Statistical analysis of 59 inspected SSME HPFTP turbine blades (uncracked and cracked)

NASA Technical Reports Server (NTRS)

Wheeler, John T.

1987-01-01

The numerical results of statistical analysis of the test data of Space Shuttle Main Engine high pressure fuel turbopump second-stage turbine blades, including some with cracks are presented. Several statistical methods use the test data to determine the application of differences in frequency variations between the uncracked and cracked blades.

Advances in Statistical Methods for Substance Abuse Prevention Research

PubMed Central

MacKinnon, David P.; Lockwood, Chondra M.

2010-01-01

The paper describes advances in statistical methods for prevention research with a particular focus on substance abuse prevention. Standard analysis methods are extended to the typical research designs and characteristics of the data collected in prevention research. Prevention research often includes longitudinal measurement, clustering of data in units such as schools or clinics, missing data, and categorical as well as continuous outcome variables. Statistical methods to handle these features of prevention data are outlined. Developments in mediation, moderation, and implementation analysis allow for the extraction of more detailed information from a prevention study. Advancements in the interpretation of prevention research results include more widespread calculation of effect size and statistical power, the use of confidence intervals as well as hypothesis testing, detailed causal analysis of research findings, and meta-analysis. The increased availability of statistical software has contributed greatly to the use of new methods in prevention research. It is likely that the Internet will continue to stimulate the development and application of new methods. PMID:12940467

Online Statistical Modeling (Regression Analysis) for Independent Responses

NASA Astrophysics Data System (ADS)

Made Tirta, I.; Anggraeni, Dian; Pandutama, Martinus

2017-06-01

Regression analysis (statistical analmodelling) are among statistical methods which are frequently needed in analyzing quantitative data, especially to model relationship between response and explanatory variables. Nowadays, statistical models have been developed into various directions to model various type and complex relationship of data. Rich varieties of advanced and recent statistical modelling are mostly available on open source software (one of them is R). However, these advanced statistical modelling, are not very friendly to novice R users, since they are based on programming script or command line interface. Our research aims to developed web interface (based on R and shiny), so that most recent and advanced statistical modelling are readily available, accessible and applicable on web. We have previously made interface in the form of e-tutorial for several modern and advanced statistical modelling on R especially for independent responses (including linear models/LM, generalized linier models/GLM, generalized additive model/GAM and generalized additive model for location scale and shape/GAMLSS). In this research we unified them in the form of data analysis, including model using Computer Intensive Statistics (Bootstrap and Markov Chain Monte Carlo/ MCMC). All are readily accessible on our online Virtual Statistics Laboratory. The web (interface) make the statistical modeling becomes easier to apply and easier to compare them in order to find the most appropriate model for the data.

An overview of the mathematical and statistical analysis component of RICIS

NASA Technical Reports Server (NTRS)

Hallum, Cecil R.

1987-01-01

Mathematical and statistical analysis components of RICIS (Research Institute for Computing and Information Systems) can be used in the following problem areas: (1) quantification and measurement of software reliability; (2) assessment of changes in software reliability over time (reliability growth); (3) analysis of software-failure data; and (4) decision logic for whether to continue or stop testing software. Other areas of interest to NASA/JSC where mathematical and statistical analysis can be successfully employed include: math modeling of physical systems, simulation, statistical data reduction, evaluation methods, optimization, algorithm development, and mathematical methods in signal processing.

Some issues in the statistical analysis of vehicle emissions

DOT National Transportation Integrated Search

2000-09-01

Some of the issues complicating the statistical analysis of vehicle emissions and the effectiveness of emissions control programs are presented in this article. Issues discussed include: the variability of inter- and intra-vehicle emissions; the skew...

Statistical correlation of structural mode shapes from test measurements and NASTRAN analytical values

NASA Technical Reports Server (NTRS)

Purves, L.; Strang, R. F.; Dube, M. P.; Alea, P.; Ferragut, N.; Hershfeld, D.

1983-01-01

The software and procedures of a system of programs used to generate a report of the statistical correlation between NASTRAN modal analysis results and physical tests results from modal surveys are described. Topics discussed include: a mathematical description of statistical correlation, a user's guide for generating a statistical correlation report, a programmer's guide describing the organization and functions of individual programs leading to a statistical correlation report, and a set of examples including complete listings of programs, and input and output data.

Reporting quality of statistical methods in surgical observational studies: protocol for systematic review.

PubMed

Wu, Robert; Glen, Peter; Ramsay, Tim; Martel, Guillaume

2014-06-28

Observational studies dominate the surgical literature. Statistical adjustment is an important strategy to account for confounders in observational studies. Research has shown that published articles are often poor in statistical quality, which may jeopardize their conclusions. The Statistical Analyses and Methods in the Published Literature (SAMPL) guidelines have been published to help establish standards for statistical reporting.This study will seek to determine whether the quality of statistical adjustment and the reporting of these methods are adequate in surgical observational studies. We hypothesize that incomplete reporting will be found in all surgical observational studies, and that the quality and reporting of these methods will be of lower quality in surgical journals when compared with medical journals. Finally, this work will seek to identify predictors of high-quality reporting. This work will examine the top five general surgical and medical journals, based on a 5-year impact factor (2007-2012). All observational studies investigating an intervention related to an essential component area of general surgery (defined by the American Board of Surgery), with an exposure, outcome, and comparator, will be included in this systematic review. Essential elements related to statistical reporting and quality were extracted from the SAMPL guidelines and include domains such as intent of analysis, primary analysis, multiple comparisons, numbers and descriptive statistics, association and correlation analyses, linear regression, logistic regression, Cox proportional hazard analysis, analysis of variance, survival analysis, propensity analysis, and independent and correlated analyses. Each article will be scored as a proportion based on fulfilling criteria in relevant analyses used in the study. A logistic regression model will be built to identify variables associated with high-quality reporting. A comparison will be made between the scores of surgical observational studies published in medical versus surgical journals. Secondary outcomes will pertain to individual domains of analysis. Sensitivity analyses will be conducted. This study will explore the reporting and quality of statistical analyses in surgical observational studies published in the most referenced surgical and medical journals in 2013 and examine whether variables (including the type of journal) can predict high-quality reporting.

Limitations of Using Microsoft Excel Version 2016 (MS Excel 2016) for Statistical Analysis for Medical Research.

PubMed

Tanavalee, Chotetawan; Luksanapruksa, Panya; Singhatanadgige, Weerasak

2016-06-01

Microsoft Excel (MS Excel) is a commonly used program for data collection and statistical analysis in biomedical research. However, this program has many limitations, including fewer functions that can be used for analysis and a limited number of total cells compared with dedicated statistical programs. MS Excel cannot complete analyses with blank cells, and cells must be selected manually for analysis. In addition, it requires multiple steps of data transformation and formulas to plot survival analysis graphs, among others. The Megastat add-on program, which will be supported by MS Excel 2016 soon, would eliminate some limitations of using statistic formulas within MS Excel.

Comparing Methods for Item Analysis: The Impact of Different Item-Selection Statistics on Test Difficulty

ERIC Educational Resources Information Center

Jones, Andrew T.

2011-01-01

Practitioners often depend on item analysis to select items for exam forms and have a variety of options available to them. These include the point-biserial correlation, the agreement statistic, the B index, and the phi coefficient. Although research has demonstrated that these statistics can be useful for item selection, no research as of yet has…

Local sensitivity analysis for inverse problems solved by singular value decomposition

USGS Publications Warehouse

Hill, M.C.; Nolan, B.T.

2010-01-01

Local sensitivity analysis provides computationally frugal ways to evaluate models commonly used for resource management, risk assessment, and so on. This includes diagnosing inverse model convergence problems caused by parameter insensitivity and(or) parameter interdependence (correlation), understanding what aspects of the model and data contribute to measures of uncertainty, and identifying new data likely to reduce model uncertainty. Here, we consider sensitivity statistics relevant to models in which the process model parameters are transformed using singular value decomposition (SVD) to create SVD parameters for model calibration. The statistics considered include the PEST identifiability statistic, and combined use of the process-model parameter statistics composite scaled sensitivities and parameter correlation coefficients (CSS and PCC). The statistics are complimentary in that the identifiability statistic integrates the effects of parameter sensitivity and interdependence, while CSS and PCC provide individual measures of sensitivity and interdependence. PCC quantifies correlations between pairs or larger sets of parameters; when a set of parameters is intercorrelated, the absolute value of PCC is close to 1.00 for all pairs in the set. The number of singular vectors to include in the calculation of the identifiability statistic is somewhat subjective and influences the statistic. To demonstrate the statistics, we use the USDA’s Root Zone Water Quality Model to simulate nitrogen fate and transport in the unsaturated zone of the Merced River Basin, CA. There are 16 log-transformed process-model parameters, including water content at field capacity (WFC) and bulk density (BD) for each of five soil layers. Calibration data consisted of 1,670 observations comprising soil moisture, soil water tension, aqueous nitrate and bromide concentrations, soil nitrate concentration, and organic matter content. All 16 of the SVD parameters could be estimated by regression based on the range of singular values. Identifiability statistic results varied based on the number of SVD parameters included. Identifiability statistics calculated for four SVD parameters indicate the same three most important process-model parameters as CSS/PCC (WFC1, WFC2, and BD2), but the order differed. Additionally, the identifiability statistic showed that BD1 was almost as dominant as WFC1. The CSS/PCC analysis showed that this results from its high correlation with WCF1 (-0.94), and not its individual sensitivity. Such distinctions, combined with analysis of how high correlations and(or) sensitivities result from the constructed model, can produce important insights into, for example, the use of sensitivity analysis to design monitoring networks. In conclusion, the statistics considered identified similar important parameters. They differ because (1) with CSS/PCC can be more awkward because sensitivity and interdependence are considered separately and (2) identifiability requires consideration of how many SVD parameters to include. A continuing challenge is to understand how these computationally efficient methods compare with computationally demanding global methods like Markov-Chain Monte Carlo given common nonlinear processes and the often even more nonlinear models.

A new u-statistic with superior design sensitivity in matched observational studies.

PubMed

Rosenbaum, Paul R

2011-09-01

In an observational or nonrandomized study of treatment effects, a sensitivity analysis indicates the magnitude of bias from unmeasured covariates that would need to be present to alter the conclusions of a naïve analysis that presumes adjustments for observed covariates suffice to remove all bias. The power of sensitivity analysis is the probability that it will reject a false hypothesis about treatment effects allowing for a departure from random assignment of a specified magnitude; in particular, if this specified magnitude is "no departure" then this is the same as the power of a randomization test in a randomized experiment. A new family of u-statistics is proposed that includes Wilcoxon's signed rank statistic but also includes other statistics with substantially higher power when a sensitivity analysis is performed in an observational study. Wilcoxon's statistic has high power to detect small effects in large randomized experiments-that is, it often has good Pitman efficiency-but small effects are invariably sensitive to small unobserved biases. Members of this family of u-statistics that emphasize medium to large effects can have substantially higher power in a sensitivity analysis. For example, in one situation with 250 pair differences that are Normal with expectation 1/2 and variance 1, the power of a sensitivity analysis that uses Wilcoxon's statistic is 0.08 while the power of another member of the family of u-statistics is 0.66. The topic is examined by performing a sensitivity analysis in three observational studies, using an asymptotic measure called the design sensitivity, and by simulating power in finite samples. The three examples are drawn from epidemiology, clinical medicine, and genetic toxicology. © 2010, The International Biometric Society.

Performance Analysis of Live-Virtual-Constructive and Distributed Virtual Simulations: Defining Requirements in Terms of Temporal Consistency

DTIC Science & Technology

2009-12-01

events. Work associated with aperiodic tasks have the same statistical behavior and the same timing requirements. The timing deadlines are soft. • Sporadic...answers, but it is possible to calculate how precise the estimates are. Simulation-based performance analysis of a model includes a statistical ...to evaluate all pos- sible states in a timely manner. This is the principle reason for resorting to simulation and statistical analysis to evaluate

Data analysis techniques

NASA Technical Reports Server (NTRS)

Park, Steve

1990-01-01

A large and diverse number of computational techniques are routinely used to process and analyze remotely sensed data. These techniques include: univariate statistics; multivariate statistics; principal component analysis; pattern recognition and classification; other multivariate techniques; geometric correction; registration and resampling; radiometric correction; enhancement; restoration; Fourier analysis; and filtering. Each of these techniques will be considered, in order.

Research Education in Undergraduate Occupational Therapy Programs.

ERIC Educational Resources Information Center

Petersen, Paul; And Others

1992-01-01

Of 63 undergraduate occupational therapy programs surveyed, the 38 responses revealed some common areas covered: elementary descriptive statistics, validity, reliability, and measurement. Areas underrepresented include statistical analysis with or without computers, research design, and advanced statistics. (SK)

Surface flaw reliability analysis of ceramic components with the SCARE finite element postprocessor program

NASA Technical Reports Server (NTRS)

Gyekenyesi, John P.; Nemeth, Noel N.

1987-01-01

The SCARE (Structural Ceramics Analysis and Reliability Evaluation) computer program on statistical fast fracture reliability analysis with quadratic elements for volume distributed imperfections is enhanced to include the use of linear finite elements and the capability of designing against concurrent surface flaw induced ceramic component failure. The SCARE code is presently coupled as a postprocessor to the MSC/NASTRAN general purpose, finite element analysis program. The improved version now includes the Weibull and Batdorf statistical failure theories for both surface and volume flaw based reliability analysis. The program uses the two-parameter Weibull fracture strength cumulative failure probability distribution model with the principle of independent action for poly-axial stress states, and Batdorf's shear-sensitive as well as shear-insensitive statistical theories. The shear-sensitive surface crack configurations include the Griffith crack and Griffith notch geometries, using the total critical coplanar strain energy release rate criterion to predict mixed-mode fracture. Weibull material parameters based on both surface and volume flaw induced fracture can also be calculated from modulus of rupture bar tests, using the least squares method with known specimen geometry and grouped fracture data. The statistical fast fracture theories for surface flaw induced failure, along with selected input and output formats and options, are summarized. An example problem to demonstrate various features of the program is included.

Prison Radicalization: The New Extremist Training Grounds?

DTIC Science & Technology

2007-09-01

distributing and collecting survey data , and the data analysis. The analytical methodology includes descriptive and inferential statistical methods, in... statistical analysis of the responses to identify significant correlations and relationships. B. SURVEY DATA COLLECTION To effectively access a...Q18, Q19, Q20, and Q21. Due to the exploratory nature of this small survey, data analyses were confined mostly to descriptive statistics and

Proceedings of the first ERDA statistical symposium, Los Alamos, NM, November 3--5, 1975. [Sixteen papers

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

Nicholson, W L; Harris, J L

1976-03-01

The First ERDA Statistical Symposium was organized to provide a means for communication among ERDA statisticians, and the sixteen papers presented at the meeting are given. Topics include techniques of numerical analysis used for accelerators, nuclear reactors, skewness and kurtosis statistics, radiochemical spectral analysis, quality control, and other statistics problems. Nine of the papers were previously announced in Nuclear Science Abstracts (NSA), while the remaining seven were abstracted for ERDA Energy Research Abstracts (ERA) and INIS Atomindex. (PMA)

Bootstrap Methods: A Very Leisurely Look.

ERIC Educational Resources Information Center

Hinkle, Dennis E.; Winstead, Wayland H.

The Bootstrap method, a computer-intensive statistical method of estimation, is illustrated using a simple and efficient Statistical Analysis System (SAS) routine. The utility of the method for generating unknown parameters, including standard errors for simple statistics, regression coefficients, discriminant function coefficients, and factor…

Examples of Data Analysis with SPSS-X.

ERIC Educational Resources Information Center

MacFarland, Thomas W.

Intended for classroom use only, these unpublished notes contain computer lessons on descriptive statistics using SPSS-X Release 3.0 for VAX/UNIX. Statistical measures covered include Chi-square analysis; Spearman's rank correlation coefficient; Student's t-test with two independent samples; Student's t-test with a paired sample; One-way analysis…

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…

Statistics for nuclear engineers and scientists. Part 1. Basic statistical inference

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

Beggs, W.J.

1981-02-01

This report is intended for the use of engineers and scientists working in the nuclear industry, especially at the Bettis Atomic Power Laboratory. It serves as the basis for several Bettis in-house statistics courses. The objectives of the report are to introduce the reader to the language and concepts of statistics and to provide a basic set of techniques to apply to problems of the collection and analysis of data. Part 1 covers subjects of basic inference. The subjects include: descriptive statistics; probability; simple inference for normally distributed populations, and for non-normal populations as well; comparison of two populations; themore » analysis of variance; quality control procedures; and linear regression analysis.« less

Sigsearch: a new term for post hoc unplanned search for statistically significant relationships with the intent to create publishable findings.

PubMed

Hashim, Muhammad Jawad

2010-09-01

Post-hoc secondary data analysis with no prespecified hypotheses has been discouraged by textbook authors and journal editors alike. Unfortunately no single term describes this phenomenon succinctly. I would like to coin the term "sigsearch" to define this practice and bring it within the teaching lexicon of statistics courses. Sigsearch would include any unplanned, post-hoc search for statistical significance using multiple comparisons of subgroups. It would also include data analysis with outcomes other than the prespecified primary outcome measure of a study as well as secondary data analyses of earlier research.

New dimensions from statistical graphics for GIS (geographic information system) analysis and interpretation

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

McCord, R.A.; Olson, R.J.

1988-01-01

Environmental research and assessment activities at Oak Ridge National Laboratory (ORNL) include the analysis of spatial and temporal patterns of ecosystem response at a landscape scale. Analysis through use of geographic information system (GIS) involves an interaction between the user and thematic data sets frequently expressed as maps. A portion of GIS analysis has a mathematical or statistical aspect, especially for the analysis of temporal patterns. ARC/INFO is an excellent tool for manipulating GIS data and producing the appropriate map graphics. INFO also has some limited ability to produce statistical tabulation. At ORNL we have extended our capabilities by graphicallymore » interfacing ARC/INFO and SAS/GRAPH to provide a combined mapping and statistical graphics environment. With the data management, statistical, and graphics capabilities of SAS added to ARC/INFO, we have expanded the analytical and graphical dimensions of the GIS environment. Pie or bar charts, frequency curves, hydrographs, or scatter plots as produced by SAS can be added to maps from attribute data associated with ARC/INFO coverages. Numerous, small, simplified graphs can also become a source of complex map ''symbols.'' These additions extend the dimensions of GIS graphics to include time, details of the thematic composition, distribution, and interrelationships. 7 refs., 3 figs.« less

Teaching statistics in biology: using inquiry-based learning to strengthen understanding of statistical analysis in biology laboratory courses.

