approaches need to be mainstreamed among young men.

Combinations of Multiple Neuroimaging Markers using Logistic Regression for Auxiliary Diagnosis of Alzheimer Disease and Mild Cognitive Impairment.

PubMed

Mao, Nini; Liu, Yunting; Chen, Kewei; Yao, Li; Wu, Xia

2018-06-05

Multiple neuroimaging modalities have been developed providing various aspects of information on the human brain. Used together and properly, these complementary multimodal neuroimaging data integrate multisource information which can facilitate a diagnosis and improve the diagnostic accuracy. In this study, 3 types of brain imaging data (sMRI, FDG-PET, and florbetapir-PET) were fused in the hope to improve diagnostic accuracy, and multivariate methods (logistic regression) were applied to these trimodal neuroimaging indices. Then, the receiver-operating characteristic (ROC) method was used to analyze the outcomes of the logistic classifier, with either each index, multiples from each modality, or all indices from all 3 modalities, to investigate their differential abilities to identify the disease. With increasing numbers of indices within each modality and across modalities, the accuracy of identifying Alzheimer disease (AD) increases to varying degrees. For example, the area under the ROC curve is above 0.98 when all the indices from the 3 imaging data types are combined. Using a combination of different indices, the results confirmed the initial hypothesis that different biomarkers were potentially complementary, and thus the conjoint analysis of multiple information from multiple sources would improve the capability to identify diseases such as AD and mild cognitive impairment. © 2018 S. Karger AG, Basel.

Multivariate research in areas of phosphorus cast-iron brake shoes manufacturing using the statistical analysis and the multiple regression equations

NASA Astrophysics Data System (ADS)

Kiss, I.; Cioată, V. G.; Alexa, V.; Raţiu, S. A.

2017-05-01

The braking system is one of the most important and complex subsystems of railway vehicles, especially when it comes for safety. Therefore, installing efficient safe brakes on the modern railway vehicles is essential. Nowadays is devoted attention to solving problems connected with using high performance brake materials and its impact on thermal and mechanical loading of railway wheels. The main factor that influences the selection of a friction material for railway applications is the performance criterion, due to the interaction between the brake block and the wheel produce complex thermos-mechanical phenomena. In this work, the investigated subjects are the cast-iron brake shoes, which are still widely used on freight wagons. Therefore, the cast-iron brake shoes - with lamellar graphite and with a high content of phosphorus (0.8-1.1%) - need a special investigation. In order to establish the optimal condition for the cast-iron brake shoes we proposed a mathematical modelling study by using the statistical analysis and multiple regression equations. Multivariate research is important in areas of cast-iron brake shoes manufacturing, because many variables interact with each other simultaneously. Multivariate visualization comes to the fore when researchers have difficulties in comprehending many dimensions at one time. Technological data (hardness and chemical composition) obtained from cast-iron brake shoes were used for this purpose. In order to settle the multiple correlation between the hardness of the cast-iron brake shoes, and the chemical compositions elements several model of regression equation types has been proposed. Because a three-dimensional surface with variables on three axes is a common way to illustrate multivariate data, in which the maximum and minimum values are easily highlighted, we plotted graphical representation of the regression equations in order to explain interaction of the variables and locate the optimal level of each variable for maximal response. For the calculation of the regression coefficients, dispersion and correlation coefficients, the software Matlab was used.

NIMEFI: Gene Regulatory Network Inference using Multiple Ensemble Feature Importance Algorithms

PubMed Central

Ruyssinck, Joeri; Huynh-Thu, Vân Anh; Geurts, Pierre; Dhaene, Tom; Demeester, Piet; Saeys, Yvan