PubMed

Metz, Anneke M

2008-01-01

There is an increasing need for students in the biological sciences to build a strong foundation in quantitative approaches to data analyses. Although most science, engineering, and math field majors are required to take at least one statistics course, statistical analysis is poorly integrated into undergraduate biology course work, particularly at the lower-division level. Elements of statistics were incorporated into an introductory biology course, including a review of statistics concepts and opportunity for students to perform statistical analysis in a biological context. Learning gains were measured with an 11-item statistics learning survey instrument developed for the course. Students showed a statistically significant 25% (p < 0.005) increase in statistics knowledge after completing introductory biology. Students improved their scores on the survey after completing introductory biology, even if they had previously completed an introductory statistics course (9%, improvement p < 0.005). Students retested 1 yr after completing introductory biology showed no loss of their statistics knowledge as measured by this instrument, suggesting that the use of statistics in biology course work may aid long-term retention of statistics knowledge. No statistically significant differences in learning were detected between male and female students in the study.

World Population: Facts in Focus. World Population Data Sheet Workbook. Population Learning Series.

ERIC Educational Resources Information Center

Crews, Kimberly A.

This workbook teaches population analysis using world population statistics. To complete the four student activity sheets, the students refer to the included "1988 World Population Data Sheet" which lists nations' statistical data that includes population totals, projected population, birth and death rates, fertility levels, and the…

Paleomagnetism.org: An online multi-platform open source environment for paleomagnetic data analysis

NASA Astrophysics Data System (ADS)

Koymans, Mathijs R.; Langereis, Cor G.; Pastor-Galán, Daniel; van Hinsbergen, Douwe J. J.

2016-08-01

This contribution provides an overview of Paleomagnetism.org, an open-source, multi-platform online environment for paleomagnetic data analysis. Paleomagnetism.org provides an interactive environment where paleomagnetic data can be interpreted, evaluated, visualized, and exported. The Paleomagnetism.org application is split in to an interpretation portal, a statistics portal, and a portal for miscellaneous paleomagnetic tools. In the interpretation portal, principle component analysis can be performed on visualized demagnetization diagrams. Interpreted directions and great circles can be combined to find great circle solutions. These directions can be used in the statistics portal, or exported as data and figures. The tools in the statistics portal cover standard Fisher statistics for directions and VGPs, including other statistical parameters used as reliability criteria. Other available tools include an eigenvector approach foldtest, two reversal test including a Monte Carlo simulation on mean directions, and a coordinate bootstrap on the original data. An implementation is included for the detection and correction of inclination shallowing in sediments following TK03.GAD. Finally we provide a module to visualize VGPs and expected paleolatitudes, declinations, and inclinations relative to widely used global apparent polar wander path models in coordinates of major continent-bearing plates. The tools in the miscellaneous portal include a net tectonic rotation (NTR) analysis to restore a body to its paleo-vertical and a bootstrapped oroclinal test using linear regressive techniques, including a modified foldtest around a vertical axis. Paleomagnetism.org provides an integrated approach for researchers to work with visualized (e.g. hemisphere projections, Zijderveld diagrams) paleomagnetic data. The application constructs a custom exportable file that can be shared freely and included in public databases. This exported file contains all data and can later be imported to the application by other researchers. The accessibility and simplicity through which paleomagnetic data can be interpreted, analyzed, visualized, and shared makes Paleomagnetism.org of interest to the community.

Statistical Approaches Used to Assess the Equity of Access to Food Outlets: A Systematic Review

PubMed Central

Lamb, Karen E.; Thornton, Lukar E.; Cerin, Ester; Ball, Kylie

2015-01-01

Background Inequalities in eating behaviours are often linked to the types of food retailers accessible in neighbourhood environments. Numerous studies have aimed to identify if access to healthy and unhealthy food retailers is socioeconomically patterned across neighbourhoods, and thus a potential risk factor for dietary inequalities. Existing reviews have examined differences between methodologies, particularly focussing on neighbourhood and food outlet access measure definitions. However, no review has informatively discussed the suitability of the statistical methodologies employed; a key issue determining the validity of study findings. Our aim was to examine the suitability of statistical approaches adopted in these analyses. Methods Searches were conducted for articles published from 2000–2014. Eligible studies included objective measures of the neighbourhood food environment and neighbourhood-level socio-economic status, with a statistical analysis of the association between food outlet access and socio-economic status. Results Fifty-four papers were included. Outlet accessibility was typically defined as the distance to the nearest outlet from the neighbourhood centroid, or as the number of food outlets within a neighbourhood (or buffer). To assess if these measures were linked to neighbourhood disadvantage, common statistical methods included ANOVA, correlation, and Poisson or negative binomial regression. Although all studies involved spatial data, few considered spatial analysis techniques or spatial autocorrelation. Conclusions With advances in GIS software, sophisticated measures of neighbourhood outlet accessibility can be considered. However, approaches to statistical analysis often appear less sophisticated. Care should be taken to consider assumptions underlying the analysis and the possibility of spatially correlated residuals which could affect the results. PMID:29546115

The Statistical Consulting Center for Astronomy (SCCA)

NASA Technical Reports Server (NTRS)

Akritas, Michael

2001-01-01

The process by which raw astronomical data acquisition is transformed into scientifically meaningful results and interpretation typically involves many statistical steps. Traditional astronomy limits itself to a narrow range of old and familiar statistical methods: means and standard deviations; least-squares methods like chi(sup 2) minimization; and simple nonparametric procedures such as the Kolmogorov-Smirnov tests. These tools are often inadequate for the complex problems and datasets under investigations, and recent years have witnessed an increased usage of maximum-likelihood, survival analysis, multivariate analysis, wavelet and advanced time-series methods. The Statistical Consulting Center for Astronomy (SCCA) assisted astronomers with the use of sophisticated tools, and to match these tools with specific problems. The SCCA operated with two professors of statistics and a professor of astronomy working together. Questions were received by e-mail, and were discussed in detail with the questioner. Summaries of those questions and answers leading to new approaches were posted on the Web (www.state.psu.edu/ mga/SCCA). In addition to serving individual astronomers, the SCCA established a Web site for general use that provides hypertext links to selected on-line public-domain statistical software and services. The StatCodes site (www.astro.psu.edu/statcodes) provides over 200 links in the areas of: Bayesian statistics; censored and truncated data; correlation and regression, density estimation and smoothing, general statistics packages and information; image analysis; interactive Web tools; multivariate analysis; multivariate clustering and classification; nonparametric analysis; software written by astronomers; spatial statistics; statistical distributions; time series analysis; and visualization tools. StatCodes has received a remarkable high and constant hit rate of 250 hits/week (over 10,000/year) since its inception in mid-1997. It is of interest to scientists both within and outside of astronomy. The most popular sections are multivariate techniques, image analysis, and time series analysis. Hundreds of copies of the ASURV, SLOPES and CENS-TAU codes developed by SCCA scientists were also downloaded from the StatCodes site. In addition to formal SCCA duties, SCCA scientists continued a variety of related activities in astrostatistics, including refereeing of statistically oriented papers submitted to the Astrophysical Journal, talks in meetings including Feigelson's talk to science journalists entitled "The reemergence of astrostatistics" at the American Association for the Advancement of Science meeting, and published papers of astrostatistical content.

Uncertainty Analysis of Seebeck Coefficient and Electrical Resistivity Characterization

NASA Technical Reports Server (NTRS)

Mackey, Jon; Sehirlioglu, Alp; Dynys, Fred

2014-01-01

In order to provide a complete description of a materials thermoelectric power factor, in addition to the measured nominal value, an uncertainty interval is required. The uncertainty may contain sources of measurement error including systematic bias error and precision error of a statistical nature. The work focuses specifically on the popular ZEM-3 (Ulvac Technologies) measurement system, but the methods apply to any measurement system. The analysis accounts for sources of systematic error including sample preparation tolerance, measurement probe placement, thermocouple cold-finger effect, and measurement parameters; in addition to including uncertainty of a statistical nature. Complete uncertainty analysis of a measurement system allows for more reliable comparison of measurement data between laboratories.

Students' Appreciation of Expectation and Variation as a Foundation for Statistical Understanding

ERIC Educational Resources Information Center

Watson, Jane M.; Callingham, Rosemary A.; Kelly, Ben A.

2007-01-01

This study presents the results of a partial credit Rasch analysis of in-depth interview data exploring statistical understanding of 73 school students in 6 contextual settings. The use of Rasch analysis allowed the exploration of a single underlying variable across contexts, which included probability sampling, representation of temperature…

AN EMPIRICAL BAYES APPROACH TO COMBINING ESTIMATES OF THE VALUE OF A STATISTICAL LIFE FOR ENVIRONMENTAL POLICY ANALYSIS

EPA Science Inventory

This analysis updates EPA's standard VSL estimate by using a more comprehensive collection of VSL studies that include studies published between 1992 and 2000, as well as applying a more appropriate statistical method. We provide a pooled effect VSL estimate by applying the empi...

[Statistical analysis using freely-available "EZR (Easy R)" software].

PubMed

Kanda, Yoshinobu

2015-10-01

Clinicians must often perform statistical analyses for purposes such evaluating preexisting evidence and designing or executing clinical studies. R is a free software environment for statistical computing. R supports many statistical analysis functions, but does not incorporate a statistical graphical user interface (GUI). The R commander provides an easy-to-use basic-statistics GUI for R. However, the statistical function of the R commander is limited, especially in the field of biostatistics. Therefore, the author added several important statistical functions to the R commander and named it "EZR (Easy R)", which is now being distributed on the following website: http://www.jichi.ac.jp/saitama-sct/. EZR allows the application of statistical functions that are frequently used in clinical studies, such as survival analyses, including competing risk analyses and the use of time-dependent covariates and so on, by point-and-click access. In addition, by saving the script automatically created by EZR, users can learn R script writing, maintain the traceability of the analysis, and assure that the statistical process is overseen by a supervisor.

Cognition of and Demand for Education and Teaching in Medical Statistics in China: A Systematic Review and Meta-Analysis

PubMed Central

Li, Gaoming; Yi, Dali; Wu, Xiaojiao; Liu, Xiaoyu; Zhang, Yanqi; Liu, Ling; Yi, Dong

2015-01-01

Background Although a substantial number of studies focus on the teaching and application of medical statistics in China, few studies comprehensively evaluate the recognition of and demand for medical statistics. In addition, the results of these various studies differ and are insufficiently comprehensive and systematic. Objectives This investigation aimed to evaluate the general cognition of and demand for medical statistics by undergraduates, graduates, and medical staff in China. Methods We performed a comprehensive database search related to the cognition of and demand for medical statistics from January 2007 to July 2014 and conducted a meta-analysis of non-controlled studies with sub-group analysis for undergraduates, graduates, and medical staff. Results There are substantial differences with respect to the cognition of theory in medical statistics among undergraduates (73.5%), graduates (60.7%), and medical staff (39.6%). The demand for theory in medical statistics is high among graduates (94.6%), undergraduates (86.1%), and medical staff (88.3%). Regarding specific statistical methods, the cognition of basic statistical methods is higher than of advanced statistical methods. The demand for certain advanced statistical methods, including (but not limited to) multiple analysis of variance (ANOVA), multiple linear regression, and logistic regression, is higher than that for basic statistical methods. The use rates of the Statistical Package for the Social Sciences (SPSS) software and statistical analysis software (SAS) are only 55% and 15%, respectively. Conclusion The overall statistical competence of undergraduates, graduates, and medical staff is insufficient, and their ability to practically apply their statistical knowledge is limited, which constitutes an unsatisfactory state of affairs for medical statistics education. Because the demand for skills in this area is increasing, the need to reform medical statistics education in China has become urgent. PMID:26053876

Cognition of and Demand for Education and Teaching in Medical Statistics in China: A Systematic Review and Meta-Analysis.

PubMed

Wu, Yazhou; Zhou, Liang; Li, Gaoming; Yi, Dali; Wu, Xiaojiao; Liu, Xiaoyu; Zhang, Yanqi; Liu, Ling; Yi, Dong

2015-01-01

Although a substantial number of studies focus on the teaching and application of medical statistics in China, few studies comprehensively evaluate the recognition of and demand for medical statistics. In addition, the results of these various studies differ and are insufficiently comprehensive and systematic. This investigation aimed to evaluate the general cognition of and demand for medical statistics by undergraduates, graduates, and medical staff in China. We performed a comprehensive database search related to the cognition of and demand for medical statistics from January 2007 to July 2014 and conducted a meta-analysis of non-controlled studies with sub-group analysis for undergraduates, graduates, and medical staff. There are substantial differences with respect to the cognition of theory in medical statistics among undergraduates (73.5%), graduates (60.7%), and medical staff (39.6%). The demand for theory in medical statistics is high among graduates (94.6%), undergraduates (86.1%), and medical staff (88.3%). Regarding specific statistical methods, the cognition of basic statistical methods is higher than of advanced statistical methods. The demand for certain advanced statistical methods, including (but not limited to) multiple analysis of variance (ANOVA), multiple linear regression, and logistic regression, is higher than that for basic statistical methods. The use rates of the Statistical Package for the Social Sciences (SPSS) software and statistical analysis software (SAS) are only 55% and 15%, respectively. The overall statistical competence of undergraduates, graduates, and medical staff is insufficient, and their ability to practically apply their statistical knowledge is limited, which constitutes an unsatisfactory state of affairs for medical statistics education. Because the demand for skills in this area is increasing, the need to reform medical statistics education in China has become urgent.

Mild cognitive impairment and fMRI studies of brain functional connectivity: the state of the art

PubMed Central

Farràs-Permanyer, Laia; Guàrdia-Olmos, Joan; Peró-Cebollero, Maribel

2015-01-01

In the last 15 years, many articles have studied brain connectivity in Mild Cognitive Impairment patients with fMRI techniques, seemingly using different connectivity statistical models in each investigation to identify complex connectivity structures so as to recognize typical behavior in this type of patient. This diversity in statistical approaches may cause problems in results comparison. This paper seeks to describe how researchers approached the study of brain connectivity in MCI patients using fMRI techniques from 2002 to 2014. The focus is on the statistical analysis proposed by each research group in reference to the limitations and possibilities of those techniques to identify some recommendations to improve the study of functional connectivity. The included articles came from a search of Web of Science and PsycINFO using the following keywords: f MRI, MCI, and functional connectivity. Eighty-one papers were found, but two of them were discarded because of the lack of statistical analysis. Accordingly, 79 articles were included in this review. We summarized some parts of the articles, including the goal of every investigation, the cognitive paradigm and methods used, brain regions involved, use of ROI analysis and statistical analysis, emphasizing on the connectivity estimation model used in each investigation. The present analysis allowed us to confirm the remarkable variability of the statistical analysis methods found. Additionally, the study of brain connectivity in this type of population is not providing, at the moment, any significant information or results related to clinical aspects relevant for prediction and treatment. We propose to follow guidelines for publishing fMRI data that would be a good solution to the problem of study replication. The latter aspect could be important for future publications because a higher homogeneity would benefit the comparison between publications and the generalization of results. PMID:26300802

Kansas's forests, 2005: statistics, methods, and quality assurance

Treesearch

Patrick D. Miles; W. Keith Moser; Charles J. Barnett

2011-01-01

The first full annual inventory of Kansas's forests was completed in 2005 after 8,868 plots were selected and 468 forested plots were visited and measured. This report includes detailed information on forest inventory methods and data quality estimates. Important resource statistics are included in the tables. A detailed analysis of Kansas inventory is presented...

Advance Report of Final Mortality Statistics, 1985.

ERIC Educational Resources Information Center

Monthly Vital Statistics Report, 1987

1987-01-01

This document presents mortality statistics for 1985 for the entire United States. Data analysis and discussion of these factors is included: death and death rates; death rates by age, sex, and race; expectation of life at birth and at specified ages; causes of death; infant mortality; and maternal mortality. Highlights reported include: (1) the…

34 CFR Appendix A to Subpart N of... - Sample Default Prevention Plan

Code of Federal Regulations, 2010 CFR

2010-07-01

... relevant default prevention statistics, including a statistical analysis of the borrowers who default on...'s delinquency status by obtaining reports from data managers and FFEL Program lenders. 5. Enhance... academic study. III. Statistics for Measuring Progress 1. The number of students enrolled at your...

The Effect of Folate and Folate Plus Zinc Supplementation on Endocrine Parameters and Sperm Characteristics in Sub-Fertile Men: A Systematic Review and Meta-Analysis.

PubMed

Irani, Morvarid; Amirian, Malihe; Sadeghi, Ramin; Lez, Justine Le; Latifnejad Roudsari, Robab

2017-08-29

To evaluate the effect of folate and folate plus zinc supplementation on endocrine parameters and sperm characteristics in sub fertile men. We conducted a systematic review and meta-analysis. Electronic databases of Medline, Scopus , Google scholar and Persian databases (SID, Iran medex, Magiran, Medlib, Iran doc) were searched from 1966 to December 2016 using a set of relevant keywords including "folate or folic acid AND (infertility, infertile, sterility)".All available randomized controlled trials (RCTs), conducted on a sample of sub fertile men with semen analyses, who took oral folic acid or folate plus zinc, were included. Data collected included endocrine parameters and sperm characteristics. Statistical analyses were done by Comprehensive Meta-analysis Version 2. In total, seven studies were included. Six studies had sufficient data for meta-analysis. "Sperm concentration was statistically higher in men supplemented with folate than with placebo (P < .001)". However, folate supplementation alone did not seem to be more effective than the placebo on the morphology (P = .056) and motility of the sperms (P = .652). Folate plus zinc supplementation did not show any statistically different effect on serum testosterone (P = .86), inhibin B (P = .84), FSH (P = .054), and sperm motility (P = .169) as compared to the placebo. Yet, folate plus zinc showed statistically higher effect on the sperm concentration (P < .001), morphology (P < .001), and serum folate level (P < .001) as compared to placebo. Folate plus zinc supplementation has a positive effect on sperm characteristics in sub fertile men. However, these results should be interpreted with caution due to the important heterogeneity of the studies included in this meta-analysis. Further trials are still needed to confirm the current findings.

Teaching Statistics in Biology: Using Inquiry-based Learning to Strengthen Understanding of Statistical Analysis in Biology Laboratory Courses

PubMed Central

2008-01-01

There is an increasing need for students in the biological sciences to build a strong foundation in quantitative approaches to data analyses. Although most science, engineering, and math field majors are required to take at least one statistics course, statistical analysis is poorly integrated into undergraduate biology course work, particularly at the lower-division level. Elements of statistics were incorporated into an introductory biology course, including a review of statistics concepts and opportunity for students to perform statistical analysis in a biological context. Learning gains were measured with an 11-item statistics learning survey instrument developed for the course. Students showed a statistically significant 25% (p < 0.005) increase in statistics knowledge after completing introductory biology. Students improved their scores on the survey after completing introductory biology, even if they had previously completed an introductory statistics course (9%, improvement p < 0.005). Students retested 1 yr after completing introductory biology showed no loss of their statistics knowledge as measured by this instrument, suggesting that the use of statistics in biology course work may aid long-term retention of statistics knowledge. No statistically significant differences in learning were detected between male and female students in the study. PMID:18765754

Dealing with missing standard deviation and mean values in meta-analysis of continuous outcomes: a systematic review.

PubMed

Weir, Christopher J; Butcher, Isabella; Assi, Valentina; Lewis, Stephanie C; Murray, Gordon D; Langhorne, Peter; Brady, Marian C

2018-03-07

Rigorous, informative meta-analyses rely on availability of appropriate summary statistics or individual participant data. For continuous outcomes, especially those with naturally skewed distributions, summary information on the mean or variability often goes unreported. While full reporting of original trial data is the ideal, we sought to identify methods for handling unreported mean or variability summary statistics in meta-analysis. We undertook two systematic literature reviews to identify methodological approaches used to deal with missing mean or variability summary statistics. Five electronic databases were searched, in addition to the Cochrane Colloquium abstract books and the Cochrane Statistics Methods Group mailing list archive. We also conducted cited reference searching and emailed topic experts to identify recent methodological developments. Details recorded included the description of the method, the information required to implement the method, any underlying assumptions and whether the method could be readily applied in standard statistical software. We provided a summary description of the methods identified, illustrating selected methods in example meta-analysis scenarios. For missing standard deviations (SDs), following screening of 503 articles, fifteen methods were identified in addition to those reported in a previous review. These included Bayesian hierarchical modelling at the meta-analysis level; summary statistic level imputation based on observed SD values from other trials in the meta-analysis; a practical approximation based on the range; and algebraic estimation of the SD based on other summary statistics. Following screening of 1124 articles for methods estimating the mean, one approximate Bayesian computation approach and three papers based on alternative summary statistics were identified. Illustrative meta-analyses showed that when replacing a missing SD the approximation using the range minimised loss of precision and generally performed better than omitting trials. When estimating missing means, a formula using the median, lower quartile and upper quartile performed best in preserving the precision of the meta-analysis findings, although in some scenarios, omitting trials gave superior results. Methods based on summary statistics (minimum, maximum, lower quartile, upper quartile, median) reported in the literature facilitate more comprehensive inclusion of randomised controlled trials with missing mean or variability summary statistics within meta-analyses.

Taxation in Public Education. Analysis and Bibliography Series, No. 12.

ERIC Educational Resources Information Center

Ross, Larry L.

Intended for both researchers and practitioners, this analysis and bibliography cites approximately 100 publications on educational taxation, including general texts and reports, statistical reports, taxation guidelines, and alternative proposals for taxation. Topics covered in the analysis section include State and Federal aid, urban and suburban…

Statistical models for the analysis and design of digital polymerase chain (dPCR) experiments

USGS Publications Warehouse

Dorazio, Robert; Hunter, Margaret

2015-01-01

Statistical methods for the analysis and design of experiments using digital PCR (dPCR) have received only limited attention and have been misused in many instances. To address this issue and to provide a more general approach to the analysis of dPCR data, we describe a class of statistical models for the analysis and design of experiments that require quantification of nucleic acids. These models are mathematically equivalent to generalized linear models of binomial responses that include a complementary, log–log link function and an offset that is dependent on the dPCR partition volume. These models are both versatile and easy to fit using conventional statistical software. Covariates can be used to specify different sources of variation in nucleic acid concentration, and a model’s parameters can be used to quantify the effects of these covariates. For purposes of illustration, we analyzed dPCR data from different types of experiments, including serial dilution, evaluation of copy number variation, and quantification of gene expression. We also showed how these models can be used to help design dPCR experiments, as in selection of sample sizes needed to achieve desired levels of precision in estimates of nucleic acid concentration or to detect differences in concentration among treatments with prescribed levels of statistical power.

Statistical Models for the Analysis and Design of Digital Polymerase Chain Reaction (dPCR) Experiments.

PubMed

Dorazio, Robert M; Hunter, Margaret E

2015-11-03

Statistical methods for the analysis and design of experiments using digital PCR (dPCR) have received only limited attention and have been misused in many instances. To address this issue and to provide a more general approach to the analysis of dPCR data, we describe a class of statistical models for the analysis and design of experiments that require quantification of nucleic acids. These models are mathematically equivalent to generalized linear models of binomial responses that include a complementary, log-log link function and an offset that is dependent on the dPCR partition volume. These models are both versatile and easy to fit using conventional statistical software. Covariates can be used to specify different sources of variation in nucleic acid concentration, and a model's parameters can be used to quantify the effects of these covariates. For purposes of illustration, we analyzed dPCR data from different types of experiments, including serial dilution, evaluation of copy number variation, and quantification of gene expression. We also showed how these models can be used to help design dPCR experiments, as in selection of sample sizes needed to achieve desired levels of precision in estimates of nucleic acid concentration or to detect differences in concentration among treatments with prescribed levels of statistical power.

Statistical methods in personality assessment research.

PubMed

Schinka, J A; LaLone, L; Broeckel, J A

1997-06-01

Emerging models of personality structure and advances in the measurement of personality and psychopathology suggest that research in personality and personality assessment has entered a stage of advanced development, in this article we examine whether researchers in these areas have taken advantage of new and evolving statistical procedures. We conducted a review of articles published in the Journal of Personality, Assessment during the past 5 years. Of the 449 articles that included some form of data analysis, 12.7% used only descriptive statistics, most employed only univariate statistics, and fewer than 10% used multivariate methods of data analysis. We discuss the cost of using limited statistical methods, the possible reasons for the apparent reluctance to employ advanced statistical procedures, and potential solutions to this technical shortcoming.

Profile of Undergraduates in U.S. Postsecondary Education Institutions, 2003-04: With a Special Analysis of Community College Students. Statistical Analysis Report. NCES 2006-184

ERIC Educational Resources Information Center

Horn, Laura; Nevill, Stephanie; Griffith, James

2006-01-01

This report is the fifth in a series of reports that provide a statistical snapshot of the undergraduate population. The reports accompany the newly released data from the National Postsecondary Student Aid Study (NPSAS), and each one includes a focused analysis on a particular topic. This report focuses on community college students, who…

Data Analysis and Graphing in an Introductory Physics Laboratory: Spreadsheet versus Statistics Suite

ERIC Educational Resources Information Center

Peterlin, Primoz

2010-01-01

Two methods of data analysis are compared: spreadsheet software and a statistics software suite. Their use is compared analysing data collected in three selected experiments taken from an introductory physics laboratory, which include a linear dependence, a nonlinear dependence and a histogram. The merits of each method are compared. (Contains 7…

Examples of Data Analysis with SPSS/PC+ Studentware.

ERIC Educational Resources Information Center

MacFarland, Thomas W.

Intended for classroom use only, these unpublished notes contain computer lessons on descriptive statistics with files previously created in WordPerfect 4.2 and Lotus 1-2-3 Version 1.A for the IBM PC+. The statistical measures covered include Student's t-test with two independent samples; Student's t-test with a paired sample; Chi-square analysis;…

ParallABEL: an R library for generalized parallelization of genome-wide association studies.

PubMed

Sangket, Unitsa; Mahasirimongkol, Surakameth; Chantratita, Wasun; Tandayya, Pichaya; Aulchenko, Yurii S

2010-04-29

Genome-Wide Association (GWA) analysis is a powerful method for identifying loci associated with complex traits and drug response. Parts of GWA analyses, especially those involving thousands of individuals and consuming hours to months, will benefit from parallel computation. It is arduous acquiring the necessary programming skills to correctly partition and distribute data, control and monitor tasks on clustered computers, and merge output files. Most components of GWA analysis can be divided into four groups based on the types of input data and statistical outputs. The first group contains statistics computed for a particular Single Nucleotide Polymorphism (SNP), or trait, such as SNP characterization statistics or association test statistics. The input data of this group includes the SNPs/traits. The second group concerns statistics characterizing an individual in a study, for example, the summary statistics of genotype quality for each sample. The input data of this group includes individuals. The third group consists of pair-wise statistics derived from analyses between each pair of individuals in the study, for example genome-wide identity-by-state or genomic kinship analyses. The input data of this group includes pairs of SNPs/traits. The final group concerns pair-wise statistics derived for pairs of SNPs, such as the linkage disequilibrium characterisation. The input data of this group includes pairs of individuals. We developed the ParallABEL library, which utilizes the Rmpi library, to parallelize these four types of computations. ParallABEL library is not only aimed at GenABEL, but may also be employed to parallelize various GWA packages in R. The data set from the North American Rheumatoid Arthritis Consortium (NARAC) includes 2,062 individuals with 545,080, SNPs' genotyping, was used to measure ParallABEL performance. Almost perfect speed-up was achieved for many types of analyses. For example, the computing time for the identity-by-state matrix was linearly reduced from approximately eight hours to one hour when ParallABEL employed eight processors. Executing genome-wide association analysis using the ParallABEL library on a computer cluster is an effective way to boost performance, and simplify the parallelization of GWA studies. ParallABEL is a user-friendly parallelization of GenABEL.

Approaching Career Criminals With An Intelligence Cycle

DTIC Science & Technology

2015-12-01

including arrest statistics and “arrest statistics have been used as the main barometer of juvenile delinquent activity, (but) many juvenile... Statistical Briefing Book,” 187. 26 guided by theories about the causes of delinquent behavior, but there was no determination if those efforts achieved the...children.”110 However, the most evidence-based comparison of juvenile delinquency reduction programs is the statistical meta-analysis (a systematic

North Dakota's forests, 2005: statistics, methods, and quality assurance

Treesearch

Patrick D. Miles; David E. Haugen; Charles J. Barnett

2011-01-01

The first full annual inventory of North Dakota's forests was completed in 2005 after 7,622 plots were selected and 164 forested plots were visited and measured. This report includes detailed information on forest inventory methods and data quality estimates. Important resource statistics are included in the tables. A detailed analysis of the North Dakota...

South Dakota's forests, 2005: statistics, methods, and quality assurance

Treesearch

Patrick D. Miles; Ronald J. Piva; Charles J. Barnett

2011-01-01

The first full annual inventory of South Dakota's forests was completed in 2005 after 8,302 plots were selected and 325 forested plots were visited and measured. This report includes detailed information on forest inventory methods and data quality estimates. Important resource statistics are included in the tables. A detailed analysis of the South Dakota...

Should I Pack My Umbrella? Clinical versus Statistical Prediction of Mental Health Decisions

ERIC Educational Resources Information Center

Aegisdottir, Stefania; Spengler, Paul M.; White, Michael J.

2006-01-01

In this rejoinder, the authors respond to the insightful commentary of Strohmer and Arm, Chwalisz, and Hilton, Harris, and Rice about the meta-analysis on statistical versus clinical prediction techniques for mental health judgments. The authors address issues including the availability of statistical prediction techniques for real-life psychology…

Meta- and statistical analysis of single-case intervention research data: quantitative gifts and a wish list.

PubMed

Kratochwill, Thomas R; Levin, Joel R

2014-04-01

In this commentary, we add to the spirit of the articles appearing in the special series devoted to meta- and statistical analysis of single-case intervention-design data. Following a brief discussion of historical factors leading to our initial involvement in statistical analysis of such data, we discuss: (a) the value added by including statistical-analysis recommendations in the What Works Clearinghouse Standards for single-case intervention designs; (b) the importance of visual analysis in single-case intervention research, along with the distinctive role that could be played by single-case effect-size measures; and (c) the elevated internal validity and statistical-conclusion validity afforded by the incorporation of various forms of randomization into basic single-case design structures. For the future, we envision more widespread application of quantitative analyses, as critical adjuncts to visual analysis, in both primary single-case intervention research studies and literature reviews in the behavioral, educational, and health sciences. Copyright © 2014 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

The use and misuse of statistical analyses. [in geophysics and space physics

NASA Technical Reports Server (NTRS)

Reiff, P. H.

1983-01-01

The statistical techniques most often used in space physics include Fourier analysis, linear correlation, auto- and cross-correlation, power spectral density, and superposed epoch analysis. Tests are presented which can evaluate the significance of the results obtained through each of these. Data presented without some form of error analysis are frequently useless, since they offer no way of assessing whether a bump on a spectrum or on a superposed epoch analysis is real or merely a statistical fluctuation. Among many of the published linear correlations, for instance, the uncertainty in the intercept and slope is not given, so that the significance of the fitted parameters cannot be assessed.

Statistical Considerations of Food Allergy Prevention Studies.

PubMed

Bahnson, Henry T; du Toit, George; Lack, Gideon

Clinical studies to prevent the development of food allergy have recently helped reshape public policy recommendations on the early introduction of allergenic foods. These trials are also prompting new research, and it is therefore important to address the unique design and analysis challenges of prevention trials. We highlight statistical concepts and give recommendations that clinical researchers may wish to adopt when designing future study protocols and analysis plans for prevention studies. Topics include selecting a study sample, addressing internal and external validity, improving statistical power, choosing alpha and beta, analysis innovations to address dilution effects, and analysis methods to deal with poor compliance, dropout, and missing data. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Statistical Estimation of Rollover Risk

DOT National Transportation Integrated Search

1989-08-01

This report describes the results of a statistical analysis to determine the : probability of a rollover in a single vehicle accident. Over 39,000 accidents, : which included 4910 rollovers in the states of Texas, Maryland, and Washington were : exam...

Analysis models for the estimation of oceanic fields

NASA Technical Reports Server (NTRS)

Carter, E. F.; Robinson, A. R.

1987-01-01

A general model for statistically optimal estimates is presented for dealing with scalar, vector and multivariate datasets. The method deals with anisotropic fields and treats space and time dependence equivalently. Problems addressed include the analysis, or the production of synoptic time series of regularly gridded fields from irregular and gappy datasets, and the estimate of fields by compositing observations from several different instruments and sampling schemes. Technical issues are discussed, including the convergence of statistical estimates, the choice of representation of the correlations, the influential domain of an observation, and the efficiency of numerical computations.

2011 statistical abstract of the United States

USGS Publications Warehouse

Krisanda, Joseph M.

2011-01-01

The Statistical Abstract of the United States, published since 1878, is the authoritative and comprehensive summary of statistics on the social, political, and economic organization of the United States.Use the Abstract as a convenient volume for statistical reference, and as a guide to sources of more information both in print and on the Web.Sources of data include the Census Bureau, Bureau of Labor Statistics, Bureau of Economic Analysis, and many other Federal agencies and private organizations.

Public and patient involvement in quantitative health research: A statistical perspective.

PubMed

Hannigan, Ailish

2018-06-19

The majority of studies included in recent reviews of impact for public and patient involvement (PPI) in health research had a qualitative design. PPI in solely quantitative designs is underexplored, particularly its impact on statistical analysis. Statisticians in practice have a long history of working in both consultative (indirect) and collaborative (direct) roles in health research, yet their perspective on PPI in quantitative health research has never been explicitly examined. To explore the potential and challenges of PPI from a statistical perspective at distinct stages of quantitative research, that is sampling, measurement and statistical analysis, distinguishing between indirect and direct PPI. Statistical analysis is underpinned by having a representative sample, and a collaborative or direct approach to PPI may help achieve that by supporting access to and increasing participation of under-represented groups in the population. Acknowledging and valuing the role of lay knowledge of the context in statistical analysis and in deciding what variables to measure may support collective learning and advance scientific understanding, as evidenced by the use of participatory modelling in other disciplines. A recurring issue for quantitative researchers, which reflects quantitative sampling methods, is the selection and required number of PPI contributors, and this requires further methodological development. Direct approaches to PPI in quantitative health research may potentially increase its impact, but the facilitation and partnership skills required may require further training for all stakeholders, including statisticians. © 2018 The Authors Health Expectations published by John Wiley & Sons Ltd.

Math Academy: Play Ball! Explorations in Data Analysis & Statistics. Book 3: Supplemental Math Materials for Grades 3-8

ERIC Educational Resources Information Center

Rimbey, Kimberly

2008-01-01

Created by teachers for teachers, the Math Academy tools and activities included in this booklet were designed to create hands-on activities and a fun learning environment for the teaching of mathematics to the students. This booklet contains the "Math Academy--Play Ball! Explorations in Data Analysis & Statistics," which teachers can use to…

Gaia DR2 documentation Chapter 1: Introduction

NASA Astrophysics Data System (ADS)

de Bruijne, J. H. J.; Abreu, A.; Brown, A. G. A.; Castañeda, J.; Cheek, N.; Crowley, C.; De Angeli, F.; Drimmel, R.; Fabricius, C.; Fleitas, J.; Gracia-Abril, G.; Guerra, R.; Hutton, A.; Messineo, R.; Mora, A.; Nienartowicz, K.; Panem, C.; Siddiqui, H.

2018-04-01

This chapter of the Gaia DR2 documentation describes the Gaia mission, the Gaia spacecraft, and the organisation of the Gaia Data Processing and Analysis Consortium (DPAC), which is responsible for the processing and analysis of the Gaia data. Furthermore, various properties of the data release are summarised, including statistical properties, object statistics, completeness, selection and filtering criteria, and limitations of the data.

Metamodels for Computer-Based Engineering Design: Survey and Recommendations

NASA Technical Reports Server (NTRS)

Simpson, Timothy W.; Peplinski, Jesse; Koch, Patrick N.; Allen, Janet K.

1997-01-01

The use of statistical techniques to build approximations of expensive computer analysis codes pervades much of todays engineering design. These statistical approximations, or metamodels, are used to replace the actual expensive computer analyses, facilitating multidisciplinary, multiobjective optimization and concept exploration. In this paper we review several of these techniques including design of experiments, response surface methodology, Taguchi methods, neural networks, inductive learning, and kriging. We survey their existing application in engineering design and then address the dangers of applying traditional statistical techniques to approximate deterministic computer analysis codes. We conclude with recommendations for the appropriate use of statistical approximation techniques in given situations and how common pitfalls can be avoided.

Statistical analysis of Thematic Mapper Simulator data for the geobotanical discrimination of rock types in southwest Oregon

NASA Technical Reports Server (NTRS)

Morrissey, L. A.; Weinstock, K. J.; Mouat, D. A.; Card, D. H.