2014-01-01

One of the long-standing open challenges in computational systems biology is the topology inference of gene regulatory networks from high-throughput omics data. Recently, two community-wide efforts, DREAM4 and DREAM5, have been established to benchmark network inference techniques using gene expression measurements. In these challenges the overall top performer was the GENIE3 algorithm. This method decomposes the network inference task into separate regression problems for each gene in the network in which the expression values of a particular target gene are predicted using all other genes as possible predictors. Next, using tree-based ensemble methods, an importance measure for each predictor gene is calculated with respect to the target gene and a high feature importance is considered as putative evidence of a regulatory link existing between both genes. The contribution of this work is twofold. First, we generalize the regression decomposition strategy of GENIE3 to other feature importance methods. We compare the performance of support vector regression, the elastic net, random forest regression, symbolic regression and their ensemble variants in this setting to the original GENIE3 algorithm. To create the ensemble variants, we propose a subsampling approach which allows us to cast any feature selection algorithm that produces a feature ranking into an ensemble feature importance algorithm. We demonstrate that the ensemble setting is key to the network inference task, as only ensemble variants achieve top performance. As second contribution, we explore the effect of using rankwise averaged predictions of multiple ensemble algorithms as opposed to only one. We name this approach NIMEFI (Network Inference using Multiple Ensemble Feature Importance algorithms) and show that this approach outperforms all individual methods in general, although on a specific network a single method can perform better. An implementation of NIMEFI has been made publicly available. PMID:24667482

Individual risk factors for deep infection and compromised fracture healing after intramedullary nailing of tibial shaft fractures: a single centre experience of 480 patients.

PubMed

Metsemakers, W-J; Handojo, K; Reynders, P; Sermon, A; Vanderschot, P; Nijs, S

2015-04-01

Despite modern advances in the treatment of tibial shaft fractures, complications including nonunion, malunion, and infection remain relatively frequent. A better understanding of these injuries and its complications could lead to prevention rather than treatment strategies. A retrospective study was performed to identify risk factors for deep infection and compromised fracture healing after intramedullary nailing (IMN) of tibial shaft fractures. Between January 2000 and January 2012, 480 consecutive patients with 486 tibial shaft fractures were enrolled in the study. Statistical analysis was performed to determine predictors of deep infection and compromised fracture healing. Compromised fracture healing was subdivided in delayed union and nonunion. The following independent variables were selected for analysis: age, sex, smoking, obesity, diabetes, American Society of Anaesthesiologists (ASA) classification, polytrauma, fracture type, open fractures, Gustilo type, primary external fixation (EF), time to nailing (TTN) and reaming. As primary statistical evaluation we performed a univariate analysis, followed by a multiple logistic regression model. Univariate regression analysis revealed similar risk factors for delayed union and nonunion, including fracture type, open fractures and Gustilo type. Factors affecting the occurrence of deep infection in this model were primary EF, a prolonged TTN, open fractures and Gustilo type. Multiple logistic regression analysis revealed polytrauma as the single risk factor for nonunion. With respect to delayed union, no risk factors could be identified. In the same statistical model, deep infection was correlated with primary EF. The purpose of this study was to evaluate risk factors of poor outcome after IMN of tibial shaft fractures. The univariate regression analysis showed that the nature of complications after tibial shaft nailing could be multifactorial. This was not confirmed in a multiple logistic regression model, which only revealed polytrauma and primary EF as risk factors for nonunion and deep infection, respectively. Future strategies should focus on prevention in high-risk populations such as polytrauma patients treated with EF. Copyright © 2014 Elsevier Ltd. All rights reserved.

Real estate value prediction using multivariate regression models

NASA Astrophysics Data System (ADS)

Manjula, R.; Jain, Shubham; Srivastava, Sharad; Rajiv Kher, Pranav

2017-11-01

The real estate market is one of the most competitive in terms of pricing and the same tends to vary significantly based on a lot of factors, hence it becomes one of the prime fields to apply the concepts of machine learning to optimize and predict the prices with high accuracy. Therefore in this paper, we present various important features to use while predicting housing prices with good accuracy. We have described regression models, using various features to have lower Residual Sum of Squares error. While using features in a regression model some feature engineering is required for better prediction. Often a set of features (multiple regressions) or polynomial regression (applying a various set of powers in the features) is used for making better model fit. For these models are expected to be susceptible towards over fitting ridge regression is used to reduce it. This paper thus directs to the best application of regression models in addition to other techniques to optimize the result.