1984-01-01

An evaluation of Thematic Mapper Simulator (TMS) data for the geobotanical discrimination of rock types based on vegetative cover characteristics is addressed in this research. A methodology for accomplishing this evaluation utilizing univariate and multivariate techniques is presented. TMS data acquired with a Daedalus DEI-1260 multispectral scanner were integrated with vegetation and geologic information for subsequent statistical analyses, which included a chi-square test, an analysis of variance, stepwise discriminant analysis, and Duncan's multiple range test. Results indicate that ultramafic rock types are spectrally separable from nonultramafics based on vegetative cover through the use of statistical analyses.

Statistical summaries of selected Iowa streamflow data through September 2013.

DOT National Transportation Integrated Search

2015-01-01

Statistical summaries of streamflow data collected at : 184 streamgages in Iowa are presented in this report. All : streamgages included for analysis have at least 10 years of : continuous record collected before or through September : 2013. This rep...

Accession Medical Standards Analysis and Research Activity (AMSARA) 2014, Annual Report, and four Supplemental Applicants and Accessions Tables for: Army, Air Force, Marine, and Navy

DTIC Science & Technology

2016-02-02

23 Descriptive Statistics for Enlisted Service Applicants and Accessions...33 Summary Statistics for Applicants and Accessions for Enlisted Service ..................................... 36 Applicants and...utilization among Soldiers screened using TAPAS. Section 2 of this report includes the descriptive statistics AMSARA compiles and publishes

Classical Statistics and Statistical Learning in Imaging Neuroscience

PubMed Central

Bzdok, Danilo

2017-01-01

Brain-imaging research has predominantly generated insight by means of classical statistics, including regression-type analyses and null-hypothesis testing using t-test and ANOVA. Throughout recent years, statistical learning methods enjoy increasing popularity especially for applications in rich and complex data, including cross-validated out-of-sample prediction using pattern classification and sparsity-inducing regression. This concept paper discusses the implications of inferential justifications and algorithmic methodologies in common data analysis scenarios in neuroimaging. It is retraced how classical statistics and statistical learning originated from different historical contexts, build on different theoretical foundations, make different assumptions, and evaluate different outcome metrics to permit differently nuanced conclusions. The present considerations should help reduce current confusion between model-driven classical hypothesis testing and data-driven learning algorithms for investigating the brain with imaging techniques. PMID:29056896

Statistics and bioinformatics in nutritional sciences: analysis of complex data in the era of systems biology⋆

PubMed Central

Fu, Wenjiang J.; Stromberg, Arnold J.; Viele, Kert; Carroll, Raymond J.; Wu, Guoyao

2009-01-01

Over the past two decades, there have been revolutionary developments in life science technologies characterized by high throughput, high efficiency, and rapid computation. Nutritionists now have the advanced methodologies for the analysis of DNA, RNA, protein, low-molecular-weight metabolites, as well as access to bioinformatics databases. Statistics, which can be defined as the process of making scientific inferences from data that contain variability, has historically played an integral role in advancing nutritional sciences. Currently, in the era of systems biology, statistics has become an increasingly important tool to quantitatively analyze information about biological macromolecules. This article describes general terms used in statistical analysis of large, complex experimental data. These terms include experimental design, power analysis, sample size calculation, and experimental errors (type I and II errors) for nutritional studies at population, tissue, cellular, and molecular levels. In addition, we highlighted various sources of experimental variations in studies involving microarray gene expression, real-time polymerase chain reaction, proteomics, and other bioinformatics technologies. Moreover, we provided guidelines for nutritionists and other biomedical scientists to plan and conduct studies and to analyze the complex data. Appropriate statistical analyses are expected to make an important contribution to solving major nutrition-associated problems in humans and animals (including obesity, diabetes, cardiovascular disease, cancer, ageing, and intrauterine fetal retardation). PMID:20233650

Quantitative investigation of inappropriate regression model construction and the importance of medical statistics experts in observational medical research: a cross-sectional study.

PubMed

Nojima, Masanori; Tokunaga, Mutsumi; Nagamura, Fumitaka

2018-05-05

To investigate under what circumstances inappropriate use of 'multivariate analysis' is likely to occur and to identify the population that needs more support with medical statistics. The frequency of inappropriate regression model construction in multivariate analysis and related factors were investigated in observational medical research publications. The inappropriate algorithm of using only variables that were significant in univariate analysis was estimated to occur at 6.4% (95% CI 4.8% to 8.5%). This was observed in 1.1% of the publications with a medical statistics expert (hereinafter 'expert') as the first author, 3.5% if an expert was included as coauthor and in 12.2% if experts were not involved. In the publications where the number of cases was 50 or less and the study did not include experts, inappropriate algorithm usage was observed with a high proportion of 20.2%. The OR of the involvement of experts for this outcome was 0.28 (95% CI 0.15 to 0.53). A further, nation-level, analysis showed that the involvement of experts and the implementation of unfavourable multivariate analysis are associated at the nation-level analysis (R=-0.652). Based on the results of this study, the benefit of participation of medical statistics experts is obvious. Experts should be involved for proper confounding adjustment and interpretation of statistical models. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Research design and statistical methods in Pakistan Journal of Medical Sciences (PJMS).

PubMed

Akhtar, Sohail; Shah, Syed Wadood Ali; Rafiq, M; Khan, Ajmal

2016-01-01

This article compares the study design and statistical methods used in 2005, 2010 and 2015 of Pakistan Journal of Medical Sciences (PJMS). Only original articles of PJMS were considered for the analysis. The articles were carefully reviewed for statistical methods and designs, and then recorded accordingly. The frequency of each statistical method and research design was estimated and compared with previous years. A total of 429 articles were evaluated (n=74 in 2005, n=179 in 2010, n=176 in 2015) in which 171 (40%) were cross-sectional and 116 (27%) were prospective study designs. A verity of statistical methods were found in the analysis. The most frequent methods include: descriptive statistics (n=315, 73.4%), chi-square/Fisher's exact tests (n=205, 47.8%) and student t-test (n=186, 43.4%). There was a significant increase in the use of statistical methods over time period: t-test, chi-square/Fisher's exact test, logistic regression, epidemiological statistics, and non-parametric tests. This study shows that a diverse variety of statistical methods have been used in the research articles of PJMS and frequency improved from 2005 to 2015. However, descriptive statistics was the most frequent method of statistical analysis in the published articles while cross-sectional study design was common study design.

Statistical properties of filtered pseudorandom digital sequences formed from the sum of maximum-length sequences

NASA Technical Reports Server (NTRS)

Wallace, G. R.; Weathers, G. D.; Graf, E. R.

1973-01-01

The statistics of filtered pseudorandom digital sequences called hybrid-sum sequences, formed from the modulo-two sum of several maximum-length sequences, are analyzed. The results indicate that a relation exists between the statistics of the filtered sequence and the characteristic polynomials of the component maximum length sequences. An analysis procedure is developed for identifying a large group of sequences with good statistical properties for applications requiring the generation of analog pseudorandom noise. By use of the analysis approach, the filtering process is approximated by the convolution of the sequence with a sum of unit step functions. A parameter reflecting the overall statistical properties of filtered pseudorandom sequences is derived. This parameter is called the statistical quality factor. A computer algorithm to calculate the statistical quality factor for the filtered sequences is presented, and the results for two examples of sequence combinations are included. The analysis reveals that the statistics of the signals generated with the hybrid-sum generator are potentially superior to the statistics of signals generated with maximum-length generators. Furthermore, fewer calculations are required to evaluate the statistics of a large group of hybrid-sum generators than are required to evaluate the statistics of the same size group of approximately equivalent maximum-length sequences.

2011 statistical abstract of the United States

USGS Publications Warehouse

Krisanda, Joseph M.

2011-01-01

The Statistical Abstract of the United States, published since 1878, is the authoritative and comprehensive summary of statistics on the social, political, and economic organization of the United States.

Use the Abstract as a convenient volume for statistical reference, and as a guide to sources of more information both in print and on the Web.

Sources of data include the Census Bureau, Bureau of Labor Statistics, Bureau of Economic Analysis, and many other Federal agencies and private organizations.

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

Gilbert, Richard O.

The application of statistics to environmental pollution monitoring studies requires a knowledge of statistical analysis methods particularly well suited to pollution data. This book fills that need by providing sampling plans, statistical tests, parameter estimation procedure techniques, and references to pertinent publications. Most of the statistical techniques are relatively simple, and examples, exercises, and case studies are provided to illustrate procedures. The book is logically divided into three parts. Chapters 1, 2, and 3 are introductory chapters. Chapters 4 through 10 discuss field sampling designs and Chapters 11 through 18 deal with a broad range of statistical analysis procedures. Somemore » statistical techniques given here are not commonly seen in statistics book. For example, see methods for handling correlated data (Sections 4.5 and 11.12), for detecting hot spots (Chapter 10), and for estimating a confidence interval for the mean of a lognormal distribution (Section 13.2). Also, Appendix B lists a computer code that estimates and tests for trends over time at one or more monitoring stations using nonparametric methods (Chapters 16 and 17). Unfortunately, some important topics could not be included because of their complexity and the need to limit the length of the book. For example, only brief mention could be made of time series analysis using Box-Jenkins methods and of kriging techniques for estimating spatial and spatial-time patterns of pollution, although multiple references on these topics are provided. Also, no discussion of methods for assessing risks from environmental pollution could be included.« less

Statistical assessment of the learning curves of health technologies.

PubMed

Ramsay, C R; Grant, A M; Wallace, S A; Garthwaite, P H; Monk, A F; Russell, I T

2001-01-01

(1) To describe systematically studies that directly assessed the learning curve effect of health technologies. (2) Systematically to identify 'novel' statistical techniques applied to learning curve data in other fields, such as psychology and manufacturing. (3) To test these statistical techniques in data sets from studies of varying designs to assess health technologies in which learning curve effects are known to exist. METHODS - STUDY SELECTION (HEALTH TECHNOLOGY ASSESSMENT LITERATURE REVIEW): For a study to be included, it had to include a formal analysis of the learning curve of a health technology using a graphical, tabular or statistical technique. METHODS - STUDY SELECTION (NON-HEALTH TECHNOLOGY ASSESSMENT LITERATURE SEARCH): For a study to be included, it had to include a formal assessment of a learning curve using a statistical technique that had not been identified in the previous search. METHODS - DATA SOURCES: Six clinical and 16 non-clinical biomedical databases were searched. A limited amount of handsearching and scanning of reference lists was also undertaken. METHODS - DATA EXTRACTION (HEALTH TECHNOLOGY ASSESSMENT LITERATURE REVIEW): A number of study characteristics were abstracted from the papers such as study design, study size, number of operators and the statistical method used. METHODS - DATA EXTRACTION (NON-HEALTH TECHNOLOGY ASSESSMENT LITERATURE SEARCH): The new statistical techniques identified were categorised into four subgroups of increasing complexity: exploratory data analysis; simple series data analysis; complex data structure analysis, generic techniques. METHODS - TESTING OF STATISTICAL METHODS: Some of the statistical methods identified in the systematic searches for single (simple) operator series data and for multiple (complex) operator series data were illustrated and explored using three data sets. The first was a case series of 190 consecutive laparoscopic fundoplication procedures performed by a single surgeon; the second was a case series of consecutive laparoscopic cholecystectomy procedures performed by ten surgeons; the third was randomised trial data derived from the laparoscopic procedure arm of a multicentre trial of groin hernia repair, supplemented by data from non-randomised operations performed during the trial. RESULTS - HEALTH TECHNOLOGY ASSESSMENT LITERATURE REVIEW: Of 4571 abstracts identified, 272 (6%) were later included in the study after review of the full paper. Some 51% of studies assessed a surgical minimal access technique and 95% were case series. The statistical method used most often (60%) was splitting the data into consecutive parts (such as halves or thirds), with only 14% attempting a more formal statistical analysis. The reporting of the studies was poor, with 31% giving no details of data collection methods. RESULTS - NON-HEALTH TECHNOLOGY ASSESSMENT LITERATURE SEARCH: Of 9431 abstracts assessed, 115 (1%) were deemed appropriate for further investigation and, of these, 18 were included in the study. All of the methods for complex data sets were identified in the non-clinical literature. These were discriminant analysis, two-stage estimation of learning rates, generalised estimating equations, multilevel models, latent curve models, time series models and stochastic parameter models. In addition, eight new shapes of learning curves were identified. RESULTS - TESTING OF STATISTICAL METHODS: No one particular shape of learning curve performed significantly better than another. The performance of 'operation time' as a proxy for learning differed between the three procedures. Multilevel modelling using the laparoscopic cholecystectomy data demonstrated and measured surgeon-specific and confounding effects. The inclusion of non-randomised cases, despite the possible limitations of the method, enhanced the interpretation of learning effects. CONCLUSIONS - HEALTH TECHNOLOGY ASSESSMENT LITERATURE REVIEW: The statistical methods used for assessing learning effects in health technology assessment have been crude and the reporting of studies poor. CONCLUSIONS - NON-HEALTH TECHNOLOGY ASSESSMENT LITERATURE SEARCH: A number of statistical methods for assessing learning effects were identified that had not hitherto been used in health technology assessment. There was a hierarchy of methods for the identification and measurement of learning, and the more sophisticated methods for both have had little if any use in health technology assessment. This demonstrated the value of considering fields outside clinical research when addressing methodological issues in health technology assessment. CONCLUSIONS - TESTING OF STATISTICAL METHODS: It has been demonstrated that the portfolio of techniques identified can enhance investigations of learning curve effects. (ABSTRACT TRUNCATED)

Landsat real-time processing

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

Davis, E.L.

A novel method for performing real-time acquisition and processing Landsat/EROS data covers all aspects including radiometric and geometric corrections of multispectral scanner or return-beam vidicon inputs, image enhancement, statistical analysis, feature extraction, and classification. Radiometric transformations include bias/gain adjustment, noise suppression, calibration, scan angle compensation, and illumination compensation, including topography and atmospheric effects. Correction or compensation for geometric distortion includes sensor-related distortions, such as centering, skew, size, scan nonlinearity, radial symmetry, and tangential symmetry. Also included are object image-related distortions such as aspect angle (altitude), scale distortion (altitude), terrain relief, and earth curvature. Ephemeral corrections are also applied to compensatemore » for satellite forward movement, earth rotation, altitude variations, satellite vibration, and mirror scan velocity. Image enhancement includes high-pass, low-pass, and Laplacian mask filtering and data restoration for intermittent losses. Resource classification is provided by statistical analysis including histograms, correlational analysis, matrix manipulations, and determination of spectral responses. Feature extraction includes spatial frequency analysis, which is used in parallel discriminant functions in each array processor for rapid determination. The technique uses integrated parallel array processors that decimate the tasks concurrently under supervision of a control processor. The operator-machine interface is optimized for programming ease and graphics image windowing.« less

Statistical Software and Artificial Intelligence: A Watershed in Applications Programming.

ERIC Educational Resources Information Center

Pickett, John C.

1984-01-01

AUTOBJ and AUTOBOX are revolutionary software programs which contain the first application of artificial intelligence to statistical procedures used in analysis of time series data. The artificial intelligence included in the programs and program features are discussed. (JN)

Statistical Analysis Tools for Learning in Engineering Laboratories.

ERIC Educational Resources Information Center

Maher, Carolyn A.

1990-01-01

Described are engineering programs that have used automated data acquisition systems to implement data collection and analyze experiments. Applications include a biochemical engineering laboratory, heat transfer performance, engineering materials testing, mechanical system reliability, statistical control laboratory, thermo-fluid laboratory, and a…

The statistical analysis of global climate change studies

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

Hardin, J.W.

1992-01-01

The focus of this work is to contribute to the enhancement of the relationship between climatologists and statisticians. The analysis of global change data has been underway for many years by atmospheric scientists. Much of this analysis includes a heavy reliance on statistics and statistical inference. Some specific climatological analyses are presented and the dependence on statistics is documented before the analysis is undertaken. The first problem presented involves the fluctuation-dissipation theorem and its application to global climate models. This problem has a sound theoretical niche in the literature of both climate modeling and physics, but a statistical analysis inmore » which the data is obtained from the model to show graphically the relationship has not been undertaken. It is under this motivation that the author presents this problem. A second problem concerning the standard errors in estimating global temperatures is purely statistical in nature although very little materials exists for sampling on such a frame. This problem not only has climatological and statistical ramifications, but political ones as well. It is planned to use these results in a further analysis of global warming using actual data collected on the earth. In order to simplify the analysis of these problems, the development of a computer program, MISHA, is presented. This interactive program contains many of the routines, functions, graphics, and map projections needed by the climatologist in order to effectively enter the arena of data visualization.« less

A Guerilla Guide to Common Problems in ‘Neurostatistics’: Essential Statistical Topics in Neuroscience

PubMed Central

Smith, Paul F.

2017-01-01

Effective inferential statistical analysis is essential for high quality studies in neuroscience. However, recently, neuroscience has been criticised for the poor use of experimental design and statistical analysis. Many of the statistical issues confronting neuroscience are similar to other areas of biology; however, there are some that occur more regularly in neuroscience studies. This review attempts to provide a succinct overview of some of the major issues that arise commonly in the analyses of neuroscience data. These include: the non-normal distribution of the data; inequality of variance between groups; extensive correlation in data for repeated measurements across time or space; excessive multiple testing; inadequate statistical power due to small sample sizes; pseudo-replication; and an over-emphasis on binary conclusions about statistical significance as opposed to effect sizes. Statistical analysis should be viewed as just another neuroscience tool, which is critical to the final outcome of the study. Therefore, it needs to be done well and it is a good idea to be proactive and seek help early, preferably before the study even begins. PMID:29371855

A Guerilla Guide to Common Problems in 'Neurostatistics': Essential Statistical Topics in Neuroscience.

PubMed

Smith, Paul F

2017-01-01

Effective inferential statistical analysis is essential for high quality studies in neuroscience. However, recently, neuroscience has been criticised for the poor use of experimental design and statistical analysis. Many of the statistical issues confronting neuroscience are similar to other areas of biology; however, there are some that occur more regularly in neuroscience studies. This review attempts to provide a succinct overview of some of the major issues that arise commonly in the analyses of neuroscience data. These include: the non-normal distribution of the data; inequality of variance between groups; extensive correlation in data for repeated measurements across time or space; excessive multiple testing; inadequate statistical power due to small sample sizes; pseudo-replication; and an over-emphasis on binary conclusions about statistical significance as opposed to effect sizes. Statistical analysis should be viewed as just another neuroscience tool, which is critical to the final outcome of the study. Therefore, it needs to be done well and it is a good idea to be proactive and seek help early, preferably before the study even begins.

A comparison of performance of automatic cloud coverage assessment algorithm for Formosat-2 image using clustering-based and spatial thresholding methods

NASA Astrophysics Data System (ADS)

Hsu, Kuo-Hsien

2012-11-01

Formosat-2 image is a kind of high-spatial-resolution (2 meters GSD) remote sensing satellite data, which includes one panchromatic band and four multispectral bands (Blue, Green, Red, near-infrared). An essential sector in the daily processing of received Formosat-2 image is to estimate the cloud statistic of image using Automatic Cloud Coverage Assessment (ACCA) algorithm. The information of cloud statistic of image is subsequently recorded as an important metadata for image product catalog. In this paper, we propose an ACCA method with two consecutive stages: preprocessing and post-processing analysis. For pre-processing analysis, the un-supervised K-means classification, Sobel's method, thresholding method, non-cloudy pixels reexamination, and cross-band filter method are implemented in sequence for cloud statistic determination. For post-processing analysis, Box-Counting fractal method is implemented. In other words, the cloud statistic is firstly determined via pre-processing analysis, the correctness of cloud statistic of image of different spectral band is eventually cross-examined qualitatively and quantitatively via post-processing analysis. The selection of an appropriate thresholding method is very critical to the result of ACCA method. Therefore, in this work, We firstly conduct a series of experiments of the clustering-based and spatial thresholding methods that include Otsu's, Local Entropy(LE), Joint Entropy(JE), Global Entropy(GE), and Global Relative Entropy(GRE) method, for performance comparison. The result shows that Otsu's and GE methods both perform better than others for Formosat-2 image. Additionally, our proposed ACCA method by selecting Otsu's method as the threshoding method has successfully extracted the cloudy pixels of Formosat-2 image for accurate cloud statistic estimation.

Statistics and Discoveries at the LHC (1/4)

ScienceCinema

Cowan, Glen

2018-02-09

The lectures will give an introduction to statistics as applied in particle physics and will provide all the necessary basics for data analysis at the LHC. Special emphasis will be placed on the the problems and questions that arise when searching for new phenomena, including p-values, discovery significance, limit setting procedures, treatment of small signals in the presence of large backgrounds. Specific issues that will be addressed include the advantages and drawbacks of different statistical test procedures (cut-based, likelihood-ratio, etc.), the look-elsewhere effect and treatment of systematic uncertainties.

Statistics and Discoveries at the LHC (3/4)

ScienceCinema

Cowan, Glen

2018-02-19

The lectures will give an introduction to statistics as applied in particle physics and will provide all the necessary basics for data analysis at the LHC. Special emphasis will be placed on the the problems and questions that arise when searching for new phenomena, including p-values, discovery significance, limit setting procedures, treatment of small signals in the presence of large backgrounds. Specific issues that will be addressed include the advantages and drawbacks of different statistical test procedures (cut-based, likelihood-ratio, etc.), the look-elsewhere effect and treatment of systematic uncertainties.

Statistics and Discoveries at the LHC (4/4)

ScienceCinema

Cowan, Glen

2018-05-22

The lectures will give an introduction to statistics as applied in particle physics and will provide all the necessary basics for data analysis at the LHC. Special emphasis will be placed on the the problems and questions that arise when searching for new phenomena, including p-values, discovery significance, limit setting procedures, treatment of small signals in the presence of large backgrounds. Specific issues that will be addressed include the advantages and drawbacks of different statistical test procedures (cut-based, likelihood-ratio, etc.), the look-elsewhere effect and treatment of systematic uncertainties.

Statistics and Discoveries at the LHC (2/4)

ScienceCinema

Cowan, Glen

2018-04-26

The lectures will give an introduction to statistics as applied in particle physics and will provide all the necessary basics for data analysis at the LHC. Special emphasis will be placed on the the problems and questions that arise when searching for new phenomena, including p-values, discovery significance, limit setting procedures, treatment of small signals in the presence of large backgrounds. Specific issues that will be addressed include the advantages and drawbacks of different statistical test procedures (cut-based, likelihood-ratio, etc.), the look-elsewhere effect and treatment of systematic uncertainties.

A Statistical Analysis of Brain Morphology Using Wild Bootstrapping

PubMed Central

Ibrahim, Joseph G.; Tang, Niansheng; Rowe, Daniel B.; Hao, Xuejun; Bansal, Ravi; Peterson, Bradley S.

2008-01-01

Methods for the analysis of brain morphology, including voxel-based morphology and surface-based morphometries, have been used to detect associations between brain structure and covariates of interest, such as diagnosis, severity of disease, age, IQ, and genotype. The statistical analysis of morphometric measures usually involves two statistical procedures: 1) invoking a statistical model at each voxel (or point) on the surface of the brain or brain subregion, followed by mapping test statistics (e.g., t test) or their associated p values at each of those voxels; 2) correction for the multiple statistical tests conducted across all voxels on the surface of the brain region under investigation. We propose the use of new statistical methods for each of these procedures. We first use a heteroscedastic linear model to test the associations between the morphological measures at each voxel on the surface of the specified subregion (e.g., cortical or subcortical surfaces) and the covariates of interest. Moreover, we develop a robust test procedure that is based on a resampling method, called wild bootstrapping. This procedure assesses the statistical significance of the associations between a measure of given brain structure and the covariates of interest. The value of this robust test procedure lies in its computationally simplicity and in its applicability to a wide range of imaging data, including data from both anatomical and functional magnetic resonance imaging (fMRI). Simulation studies demonstrate that this robust test procedure can accurately control the family-wise error rate. We demonstrate the application of this robust test procedure to the detection of statistically significant differences in the morphology of the hippocampus over time across gender groups in a large sample of healthy subjects. PMID:17649909

CORSSA: Community Online Resource for Statistical Seismicity Analysis

NASA Astrophysics Data System (ADS)

Zechar, J. D.; Hardebeck, J. L.; Michael, A. J.; Naylor, M.; Steacy, S.; Wiemer, S.; Zhuang, J.

2011-12-01

Statistical seismology is critical to the understanding of seismicity, the evaluation of proposed earthquake prediction and forecasting methods, and the assessment of seismic hazard. Unfortunately, despite its importance to seismology-especially to those aspects with great impact on public policy-statistical seismology is mostly ignored in the education of seismologists, and there is no central repository for the existing open-source software tools. To remedy these deficiencies, and with the broader goal to enhance the quality of statistical seismology research, we have begun building the Community Online Resource for Statistical Seismicity Analysis (CORSSA, www.corssa.org). We anticipate that the users of CORSSA will range from beginning graduate students to experienced researchers. More than 20 scientists from around the world met for a week in Zurich in May 2010 to kick-start the creation of CORSSA: the format and initial table of contents were defined; a governing structure was organized; and workshop participants began drafting articles. CORSSA materials are organized with respect to six themes, each will contain between four and eight articles. CORSSA now includes seven articles with an additional six in draft form along with forums for discussion, a glossary, and news about upcoming meetings, special issues, and recent papers. Each article is peer-reviewed and presents a balanced discussion, including illustrative examples and code snippets. Topics in the initial set of articles include: introductions to both CORSSA and statistical seismology, basic statistical tests and their role in seismology; understanding seismicity catalogs and their problems; basic techniques for modeling seismicity; and methods for testing earthquake predictability hypotheses. We have also begun curating a collection of statistical seismology software packages.

Periodontal disease and carotid atherosclerosis: A meta-analysis of 17,330 participants.

PubMed

Zeng, Xian-Tao; Leng, Wei-Dong; Lam, Yat-Yin; Yan, Bryan P; Wei, Xue-Mei; Weng, Hong; Kwong, Joey S W

2016-01-15

The association between periodontal disease and carotid atherosclerosis has been evaluated primarily in single-center studies, and whether periodontal disease is an independent risk factor of carotid atherosclerosis remains uncertain. This meta-analysis aimed to evaluate the association between periodontal disease and carotid atherosclerosis. We searched PubMed and Embase for relevant observational studies up to February 20, 2015. Two authors independently extracted data from included studies, and odds ratios (ORs) with 95% confidence intervals (CIs) were calculated for overall and subgroup meta-analyses. Statistical heterogeneity was assessed by the chi-squared test (P<0.1 for statistical significance) and quantified by the I(2) statistic. Data analysis was conducted using the Comprehensive Meta-Analysis (CMA) software. Fifteen observational studies involving 17,330 participants were included in the meta-analysis. The overall pooled result showed that periodontal disease was associated with carotid atherosclerosis (OR: 1.27, 95% CI: 1.14-1.41; P<0.001) but statistical heterogeneity was substantial (I(2)=78.90%). Subgroup analysis of adjusted smoking and diabetes mellitus showed borderline significance (OR: 1.08; 95% CI: 1.00-1.18; P=0.05). Sensitivity and cumulative analyses both indicated that our results were robust. Findings of our meta-analysis indicated that the presence of periodontal disease was associated with carotid atherosclerosis; however, further large-scale, well-conducted clinical studies are needed to explore the precise risk of developing carotid atherosclerosis in patients with periodontal disease. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Diagnosis checking of statistical analysis in RCTs indexed in PubMed.

PubMed

Lee, Paul H; Tse, Andy C Y

2017-11-01

Statistical analysis is essential for reporting of the results of randomized controlled trials (RCTs), as well as evaluating their effectiveness. However, the validity of a statistical analysis also depends on whether the assumptions of that analysis are valid. To review all RCTs published in journals indexed in PubMed during December 2014 to provide a complete picture of how RCTs handle assumptions of statistical analysis. We reviewed all RCTs published in December 2014 that appeared in journals indexed in PubMed using the Cochrane highly sensitive search strategy. The 2014 impact factors of the journals were used as proxies for their quality. The type of statistical analysis used and whether the assumptions of the analysis were tested were reviewed. In total, 451 papers were included. Of the 278 papers that reported a crude analysis for the primary outcomes, 31 (27·2%) reported whether the outcome was normally distributed. Of the 172 papers that reported an adjusted analysis for the primary outcomes, diagnosis checking was rarely conducted, with only 20%, 8·6% and 7% checked for generalized linear model, Cox proportional hazard model and multilevel model, respectively. Study characteristics (study type, drug trial, funding sources, journal type and endorsement of CONSORT guidelines) were not associated with the reporting of diagnosis checking. The diagnosis of statistical analyses in RCTs published in PubMed-indexed journals was usually absent. Journals should provide guidelines about the reporting of a diagnosis of assumptions. © 2017 Stichting European Society for Clinical Investigation Journal Foundation.

ParallABEL: an R library for generalized parallelization of genome-wide association studies

PubMed Central

2010-01-01

Background Genome-Wide Association (GWA) analysis is a powerful method for identifying loci associated with complex traits and drug response. Parts of GWA analyses, especially those involving thousands of individuals and consuming hours to months, will benefit from parallel computation. It is arduous acquiring the necessary programming skills to correctly partition and distribute data, control and monitor tasks on clustered computers, and merge output files. Results Most components of GWA analysis can be divided into four groups based on the types of input data and statistical outputs. The first group contains statistics computed for a particular Single Nucleotide Polymorphism (SNP), or trait, such as SNP characterization statistics or association test statistics. The input data of this group includes the SNPs/traits. The second group concerns statistics characterizing an individual in a study, for example, the summary statistics of genotype quality for each sample. The input data of this group includes individuals. The third group consists of pair-wise statistics derived from analyses between each pair of individuals in the study, for example genome-wide identity-by-state or genomic kinship analyses. The input data of this group includes pairs of SNPs/traits. The final group concerns pair-wise statistics derived for pairs of SNPs, such as the linkage disequilibrium characterisation. The input data of this group includes pairs of individuals. We developed the ParallABEL library, which utilizes the Rmpi library, to parallelize these four types of computations. ParallABEL library is not only aimed at GenABEL, but may also be employed to parallelize various GWA packages in R. The data set from the North American Rheumatoid Arthritis Consortium (NARAC) includes 2,062 individuals with 545,080, SNPs' genotyping, was used to measure ParallABEL performance. Almost perfect speed-up was achieved for many types of analyses. For example, the computing time for the identity-by-state matrix was linearly reduced from approximately eight hours to one hour when ParallABEL employed eight processors. Conclusions Executing genome-wide association analysis using the ParallABEL library on a computer cluster is an effective way to boost performance, and simplify the parallelization of GWA studies. ParallABEL is a user-friendly parallelization of GenABEL. PMID:20429914

Meta-analysis of neutropenia or leukopenia as a prognostic factor in patients with malignant disease undergoing chemotherapy.

PubMed

Shitara, Kohei; Matsuo, Keitaro; Oze, Isao; Mizota, Ayako; Kondo, Chihiro; Nomura, Motoo; Yokota, Tomoya; Takahari, Daisuke; Ura, Takashi; Muro, Kei

2011-08-01

We performed a systematic review and meta-analysis to determine the impact of neutropenia or leukopenia experienced during chemotherapy on survival. Eligible studies included prospective or retrospective analyses that evaluated neutropenia or leukopenia as a prognostic factor for overall survival or disease-free survival. Statistical analyses were conducted to calculate a summary hazard ratio and 95% confidence interval (CI) using random-effects or fixed-effects models based on the heterogeneity of the included studies. Thirteen trials were selected for the meta-analysis, with a total of 9,528 patients. The hazard ratio of death was 0.69 (95% CI, 0.64-0.75) for patients with higher-grade neutropenia or leukopenia compared to patients with lower-grade or lack of cytopenia. Our analysis was also stratified by statistical method (any statistical method to decrease lead-time bias; time-varying analysis or landmark analysis), but no differences were observed. Our results indicate that neutropenia or leukopenia experienced during chemotherapy is associated with improved survival in patients with advanced cancer or hematological malignancies undergoing chemotherapy. Future prospective analyses designed to investigate the potential impact of chemotherapy dose adjustment coupled with monitoring of neutropenia or leukopenia on survival are warranted.

SWToolbox: A surface-water tool-box for statistical analysis of streamflow time series

USGS Publications Warehouse

Kiang, Julie E.; Flynn, Kate; Zhai, Tong; Hummel, Paul; Granato, Gregory

2018-03-07

This report is a user guide for the low-flow analysis methods provided with version 1.0 of the Surface Water Toolbox (SWToolbox) computer program. The software combines functionality from two software programs—U.S. Geological Survey (USGS) SWSTAT and U.S. Environmental Protection Agency (EPA) DFLOW. Both of these programs have been used primarily for computation of critical low-flow statistics. The main analysis methods are the computation of hydrologic frequency statistics such as the 7-day minimum flow that occurs on average only once every 10 years (7Q10), computation of design flows including biologically based flows, and computation of flow-duration curves and duration hydrographs. Other annual, monthly, and seasonal statistics can also be computed. The interface facilitates retrieval of streamflow discharge data from the USGS National Water Information System and outputs text reports for a record of the analysis. Tools for graphing data and screening tests are available to assist the analyst in conducting the analysis.

Visual Data Analysis for Satellites

NASA Technical Reports Server (NTRS)

Lau, Yee; Bhate, Sachin; Fitzpatrick, Patrick

2008-01-01

The Visual Data Analysis Package is a collection of programs and scripts that facilitate visual analysis of data available from NASA and NOAA satellites, as well as dropsonde, buoy, and conventional in-situ observations. The package features utilities for data extraction, data quality control, statistical analysis, and data visualization. The Hierarchical Data Format (HDF) satellite data extraction routines from NASA's Jet Propulsion Laboratory were customized for specific spatial coverage and file input/output. Statistical analysis includes the calculation of the relative error, the absolute error, and the root mean square error. Other capabilities include curve fitting through the data points to fill in missing data points between satellite passes or where clouds obscure satellite data. For data visualization, the software provides customizable Generic Mapping Tool (GMT) scripts to generate difference maps, scatter plots, line plots, vector plots, histograms, timeseries, and color fill images.

Comment on “Two statistics for evaluating parameter identifiability and error reduction” by John Doherty and Randall J. Hunt

USGS Publications Warehouse

Hill, Mary C.

2010-01-01

Doherty and Hunt (2009) present important ideas for first-order-second moment sensitivity analysis, but five issues are discussed in this comment. First, considering the composite-scaled sensitivity (CSS) jointly with parameter correlation coefficients (PCC) in a CSS/PCC analysis addresses the difficulties with CSS mentioned in the introduction. Second, their new parameter identifiability statistic actually is likely to do a poor job of parameter identifiability in common situations. The statistic instead performs the very useful role of showing how model parameters are included in the estimated singular value decomposition (SVD) parameters. Its close relation to CSS is shown. Third, the idea from p. 125 that a suitable truncation point for SVD parameters can be identified using the prediction variance is challenged using results from Moore and Doherty (2005). Fourth, the relative error reduction statistic of Doherty and Hunt is shown to belong to an emerging set of statistics here named perturbed calculated variance statistics. Finally, the perturbed calculated variance statistics OPR and PPR mentioned on p. 121 are shown to explicitly include the parameter null-space component of uncertainty. Indeed, OPR and PPR results that account for null-space uncertainty have appeared in the literature since 2000.

Statistical Analysis of Big Data on Pharmacogenomics

PubMed Central

Fan, Jianqing; Liu, Han

2013-01-01

This paper discusses statistical methods for estimating complex correlation structure from large pharmacogenomic datasets. We selectively review several prominent statistical methods for estimating large covariance matrix for understanding correlation structure, inverse covariance matrix for network modeling, large-scale simultaneous tests for selecting significantly differently expressed genes and proteins and genetic markers for complex diseases, and high dimensional variable selection for identifying important molecules for understanding molecule mechanisms in pharmacogenomics. Their applications to gene network estimation and biomarker selection are used to illustrate the methodological power. Several new challenges of Big data analysis, including complex data distribution, missing data, measurement error, spurious correlation, endogeneity, and the need for robust statistical methods, are also discussed. PMID:23602905

Linnorm: improved statistical analysis for single cell RNA-seq expression data

PubMed Central

Yip, Shun H.; Wang, Panwen; Kocher, Jean-Pierre A.; Sham, Pak Chung

2017-01-01

Abstract Linnorm is a novel normalization and transformation method for the analysis of single cell RNA sequencing (scRNA-seq) data. Linnorm is developed to remove technical noises and simultaneously preserve biological variations in scRNA-seq data, such that existing statistical methods can be improved. Using real scRNA-seq data, we compared Linnorm with existing normalization methods, including NODES, SAMstrt, SCnorm, scran, DESeq and TMM. Linnorm shows advantages in speed, technical noise removal and preservation of cell heterogeneity, which can improve existing methods in the discovery of novel subtypes, pseudo-temporal ordering of cells, clustering analysis, etc. Linnorm also performs better than existing DEG analysis methods, including BASiCS, NODES, SAMstrt, Seurat and DESeq2, in false positive rate control and accuracy. PMID:28981748

Statistical Analysis of speckle noise reduction techniques for echocardiographic Images

NASA Astrophysics Data System (ADS)

Saini, Kalpana; Dewal, M. L.; Rohit, Manojkumar

2011-12-01

Echocardiography is the safe, easy and fast technology for diagnosing the cardiac diseases. As in other ultrasound images these images also contain speckle noise. In some cases this speckle noise is useful such as in motion detection. But in general noise removal is required for better analysis of the image and proper diagnosis. Different Adaptive and anisotropic filters are included for statistical analysis. Statistical parameters such as Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR), and Root Mean Square Error (RMSE) calculated for performance measurement. One more important aspect that there may be blurring during speckle noise removal. So it is prefered that filter should be able to enhance edges during noise removal.

Monte Carlo based statistical power analysis for mediation models: methods and software.

PubMed

Zhang, Zhiyong

2014-12-01

The existing literature on statistical power analysis for mediation models often assumes data normality and is based on a less powerful Sobel test instead of the more powerful bootstrap test. This study proposes to estimate statistical power to detect mediation effects on the basis of the bootstrap method through Monte Carlo simulation. Nonnormal data with excessive skewness and kurtosis are allowed in the proposed method. A free R package called bmem is developed to conduct the power analysis discussed in this study. Four examples, including a simple mediation model, a multiple-mediator model with a latent mediator, a multiple-group mediation model, and a longitudinal mediation model, are provided to illustrate the proposed method.

SOCR Analyses - an Instructional Java Web-based Statistical Analysis Toolkit.

PubMed

Chu, Annie; Cui, Jenny; Dinov, Ivo D

2009-03-01

The Statistical Online Computational Resource (SOCR) designs web-based tools for educational use in a variety of undergraduate courses (Dinov 2006). Several studies have demonstrated that these resources significantly improve students' motivation and learning experiences (Dinov et al. 2008). SOCR Analyses is a new component that concentrates on data modeling and analysis using parametric and non-parametric techniques supported with graphical model diagnostics. Currently implemented analyses include commonly used models in undergraduate statistics courses like linear models (Simple Linear Regression, Multiple Linear Regression, One-Way and Two-Way ANOVA). In addition, we implemented tests for sample comparisons, such as t-test in the parametric category; and Wilcoxon rank sum test, Kruskal-Wallis test, Friedman's test, in the non-parametric category. SOCR Analyses also include several hypothesis test models, such as Contingency tables, Friedman's test and Fisher's exact test.The code itself is open source (http://socr.googlecode.com/), hoping to contribute to the efforts of the statistical computing community. The code includes functionality for each specific analysis model and it has general utilities that can be applied in various statistical computing tasks. For example, concrete methods with API (Application Programming Interface) have been implemented in statistical summary, least square solutions of general linear models, rank calculations, etc. HTML interfaces, tutorials, source code, activities, and data are freely available via the web (www.SOCR.ucla.edu). Code examples for developers and demos for educators are provided on the SOCR Wiki website.In this article, the pedagogical utilization of the SOCR Analyses is discussed, as well as the underlying design framework. As the SOCR project is on-going and more functions and tools are being added to it, these resources are constantly improved. The reader is strongly encouraged to check the SOCR site for most updated information and newly added models.

Integration of modern statistical tools for the analysis of climate extremes into the web-GIS “CLIMATE”

NASA Astrophysics Data System (ADS)

Ryazanova, A. A.; Okladnikov, I. G.; Gordov, E. P.

2017-11-01

The frequency of occurrence and magnitude of precipitation and temperature extreme events show positive trends in several geographical regions. These events must be analyzed and studied in order to better understand their impact on the environment, predict their occurrences, and mitigate their effects. For this purpose, we augmented web-GIS called “CLIMATE” to include a dedicated statistical package developed in the R language. The web-GIS “CLIMATE” is a software platform for cloud storage processing and visualization of distributed archives of spatial datasets. It is based on a combined use of web and GIS technologies with reliable procedures for searching, extracting, processing, and visualizing the spatial data archives. The system provides a set of thematic online tools for the complex analysis of current and future climate changes and their effects on the environment. The package includes new powerful methods of time-dependent statistics of extremes, quantile regression and copula approach for the detailed analysis of various climate extreme events. Specifically, the very promising copula approach allows obtaining the structural connections between the extremes and the various environmental characteristics. The new statistical methods integrated into the web-GIS “CLIMATE” can significantly facilitate and accelerate the complex analysis of climate extremes using only a desktop PC connected to the Internet.

Comparisons of non-Gaussian statistical models in DNA methylation analysis.

PubMed

Ma, Zhanyu; Teschendorff, Andrew E; Yu, Hong; Taghia, Jalil; Guo, Jun

2014-06-16

As a key regulatory mechanism of gene expression, DNA methylation patterns are widely altered in many complex genetic diseases, including cancer. DNA methylation is naturally quantified by bounded support data; therefore, it is non-Gaussian distributed. In order to capture such properties, we introduce some non-Gaussian statistical models to perform dimension reduction on DNA methylation data. Afterwards, non-Gaussian statistical model-based unsupervised clustering strategies are applied to cluster the data. Comparisons and analysis of different dimension reduction strategies and unsupervised clustering methods are presented. Experimental results show that the non-Gaussian statistical model-based methods are superior to the conventional Gaussian distribution-based method. They are meaningful tools for DNA methylation analysis. Moreover, among several non-Gaussian methods, the one that captures the bounded nature of DNA methylation data reveals the best clustering performance.

Comparisons of Non-Gaussian Statistical Models in DNA Methylation Analysis

PubMed Central

Ma, Zhanyu; Teschendorff, Andrew E.; Yu, Hong; Taghia, Jalil; Guo, Jun

2014-01-01

As a key regulatory mechanism of gene expression, DNA methylation patterns are widely altered in many complex genetic diseases, including cancer. DNA methylation is naturally quantified by bounded support data; therefore, it is non-Gaussian distributed. In order to capture such properties, we introduce some non-Gaussian statistical models to perform dimension reduction on DNA methylation data. Afterwards, non-Gaussian statistical model-based unsupervised clustering strategies are applied to cluster the data. Comparisons and analysis of different dimension reduction strategies and unsupervised clustering methods are presented. Experimental results show that the non-Gaussian statistical model-based methods are superior to the conventional Gaussian distribution-based method. They are meaningful tools for DNA methylation analysis. Moreover, among several non-Gaussian methods, the one that captures the bounded nature of DNA methylation data reveals the best clustering performance. PMID:24937687

Assessing compositional variability through graphical analysis and Bayesian statistical approaches: case studies on transgenic crops.

PubMed

Harrigan, George G; Harrison, Jay M

2012-01-01

New transgenic (GM) crops are subjected to extensive safety assessments that include compositional comparisons with conventional counterparts as a cornerstone of the process. The influence of germplasm, location, environment, and agronomic treatments on compositional variability is, however, often obscured in these pair-wise comparisons. Furthermore, classical statistical significance testing can often provide an incomplete and over-simplified summary of highly responsive variables such as crop composition. In order to more clearly describe the influence of the numerous sources of compositional variation we present an introduction to two alternative but complementary approaches to data analysis and interpretation. These include i) exploratory data analysis (EDA) with its emphasis on visualization and graphics-based approaches and ii) Bayesian statistical methodology that provides easily interpretable and meaningful evaluations of data in terms of probability distributions. The EDA case-studies include analyses of herbicide-tolerant GM soybean and insect-protected GM maize and soybean. Bayesian approaches are presented in an analysis of herbicide-tolerant GM soybean. Advantages of these approaches over classical frequentist significance testing include the more direct interpretation of results in terms of probabilities pertaining to quantities of interest and no confusion over the application of corrections for multiple comparisons. It is concluded that a standardized framework for these methodologies could provide specific advantages through enhanced clarity of presentation and interpretation in comparative assessments of crop composition.

GARNET--gene set analysis with exploration of annotation relations.

PubMed

Rho, Kyoohyoung; Kim, Bumjin; Jang, Youngjun; Lee, Sanghyun; Bae, Taejeong; Seo, Jihae; Seo, Chaehwa; Lee, Jihyun; Kang, Hyunjung; Yu, Ungsik; Kim, Sunghoon; Lee, Sanghyuk; Kim, Wan Kyu

2011-02-15

Gene set analysis is a powerful method of deducing biological meaning for an a priori defined set of genes. Numerous tools have been developed to test statistical enrichment or depletion in specific pathways or gene ontology (GO) terms. Major difficulties towards biological interpretation are integrating diverse types of annotation categories and exploring the relationships between annotation terms of similar information. GARNET (Gene Annotation Relationship NEtwork Tools) is an integrative platform for gene set analysis with many novel features. It includes tools for retrieval of genes from annotation database, statistical analysis & visualization of annotation relationships, and managing gene sets. In an effort to allow access to a full spectrum of amassed biological knowledge, we have integrated a variety of annotation data that include the GO, domain, disease, drug, chromosomal location, and custom-defined annotations. Diverse types of molecular networks (pathways, transcription and microRNA regulations, protein-protein interaction) are also included. The pair-wise relationship between annotation gene sets was calculated using kappa statistics. GARNET consists of three modules--gene set manager, gene set analysis and gene set retrieval, which are tightly integrated to provide virtually automatic analysis for gene sets. A dedicated viewer for annotation network has been developed to facilitate exploration of the related annotations. GARNET (gene annotation relationship network tools) is an integrative platform for diverse types of gene set analysis, where complex relationships among gene annotations can be easily explored with an intuitive network visualization tool (http://garnet.isysbio.org/ or http://ercsb.ewha.ac.kr/garnet/).

From Combat to Campus

ERIC Educational Resources Information Center

Bellafiore, Margaret

2012-01-01

Soldiers are returning from war to college. The number of veterans enrolled nationally is hard to find. Data from the National Center for Veterans Analysis and Statistics identify nearly 924,000 veterans as "total education program beneficiaries" for 2011. These statistics combine many categories, including dependents and survivors. The…

Research design and statistical methods in Pakistan Journal of Medical Sciences (PJMS)

PubMed Central

Akhtar, Sohail; Shah, Syed Wadood Ali; Rafiq, M.; Khan, Ajmal

2016-01-01

Objective: This article compares the study design and statistical methods used in 2005, 2010 and 2015 of Pakistan Journal of Medical Sciences (PJMS). Methods: Only original articles of PJMS were considered for the analysis. The articles were carefully reviewed for statistical methods and designs, and then recorded accordingly. The frequency of each statistical method and research design was estimated and compared with previous years. Results: A total of 429 articles were evaluated (n=74 in 2005, n=179 in 2010, n=176 in 2015) in which 171 (40%) were cross-sectional and 116 (27%) were prospective study designs. A verity of statistical methods were found in the analysis. The most frequent methods include: descriptive statistics (n=315, 73.4%), chi-square/Fisher’s exact tests (n=205, 47.8%) and student t-test (n=186, 43.4%). There was a significant increase in the use of statistical methods over time period: t-test, chi-square/Fisher’s exact test, logistic regression, epidemiological statistics, and non-parametric tests. Conclusion: This study shows that a diverse variety of statistical methods have been used in the research articles of PJMS and frequency improved from 2005 to 2015. However, descriptive statistics was the most frequent method of statistical analysis in the published articles while cross-sectional study design was common study design. PMID:27022365

AGR-1 Thermocouple Data Analysis

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

Jeff Einerson

2012-05-01

This report documents an effort to analyze measured and simulated data obtained in the Advanced Gas Reactor (AGR) fuel irradiation test program conducted in the INL's Advanced Test Reactor (ATR) to support the Next Generation Nuclear Plant (NGNP) R&D program. The work follows up on a previous study (Pham and Einerson, 2010), in which statistical analysis methods were applied for AGR-1 thermocouple data qualification. The present work exercises the idea that, while recognizing uncertainties inherent in physics and thermal simulations of the AGR-1 test, results of the numerical simulations can be used in combination with the statistical analysis methods tomore » further improve qualification of measured data. Additionally, the combined analysis of measured and simulation data can generate insights about simulation model uncertainty that can be useful for model improvement. This report also describes an experimental control procedure to maintain fuel target temperature in the future AGR tests using regression relationships that include simulation results. The report is organized into four chapters. Chapter 1 introduces the AGR Fuel Development and Qualification program, AGR-1 test configuration and test procedure, overview of AGR-1 measured data, and overview of physics and thermal simulation, including modeling assumptions and uncertainties. A brief summary of statistical analysis methods developed in (Pham and Einerson 2010) for AGR-1 measured data qualification within NGNP Data Management and Analysis System (NDMAS) is also included for completeness. Chapters 2-3 describe and discuss cases, in which the combined use of experimental and simulation data is realized. A set of issues associated with measurement and modeling uncertainties resulted from the combined analysis are identified. This includes demonstration that such a combined analysis led to important insights for reducing uncertainty in presentation of AGR-1 measured data (Chapter 2) and interpretation of simulation results (Chapter 3). The statistics-based simulation-aided experimental control procedure described for the future AGR tests is developed and demonstrated in Chapter 4. The procedure for controlling the target fuel temperature (capsule peak or average) is based on regression functions of thermocouple readings and other relevant parameters and accounting for possible changes in both physical and thermal conditions and in instrument performance.« less

Significant Association of Urinary Toxic Metals and Autism-Related Symptoms—A Nonlinear Statistical Analysis with Cross Validation

PubMed Central

Adams, James; Kruger, Uwe; Geis, Elizabeth; Gehn, Eva; Fimbres, Valeria; Pollard, Elena; Mitchell, Jessica; Ingram, Julie; Hellmers, Robert; Quig, David; Hahn, Juergen

2017-01-01

Introduction A number of previous studies examined a possible association of toxic metals and autism, and over half of those studies suggest that toxic metal levels are different in individuals with Autism Spectrum Disorders (ASD). Additionally, several studies found that those levels correlate with the severity of ASD. Methods In order to further investigate these points, this paper performs the most detailed statistical analysis to date of a data set in this field. First morning urine samples were collected from 67 children and adults with ASD and 50 neurotypical controls of similar age and gender. The samples were analyzed to determine the levels of 10 urinary toxic metals (UTM). Autism-related symptoms were assessed with eleven behavioral measures. Statistical analysis was used to distinguish participants on the ASD spectrum and neurotypical participants based upon the UTM data alone. The analysis also included examining the association of autism severity with toxic metal excretion data using linear and nonlinear analysis. “Leave-one-out” cross-validation was used to ensure statistical independence of results. Results and Discussion Average excretion levels of several toxic metals (lead, tin, thallium, antimony) were significantly higher in the ASD group. However, ASD classification using univariate statistics proved difficult due to large variability, but nonlinear multivariate statistical analysis significantly improved ASD classification with Type I/II errors of 15% and 18%, respectively. These results clearly indicate that the urinary toxic metal excretion profiles of participants in the ASD group were significantly different from those of the neurotypical participants. Similarly, nonlinear methods determined a significantly stronger association between the behavioral measures and toxic metal excretion. The association was strongest for the Aberrant Behavior Checklist (including subscales on Irritability, Stereotypy, Hyperactivity, and Inappropriate Speech), but significant associations were found for UTM with all eleven autism-related assessments with cross-validation R2 values ranging from 0.12–0.48. PMID:28068407

Frontiers of Two-Dimensional Correlation Spectroscopy. Part 1. New concepts and noteworthy developments

NASA Astrophysics Data System (ADS)

Noda, Isao

2014-07-01

A comprehensive survey review of new and noteworthy developments, which are advancing forward the frontiers in the field of 2D correlation spectroscopy during the last four years, is compiled. This review covers books, proceedings, and review articles published on 2D correlation spectroscopy, a number of significant conceptual developments in the field, data pretreatment methods and other pertinent topics, as well as patent and publication trends and citation activities. Developments discussed include projection 2D correlation analysis, concatenated 2D correlation, and correlation under multiple perturbation effects, as well as orthogonal sample design, predicting 2D correlation spectra, manipulating and comparing 2D spectra, correlation strategy based on segmented data blocks, such as moving-window analysis, features like determination of sequential order and enhanced spectral resolution, statistical 2D spectroscopy using covariance and other statistical metrics, hetero-correlation analysis, and sample-sample correlation technique. Data pretreatment operations prior to 2D correlation analysis are discussed, including the correction for physical effects, background and baseline subtraction, selection of reference spectrum, normalization and scaling of data, derivatives spectra and deconvolution technique, and smoothing and noise reduction. Other pertinent topics include chemometrics and statistical considerations, peak position shift phenomena, variable sampling increments, computation and software, display schemes, such as color coded format, slice and power spectra, tabulation, and other schemes.

Statistical analysis of Skylab 3. [endocrine/metabolic studies of astronauts

NASA Technical Reports Server (NTRS)

Johnston, D. A.

1974-01-01

The results of endocrine/metabolic studies of astronauts on Skylab 3 are reported. One-way analysis of variance, contrasts, two-way unbalanced analysis of variance, and analysis of periodic changes in flight are included. Results for blood tests, and urine tests are presented.

A Matlab user interface for the statistically assisted fluid registration algorithm and tensor-based morphometry

NASA Astrophysics Data System (ADS)

Yepes-Calderon, Fernando; Brun, Caroline; Sant, Nishita; Thompson, Paul; Lepore, Natasha

2015-01-01

Tensor-Based Morphometry (TBM) is an increasingly popular method for group analysis of brain MRI data. The main steps in the analysis consist of a nonlinear registration to align each individual scan to a common space, and a subsequent statistical analysis to determine morphometric differences, or difference in fiber structure between groups. Recently, we implemented the Statistically-Assisted Fluid Registration Algorithm or SAFIRA,1 which is designed for tracking morphometric differences among populations. To this end, SAFIRA allows the inclusion of statistical priors extracted from the populations being studied as regularizers in the registration. This flexibility and degree of sophistication limit the tool to expert use, even more so considering that SAFIRA was initially implemented in command line mode. Here, we introduce a new, intuitive, easy to use, Matlab-based graphical user interface for SAFIRA's multivariate TBM. The interface also generates different choices for the TBM statistics, including both the traditional univariate statistics on the Jacobian matrix, and comparison of the full deformation tensors.2 This software will be freely disseminated to the neuroimaging research community.

Generalization of Entropy Based Divergence Measures for Symbolic Sequence Analysis

PubMed Central

Ré, Miguel A.; Azad, Rajeev K.

2014-01-01

Entropy based measures have been frequently used in symbolic sequence analysis. A symmetrized and smoothed form of Kullback-Leibler divergence or relative entropy, the Jensen-Shannon divergence (JSD), is of particular interest because of its sharing properties with families of other divergence measures and its interpretability in different domains including statistical physics, information theory and mathematical statistics. The uniqueness and versatility of this measure arise because of a number of attributes including generalization to any number of probability distributions and association of weights to the distributions. Furthermore, its entropic formulation allows its generalization in different statistical frameworks, such as, non-extensive Tsallis statistics and higher order Markovian statistics. We revisit these generalizations and propose a new generalization of JSD in the integrated Tsallis and Markovian statistical framework. We show that this generalization can be interpreted in terms of mutual information. We also investigate the performance of different JSD generalizations in deconstructing chimeric DNA sequences assembled from bacterial genomes including that of E. coli, S. enterica typhi, Y. pestis and H. influenzae. Our results show that the JSD generalizations bring in more pronounced improvements when the sequences being compared are from phylogenetically proximal organisms, which are often difficult to distinguish because of their compositional similarity. While small but noticeable improvements were observed with the Tsallis statistical JSD generalization, relatively large improvements were observed with the Markovian generalization. In contrast, the proposed Tsallis-Markovian generalization yielded more pronounced improvements relative to the Tsallis and Markovian generalizations, specifically when the sequences being compared arose from phylogenetically proximal organisms. PMID:24728338

Generalization of entropy based divergence measures for symbolic sequence analysis.

PubMed

Ré, Miguel A; Azad, Rajeev K

2014-01-01

Entropy based measures have been frequently used in symbolic sequence analysis. A symmetrized and smoothed form of Kullback-Leibler divergence or relative entropy, the Jensen-Shannon divergence (JSD), is of particular interest because of its sharing properties with families of other divergence measures and its interpretability in different domains including statistical physics, information theory and mathematical statistics. The uniqueness and versatility of this measure arise because of a number of attributes including generalization to any number of probability distributions and association of weights to the distributions. Furthermore, its entropic formulation allows its generalization in different statistical frameworks, such as, non-extensive Tsallis statistics and higher order Markovian statistics. We revisit these generalizations and propose a new generalization of JSD in the integrated Tsallis and Markovian statistical framework. We show that this generalization can be interpreted in terms of mutual information. We also investigate the performance of different JSD generalizations in deconstructing chimeric DNA sequences assembled from bacterial genomes including that of E. coli, S. enterica typhi, Y. pestis and H. influenzae. Our results show that the JSD generalizations bring in more pronounced improvements when the sequences being compared are from phylogenetically proximal organisms, which are often difficult to distinguish because of their compositional similarity. While small but noticeable improvements were observed with the Tsallis statistical JSD generalization, relatively large improvements were observed with the Markovian generalization. In contrast, the proposed Tsallis-Markovian generalization yielded more pronounced improvements relative to the Tsallis and Markovian generalizations, specifically when the sequences being compared arose from phylogenetically proximal organisms.

Improved Statistics for Genome-Wide Interaction Analysis

PubMed Central

Ueki, Masao; Cordell, Heather J.

2012-01-01

Recently, Wu and colleagues [1] proposed two novel statistics for genome-wide interaction analysis using case/control or case-only data. In computer simulations, their proposed case/control statistic outperformed competing approaches, including the fast-epistasis option in PLINK and logistic regression analysis under the correct model; however, reasons for its superior performance were not fully explored. Here we investigate the theoretical properties and performance of Wu et al.'s proposed statistics and explain why, in some circumstances, they outperform competing approaches. Unfortunately, we find minor errors in the formulae for their statistics, resulting in tests that have higher than nominal type 1 error. We also find minor errors in PLINK's fast-epistasis and case-only statistics, although theory and simulations suggest that these errors have only negligible effect on type 1 error. We propose adjusted versions of all four statistics that, both theoretically and in computer simulations, maintain correct type 1 error rates under the null hypothesis. We also investigate statistics based on correlation coefficients that maintain similar control of type 1 error. Although designed to test specifically for interaction, we show that some of these previously-proposed statistics can, in fact, be sensitive to main effects at one or both loci, particularly in the presence of linkage disequilibrium. We propose two new “joint effects” statistics that, provided the disease is rare, are sensitive only to genuine interaction effects. In computer simulations we find, in most situations considered, that highest power is achieved by analysis under the correct genetic model. Such an analysis is unachievable in practice, as we do not know this model. However, generally high power over a wide range of scenarios is exhibited by our joint effects and adjusted Wu statistics. We recommend use of these alternative or adjusted statistics and urge caution when using Wu et al.'s originally-proposed statistics, on account of the inflated error rate that can result. PMID:22496670

Analysis strategies for longitudinal attachment loss data.

PubMed

Beck, J D; Elter, J R

2000-02-01

The purpose of this invited review is to describe and discuss methods currently in use to quantify the progression of attachment loss in epidemiological studies of periodontal disease, and to make recommendations for specific analytic methods based upon the particular design of the study and structure of the data. The review concentrates on the definition of incident attachment loss (ALOSS) and its component parts; measurement issues including thresholds and regression to the mean; methods of accounting for longitudinal change, including changes in means, changes in proportions of affected sites, incidence density, the effect of tooth loss and reversals, and repeated events; statistical models of longitudinal change, including the incorporation of the time element, use of linear, logistic or Poisson regression or survival analysis, and statistical tests; site vs person level of analysis, including statistical adjustment for correlated data; the strengths and limitations of ALOSS data. Examples from the Piedmont 65+ Dental Study are used to illustrate specific concepts. We conclude that incidence density is the preferred methodology to use for periodontal studies with more than one period of follow-up and that the use of studies not employing methods for dealing with complex samples, correlated data, and repeated measures does not take advantage of our current understanding of the site- and person-level variables important in periodontal disease and may generate biased results.

Noise limitations in optical linear algebra processors.

PubMed

Batsell, S G; Jong, T L; Walkup, J F; Krile, T F

1990-05-10

A general statistical noise model is presented for optical linear algebra processors. A statistical analysis which includes device noise, the multiplication process, and the addition operation is undertaken. We focus on those processes which are architecturally independent. Finally, experimental results which verify the analytical predictions are also presented.

ASURV: Astronomical SURVival Statistics

NASA Astrophysics Data System (ADS)

Feigelson, E. D.; Nelson, P. I.; Isobe, T.; LaValley, M.

2014-06-01

ASURV (Astronomical SURVival Statistics) provides astronomy survival analysis for right- and left-censored data including the maximum-likelihood Kaplan-Meier estimator and several univariate two-sample tests, bivariate correlation measures, and linear regressions. ASURV is written in FORTRAN 77, and is stand-alone and does not call any specialized libraries.

An introduction to Bayesian statistics in health psychology.

PubMed

Depaoli, Sarah; Rus, Holly M; Clifton, James P; van de Schoot, Rens; Tiemensma, Jitske

2017-09-01

The aim of the current article is to provide a brief introduction to Bayesian statistics within the field of health psychology. Bayesian methods are increasing in prevalence in applied fields, and they have been shown in simulation research to improve the estimation accuracy of structural equation models, latent growth curve (and mixture) models, and hierarchical linear models. Likewise, Bayesian methods can be used with small sample sizes since they do not rely on large sample theory. In this article, we discuss several important components of Bayesian statistics as they relate to health-based inquiries. We discuss the incorporation and impact of prior knowledge into the estimation process and the different components of the analysis that should be reported in an article. We present an example implementing Bayesian estimation in the context of blood pressure changes after participants experienced an acute stressor. We conclude with final thoughts on the implementation of Bayesian statistics in health psychology, including suggestions for reviewing Bayesian manuscripts and grant proposals. We have also included an extensive amount of online supplementary material to complement the content presented here, including Bayesian examples using many different software programmes and an extensive sensitivity analysis examining the impact of priors.

Misuse of statistical methods: critical assessment of articles in BMJ from January to March 1976.

PubMed Central

Gore, S M; Jones, I G; Rytter, E C

1977-01-01

Sixty-two reports that appeared as Papers and Originals (excluding short reports) in 13 consecutive issues of the British Medical journal included statistical analysis. Thirty-two had statistical errors of one kind or another; in 18 fairly serious faults were discovered. The summaries of five reports made some claim that was unsupportable on re-examination of the data. Medical investigators should consult with people who have a real understanding of statistical methods throughout their projects. PMID:832023

Australia 31-GHz brightness temperature exceedance statistics

NASA Technical Reports Server (NTRS)

Gary, B. L.

1988-01-01

Water vapor radiometer measurements were made at DSS 43 during an 18 month period. Brightness temperatures at 31 GHz were subjected to a statistical analysis which included correction for the effects of occasional water on the radiometer radome. An exceedance plot was constructed, and the 1 percent exceedance statistics occurs at 120 K. The 5 percent exceedance statistics occurs at 70 K, compared with 75 K in Spain. These values are valid for all of the three month groupings that were studied.

Systematic Review and Meta-Analysis of Studies Evaluating Diagnostic Test Accuracy: A Practical Review for Clinical Researchers-Part II. Statistical Methods of Meta-Analysis

PubMed Central

Lee, Juneyoung; Kim, Kyung Won; Choi, Sang Hyun; Huh, Jimi

2015-01-01

Meta-analysis of diagnostic test accuracy studies differs from the usual meta-analysis of therapeutic/interventional studies in that, it is required to simultaneously analyze a pair of two outcome measures such as sensitivity and specificity, instead of a single outcome. Since sensitivity and specificity are generally inversely correlated and could be affected by a threshold effect, more sophisticated statistical methods are required for the meta-analysis of diagnostic test accuracy. Hierarchical models including the bivariate model and the hierarchical summary receiver operating characteristic model are increasingly being accepted as standard methods for meta-analysis of diagnostic test accuracy studies. We provide a conceptual review of statistical methods currently used and recommended for meta-analysis of diagnostic test accuracy studies. This article could serve as a methodological reference for those who perform systematic review and meta-analysis of diagnostic test accuracy studies. PMID:26576107

Risk of thromboembolism with thrombopoietin receptor agonists in adult patients with thrombocytopenia: Systematic review and meta-analysis of randomized controlled trials.

PubMed

Catalá-López, Ferrán; Corrales, Inmaculada; de la Fuente-Honrubia, César; González-Bermejo, Diana; Martín-Serrano, Gloria; Montero, Dolores; Saint-Gerons, Diego Macías

2015-12-21

Romiplostim and eltrombopag are thrombopoietin receptor (TPOr) agonists that promote megakaryocyte differentiation, proliferation and platelet production. In 2012, a systematic review and meta-analysis reported a non-statistically significant increased risk of thromboembolic events for these drugs, but analyses were limited by lack of statistical power. Our objective was to update the 2012 meta-analysis examining whether TPOr agonists affect thromboembolism occurrence in adult thrombocytopenic patients. We conducted a systematic review and meta-analysis of randomized controlled trials (RCTs). Updated searches were conduced on PubMed, Cochrane Central, and publicly available registries (up to December 2014). RCTs using romiplostim or eltrombopag in at least one group were included. Relative risks (RR), absolute risk ratios (ARR) and number needed to harm (NNH) were estimated. Heterogeneity was analyzed using Cochran's Q test and I(2) statistic. Fifteen studies with 3026 adult thrombocytopenic patients were included. Estimated frequency of thromboembolism was 3.69% (95% CI: 2.95-4.61%) for TPOr agonists and 1.46% (95% CI: 0.89-2.40%) for controls. TPOr agonists were associated with a RR of thromboembolism of 1.81 (95% CI: 1.04-3.14) and an ARR of 2.10% (95% CI: 0.03-3.90%) meaning a NNH of 48. Overall, we did not find evidence of statistical heterogeneity (p=0.43; I(2)=1.60%). Our updated meta-analysis suggested that TPOr agonists are associated with a higher risk of thromboemboembolic events compared with controls, and supports the current recommendations included in the European product information on this respect. Copyright © 2015 Elsevier España, S.L.U. All rights reserved.

Algorithm for Identifying Erroneous Rain-Gauge Readings

NASA Technical Reports Server (NTRS)

Rickman, Doug

2005-01-01

An algorithm analyzes rain-gauge data to identify statistical outliers that could be deemed to be erroneous readings. Heretofore, analyses of this type have been performed in burdensome manual procedures that have involved subjective judgements. Sometimes, the analyses have included computational assistance for detecting values falling outside of arbitrary limits. The analyses have been performed without statistically valid knowledge of the spatial and temporal variations of precipitation within rain events. In contrast, the present algorithm makes it possible to automate such an analysis, makes the analysis objective, takes account of the spatial distribution of rain gauges in conjunction with the statistical nature of spatial variations in rainfall readings, and minimizes the use of arbitrary criteria. The algorithm implements an iterative process that involves nonparametric statistics.

Event coincidence analysis for quantifying statistical interrelationships between event time series. On the role of flood events as triggers of epidemic outbreaks

NASA Astrophysics Data System (ADS)

Donges, J. F.; Schleussner, C.-F.; Siegmund, J. F.; Donner, R. V.

2016-05-01

Studying event time series is a powerful approach for analyzing the dynamics of complex dynamical systems in many fields of science. In this paper, we describe the method of event coincidence analysis to provide a framework for quantifying the strength, directionality and time lag of statistical interrelationships between event series. Event coincidence analysis allows to formulate and test null hypotheses on the origin of the observed interrelationships including tests based on Poisson processes or, more generally, stochastic point processes with a prescribed inter-event time distribution and other higher-order properties. Applying the framework to country-level observational data yields evidence that flood events have acted as triggers of epidemic outbreaks globally since the 1950s. Facing projected future changes in the statistics of climatic extreme events, statistical techniques such as event coincidence analysis will be relevant for investigating the impacts of anthropogenic climate change on human societies and ecosystems worldwide.

Linnorm: improved statistical analysis for single cell RNA-seq expression data.

PubMed

Yip, Shun H; Wang, Panwen; Kocher, Jean-Pierre A; Sham, Pak Chung; Wang, Junwen

2017-12-15

Linnorm is a novel normalization and transformation method for the analysis of single cell RNA sequencing (scRNA-seq) data. Linnorm is developed to remove technical noises and simultaneously preserve biological variations in scRNA-seq data, such that existing statistical methods can be improved. Using real scRNA-seq data, we compared Linnorm with existing normalization methods, including NODES, SAMstrt, SCnorm, scran, DESeq and TMM. Linnorm shows advantages in speed, technical noise removal and preservation of cell heterogeneity, which can improve existing methods in the discovery of novel subtypes, pseudo-temporal ordering of cells, clustering analysis, etc. Linnorm also performs better than existing DEG analysis methods, including BASiCS, NODES, SAMstrt, Seurat and DESeq2, in false positive rate control and accuracy. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Experimental analysis of computer system dependability

NASA Technical Reports Server (NTRS)

Iyer, Ravishankar, K.; Tang, Dong

1993-01-01

This paper reviews an area which has evolved over the past 15 years: experimental analysis of computer system dependability. Methodologies and advances are discussed for three basic approaches used in the area: simulated fault injection, physical fault injection, and measurement-based analysis. The three approaches are suited, respectively, to dependability evaluation in the three phases of a system's life: design phase, prototype phase, and operational phase. Before the discussion of these phases, several statistical techniques used in the area are introduced. For each phase, a classification of research methods or study topics is outlined, followed by discussion of these methods or topics as well as representative studies. The statistical techniques introduced include the estimation of parameters and confidence intervals, probability distribution characterization, and several multivariate analysis methods. Importance sampling, a statistical technique used to accelerate Monte Carlo simulation, is also introduced. The discussion of simulated fault injection covers electrical-level, logic-level, and function-level fault injection methods as well as representative simulation environments such as FOCUS and DEPEND. The discussion of physical fault injection covers hardware, software, and radiation fault injection methods as well as several software and hybrid tools including FIAT, FERARI, HYBRID, and FINE. The discussion of measurement-based analysis covers measurement and data processing techniques, basic error characterization, dependency analysis, Markov reward modeling, software-dependability, and fault diagnosis. The discussion involves several important issues studies in the area, including fault models, fast simulation techniques, workload/failure dependency, correlated failures, and software fault tolerance.

Landing Site Dispersion Analysis and Statistical Assessment for the Mars Phoenix Lander

NASA Technical Reports Server (NTRS)

Bonfiglio, Eugene P.; Adams, Douglas; Craig, Lynn; Spencer, David A.; Strauss, William; Seelos, Frank P.; Seelos, Kimberly D.; Arvidson, Ray; Heet, Tabatha

2008-01-01

The Mars Phoenix Lander launched on August 4, 2007 and successfully landed on Mars 10 months later on May 25, 2008. Landing ellipse predicts and hazard maps were key in selecting safe surface targets for Phoenix. Hazard maps were based on terrain slopes, geomorphology maps and automated rock counts of MRO's High Resolution Imaging Science Experiment (HiRISE) images. The expected landing dispersion which led to the selection of Phoenix's surface target is discussed as well as the actual landing dispersion predicts determined during operations in the weeks, days, and hours before landing. A statistical assessment of these dispersions is performed, comparing the actual landing-safety probabilities to criteria levied by the project. Also discussed are applications for this statistical analysis which were used by the Phoenix project. These include using the statistical analysis used to verify the effectiveness of a pre-planned maneuver menu and calculating the probability of future maneuvers.

SPA- STATISTICAL PACKAGE FOR TIME AND FREQUENCY DOMAIN ANALYSIS

NASA Technical Reports Server (NTRS)

Brownlow, J. D.

1994-01-01

The need for statistical analysis often arises when data is in the form of a time series. This type of data is usually a collection of numerical observations made at specified time intervals. Two kinds of analysis may be performed on the data. First, the time series may be treated as a set of independent observations using a time domain analysis to derive the usual statistical properties including the mean, variance, and distribution form. Secondly, the order and time intervals of the observations may be used in a frequency domain analysis to examine the time series for periodicities. In almost all practical applications, the collected data is actually a mixture of the desired signal and a noise signal which is collected over a finite time period with a finite precision. Therefore, any statistical calculations and analyses are actually estimates. The Spectrum Analysis (SPA) program was developed to perform a wide range of statistical estimation functions. SPA can provide the data analyst with a rigorous tool for performing time and frequency domain studies. In a time domain statistical analysis the SPA program will compute the mean variance, standard deviation, mean square, and root mean square. It also lists the data maximum, data minimum, and the number of observations included in the sample. In addition, a histogram of the time domain data is generated, a normal curve is fit to the histogram, and a goodness-of-fit test is performed. These time domain calculations may be performed on both raw and filtered data. For a frequency domain statistical analysis the SPA program computes the power spectrum, cross spectrum, coherence, phase angle, amplitude ratio, and transfer function. The estimates of the frequency domain parameters may be smoothed with the use of Hann-Tukey, Hamming, Barlett, or moving average windows. Various digital filters are available to isolate data frequency components. Frequency components with periods longer than the data collection interval are removed by least-squares detrending. As many as ten channels of data may be analyzed at one time. Both tabular and plotted output may be generated by the SPA program. This program is written in FORTRAN IV and has been implemented on a CDC 6000 series computer with a central memory requirement of approximately 142K (octal) of 60 bit words. This core requirement can be reduced by segmentation of the program. The SPA program was developed in 1978.

WASP (Write a Scientific Paper) using Excel - 6: Standard error and confidence interval.

PubMed

Grech, Victor

2018-03-01

The calculation of descriptive statistics includes the calculation of standard error and confidence interval, an inevitable component of data analysis in inferential statistics. This paper provides pointers as to how to do this in Microsoft Excel™. Copyright © 2018 Elsevier B.V. All rights reserved.

Analyzing a Mature Software Inspection Process Using Statistical Process Control (SPC)

NASA Technical Reports Server (NTRS)

Barnard, Julie; Carleton, Anita; Stamper, Darrell E. (Technical Monitor)

1999-01-01

This paper presents a cooperative effort where the Software Engineering Institute and the Space Shuttle Onboard Software Project could experiment applying Statistical Process Control (SPC) analysis to inspection activities. The topics include: 1) SPC Collaboration Overview; 2) SPC Collaboration Approach and Results; and 3) Lessons Learned.

Genetic structure of populations and differentiation in forest trees

Treesearch

Raymond P. Guries; F. Thomas Ledig

1981-01-01

Electrophoretic techniques permit population biologists to analyze genetic structure of natural populations by using large numbers of allozyme loci. Several methods of analysis have been applied to allozyme data, including chi-square contingency tests, F-statistics, and genetic distance. This paper compares such statistics for pitch pine (Pinus rigida...

PANDA-view: An easy-to-use tool for statistical analysis and visualization of quantitative proteomics data.

PubMed

Chang, Cheng; Xu, Kaikun; Guo, Chaoping; Wang, Jinxia; Yan, Qi; Zhang, Jian; He, Fuchu; Zhu, Yunping

2018-05-22

Compared with the numerous software tools developed for identification and quantification of -omics data, there remains a lack of suitable tools for both downstream analysis and data visualization. To help researchers better understand the biological meanings in their -omics data, we present an easy-to-use tool, named PANDA-view, for both statistical analysis and visualization of quantitative proteomics data and other -omics data. PANDA-view contains various kinds of analysis methods such as normalization, missing value imputation, statistical tests, clustering and principal component analysis, as well as the most commonly-used data visualization methods including an interactive volcano plot. Additionally, it provides user-friendly interfaces for protein-peptide-spectrum representation of the quantitative proteomics data. PANDA-view is freely available at https://sourceforge.net/projects/panda-view/. 1987ccpacer@163.com and zhuyunping@gmail.com. Supplementary data are available at Bioinformatics online.

Measuring the statistical validity of summary meta-analysis and meta-regression results for use in clinical practice.

PubMed

Willis, Brian H; Riley, Richard D

2017-09-20

An important question for clinicians appraising a meta-analysis is: are the findings likely to be valid in their own practice-does the reported effect accurately represent the effect that would occur in their own clinical population? To this end we advance the concept of statistical validity-where the parameter being estimated equals the corresponding parameter for a new independent study. Using a simple ('leave-one-out') cross-validation technique, we demonstrate how we may test meta-analysis estimates for statistical validity using a new validation statistic, Vn, and derive its distribution. We compare this with the usual approach of investigating heterogeneity in meta-analyses and demonstrate the link between statistical validity and homogeneity. Using a simulation study, the properties of Vn and the Q statistic are compared for univariate random effects meta-analysis and a tailored meta-regression model, where information from the setting (included as model covariates) is used to calibrate the summary estimate to the setting of application. Their properties are found to be similar when there are 50 studies or more, but for fewer studies Vn has greater power but a higher type 1 error rate than Q. The power and type 1 error rate of Vn are also shown to depend on the within-study variance, between-study variance, study sample size, and the number of studies in the meta-analysis. Finally, we apply Vn to two published meta-analyses and conclude that it usefully augments standard methods when deciding upon the likely validity of summary meta-analysis estimates in clinical practice. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

On the Application of Syntactic Methodologies in Automatic Text Analysis.

ERIC Educational Resources Information Center

Salton, Gerard; And Others

1990-01-01

Summarizes various linguistic approaches proposed for document analysis in information retrieval environments. Topics discussed include syntactic analysis; use of machine-readable dictionary information; knowledge base construction; the PLNLP English Grammar (PEG) system; phrase normalization; and statistical and syntactic phrase evaluation used…

Random forests for classification in ecology

USGS Publications Warehouse

Cutler, D.R.; Edwards, T.C.; Beard, K.H.; Cutler, A.; Hess, K.T.; Gibson, J.; Lawler, J.J.

2007-01-01

Classification procedures are some of the most widely used statistical methods in ecology. Random forests (RF) is a new and powerful statistical classifier that is well established in other disciplines but is relatively unknown in ecology. Advantages of RF compared to other statistical classifiers include (1) very high classification accuracy; (2) a novel method of determining variable importance; (3) ability to model complex interactions among predictor variables; (4) flexibility to perform several types of statistical data analysis, including regression, classification, survival analysis, and unsupervised learning; and (5) an algorithm for imputing missing values. We compared the accuracies of RF and four other commonly used statistical classifiers using data on invasive plant species presence in Lava Beds National Monument, California, USA, rare lichen species presence in the Pacific Northwest, USA, and nest sites for cavity nesting birds in the Uinta Mountains, Utah, USA. We observed high classification accuracy in all applications as measured by cross-validation and, in the case of the lichen data, by independent test data, when comparing RF to other common classification methods. We also observed that the variables that RF identified as most important for classifying invasive plant species coincided with expectations based on the literature. ?? 2007 by the Ecological Society of America.

Advanced statistical methods for improved data analysis of NASA astrophysics missions

NASA Technical Reports Server (NTRS)

Feigelson, Eric D.

1992-01-01

The investigators under this grant studied ways to improve the statistical analysis of astronomical data. They looked at existing techniques, the development of new techniques, and the production and distribution of specialized software to the astronomical community. Abstracts of nine papers that were produced are included, as well as brief descriptions of four software packages. The articles that are abstracted discuss analytical and Monte Carlo comparisons of six different linear least squares fits, a (second) paper on linear regression in astronomy, two reviews of public domain software for the astronomer, subsample and half-sample methods for estimating sampling distributions, a nonparametric estimation of survival functions under dependent competing risks, censoring in astronomical data due to nondetections, an astronomy survival analysis computer package called ASURV, and improving the statistical methodology of astronomical data analysis.

Scaling up to address data science challenges

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

Wendelberger, Joanne R.

Statistics and Data Science provide a variety of perspectives and technical approaches for exploring and understanding Big Data. Partnerships between scientists from different fields such as statistics, machine learning, computer science, and applied mathematics can lead to innovative approaches for addressing problems involving increasingly large amounts of data in a rigorous and effective manner that takes advantage of advances in computing. Here, this article will explore various challenges in Data Science and will highlight statistical approaches that can facilitate analysis of large-scale data including sampling and data reduction methods, techniques for effective analysis and visualization of large-scale simulations, and algorithmsmore » and procedures for efficient processing.« less

Statistical innovations in the medical device world sparked by the FDA.

PubMed

Campbell, Gregory; Yue, Lilly Q

2016-01-01

The world of medical devices while highly diverse is extremely innovative, and this facilitates the adoption of innovative statistical techniques. Statisticians in the Center for Devices and Radiological Health (CDRH) at the Food and Drug Administration (FDA) have provided leadership in implementing statistical innovations. The innovations discussed include: the incorporation of Bayesian methods in clinical trials, adaptive designs, the use and development of propensity score methodology in the design and analysis of non-randomized observational studies, the use of tipping-point analysis for missing data, techniques for diagnostic test evaluation, bridging studies for companion diagnostic tests, quantitative benefit-risk decisions, and patient preference studies.

Scaling up to address data science challenges

DOE PAGES

Wendelberger, Joanne R.

2017-04-27

Statistics and Data Science provide a variety of perspectives and technical approaches for exploring and understanding Big Data. Partnerships between scientists from different fields such as statistics, machine learning, computer science, and applied mathematics can lead to innovative approaches for addressing problems involving increasingly large amounts of data in a rigorous and effective manner that takes advantage of advances in computing. Here, this article will explore various challenges in Data Science and will highlight statistical approaches that can facilitate analysis of large-scale data including sampling and data reduction methods, techniques for effective analysis and visualization of large-scale simulations, and algorithmsmore » and procedures for efficient processing.« less

Cognition, comprehension and application of biostatistics in research by Indian postgraduate students in periodontics.

PubMed

Swetha, Jonnalagadda Laxmi; Arpita, Ramisetti; Srikanth, Chintalapani; Nutalapati, Rajasekhar

2014-01-01

Biostatistics is an integral part of research protocols. In any field of inquiry or investigation, data obtained is subsequently classified, analyzed and tested for accuracy by statistical methods. Statistical analysis of collected data, thus, forms the basis for all evidence-based conclusions. The aim of this study is to evaluate the cognition, comprehension and application of biostatistics in research among post graduate students in Periodontics, in India. A total of 391 post graduate students registered for a master's course in periodontics at various dental colleges across India were included in the survey. Data regarding the level of knowledge, understanding and its application in design and conduct of the research protocol was collected using a dichotomous questionnaire. A descriptive statistics was used for data analysis. Nearly 79.2% students were aware of the importance of biostatistics in research, 55-65% were familiar with MS-EXCEL spreadsheet for graphical representation of data and with the statistical softwares available on the internet, 26.0% had biostatistics as mandatory subject in their curriculum, 9.5% tried to perform statistical analysis on their own while 3.0% were successful in performing statistical analysis of their studies on their own. Biostatistics should play a central role in planning, conduct, interim analysis, final analysis and reporting of periodontal research especially by the postgraduate students. Indian postgraduate students in periodontics are aware of the importance of biostatistics in research but the level of understanding and application is still basic and needs to be addressed.

Analysis and interpretation of cost data in randomised controlled trials: review of published studies

PubMed Central

Barber, Julie A; Thompson, Simon G

1998-01-01

Objective To review critically the statistical methods used for health economic evaluations in randomised controlled trials where an estimate of cost is available for each patient in the study. Design Survey of published randomised trials including an economic evaluation with cost values suitable for statistical analysis; 45 such trials published in 1995 were identified from Medline. Main outcome measures The use of statistical methods for cost data was assessed in terms of the descriptive statistics reported, use of statistical inference, and whether the reported conclusions were justified. Results Although all 45 trials reviewed apparently had cost data for each patient, only 9 (20%) reported adequate measures of variability for these data and only 25 (56%) gave results of statistical tests or a measure of precision for the comparison of costs between the randomised groups. Only 16 (36%) of the articles gave conclusions which were justified on the basis of results presented in the paper. No paper reported sample size calculations for costs. Conclusions The analysis and interpretation of cost data from published trials reveal a lack of statistical awareness. Strong and potentially misleading conclusions about the relative costs of alternative therapies have often been reported in the absence of supporting statistical evidence. Improvements in the analysis and reporting of health economic assessments are urgently required. Health economic guidelines need to be revised to incorporate more detailed statistical advice. Key messagesHealth economic evaluations required for important healthcare policy decisions are often carried out in randomised controlled trialsA review of such published economic evaluations assessed whether statistical methods for cost outcomes have been appropriately used and interpretedFew publications presented adequate descriptive information for costs or performed appropriate statistical analysesIn at least two thirds of the papers, the main conclusions regarding costs were not justifiedThe analysis and reporting of health economic assessments within randomised controlled trials urgently need improving PMID:9794854

Use of model calibration to achieve high accuracy in analysis of computer networks

DOEpatents

Frogner, Bjorn; Guarro, Sergio; Scharf, Guy

2004-05-11

A system and method are provided for creating a network performance prediction model, and calibrating the prediction model, through application of network load statistical analyses. The method includes characterizing the measured load on the network, which may include background load data obtained over time, and may further include directed load data representative of a transaction-level event. Probabilistic representations of load data are derived to characterize the statistical persistence of the network performance variability and to determine delays throughout the network. The probabilistic representations are applied to the network performance prediction model to adapt the model for accurate prediction of network performance. Certain embodiments of the method and system may be used for analysis of the performance of a distributed application characterized as data packet streams.

Compression Algorithm Analysis of In-Situ (S)TEM Video: Towards Automatic Event Detection and Characterization

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

Teuton, Jeremy R.; Griswold, Richard L.; Mehdi, Beata L.

Precise analysis of both (S)TEM images and video are time and labor intensive processes. As an example, determining when crystal growth and shrinkage occurs during the dynamic process of Li dendrite deposition and stripping involves manually scanning through each frame in the video to extract a specific set of frames/images. For large numbers of images, this process can be very time consuming, so a fast and accurate automated method is desirable. Given this need, we developed software that uses analysis of video compression statistics for detecting and characterizing events in large data sets. This software works by converting the datamore » into a series of images which it compresses into an MPEG-2 video using the open source “avconv” utility [1]. The software does not use the video itself, but rather analyzes the video statistics from the first pass of the video encoding that avconv records in the log file. This file contains statistics for each frame of the video including the frame quality, intra-texture and predicted texture bits, forward and backward motion vector resolution, among others. In all, avconv records 15 statistics for each frame. By combining different statistics, we have been able to detect events in various types of data. We have developed an interactive tool for exploring the data and the statistics that aids the analyst in selecting useful statistics for each analysis. Going forward, an algorithm for detecting and possibly describing events automatically can be written based on statistic(s) for each data type.« less

Developing a Campaign Plan to Target Centers of Gravity Within Economic Systems

DTIC Science & Technology

1995-05-01

Conclusion 67 CHAPTER 7: CURRENT AND FUTURE CONCERNS 69 Decision Making and Planning 69 Conclusion 72 CHAPTER 8: CONCLUSION 73 APPENDIX A: STATISTICS 80...Terminology and Statistical Tests 80 Country Analysis 84 APPENDIX B 154 BIBLIOGRAPHY 157 VITAE 162 IV LIST OF FIGURES Figure 1. Air Campaign...This project furthers the original statistical effort and adds to this a campaign planning approach (including both systems and operational level

The Ontology of Biological and Clinical Statistics (OBCS) for standardized and reproducible statistical analysis.

PubMed

Zheng, Jie; Harris, Marcelline R; Masci, Anna Maria; Lin, Yu; Hero, Alfred; Smith, Barry; He, Yongqun

2016-09-14

Statistics play a critical role in biological and clinical research. However, most reports of scientific results in the published literature make it difficult for the reader to reproduce the statistical analyses performed in achieving those results because they provide inadequate documentation of the statistical tests and algorithms applied. The Ontology of Biological and Clinical Statistics (OBCS) is put forward here as a step towards solving this problem. The terms in OBCS including 'data collection', 'data transformation in statistics', 'data visualization', 'statistical data analysis', and 'drawing a conclusion based on data', cover the major types of statistical processes used in basic biological research and clinical outcome studies. OBCS is aligned with the Basic Formal Ontology (BFO) and extends the Ontology of Biomedical Investigations (OBI), an OBO (Open Biological and Biomedical Ontologies) Foundry ontology supported by over 20 research communities. Currently, OBCS comprehends 878 terms, representing 20 BFO classes, 403 OBI classes, 229 OBCS specific classes, and 122 classes imported from ten other OBO ontologies. We discuss two examples illustrating how the ontology is being applied. In the first (biological) use case, we describe how OBCS was applied to represent the high throughput microarray data analysis of immunological transcriptional profiles in human subjects vaccinated with an influenza vaccine. In the second (clinical outcomes) use case, we applied OBCS to represent the processing of electronic health care data to determine the associations between hospital staffing levels and patient mortality. Our case studies were designed to show how OBCS can be used for the consistent representation of statistical analysis pipelines under two different research paradigms. Other ongoing projects using OBCS for statistical data processing are also discussed. The OBCS source code and documentation are available at: https://github.com/obcs/obcs . The Ontology of Biological and Clinical Statistics (OBCS) is a community-based open source ontology in the domain of biological and clinical statistics. OBCS is a timely ontology that represents statistics-related terms and their relations in a rigorous fashion, facilitates standard data analysis and integration, and supports reproducible biological and clinical research.

Biological Parametric Mapping: A Statistical Toolbox for Multi-Modality Brain Image Analysis

PubMed Central

Casanova, Ramon; Ryali, Srikanth; Baer, Aaron; Laurienti, Paul J.; Burdette, Jonathan H.; Hayasaka, Satoru; Flowers, Lynn; Wood, Frank; Maldjian, Joseph A.

2006-01-01

In recent years multiple brain MR imaging modalities have emerged; however, analysis methodologies have mainly remained modality specific. In addition, when comparing across imaging modalities, most researchers have been forced to rely on simple region-of-interest type analyses, which do not allow the voxel-by-voxel comparisons necessary to answer more sophisticated neuroscience questions. To overcome these limitations, we developed a toolbox for multimodal image analysis called biological parametric mapping (BPM), based on a voxel-wise use of the general linear model. The BPM toolbox incorporates information obtained from other modalities as regressors in a voxel-wise analysis, thereby permitting investigation of more sophisticated hypotheses. The BPM toolbox has been developed in MATLAB with a user friendly interface for performing analyses, including voxel-wise multimodal correlation, ANCOVA, and multiple regression. It has a high degree of integration with the SPM (statistical parametric mapping) software relying on it for visualization and statistical inference. Furthermore, statistical inference for a correlation field, rather than a widely-used T-field, has been implemented in the correlation analysis for more accurate results. An example with in-vivo data is presented demonstrating the potential of the BPM methodology as a tool for multimodal image analysis. PMID:17070709

Higher Education: Gaps in Access and Persistence Study. Statistical Analysis Report. NCES 2012-046

ERIC Educational Resources Information Center

Ross, Terris; Kena, Grace; Rathbun, Amy; KewalRamani, Angelina; Zhang, Jijun; Kristapovich, Paul; Manning, Eileen

2012-01-01

Numerous studies, including those of the National Center for Education Statistics (NCES), have documented persistent gaps between the educational attainment of White males and that of Black, Hispanic, American Indian/Alaska Native, and Native Hawaiian/Pacific Islander males. Further, there is evidence of growing gaps by sex within these…

[Artificial neural networks for decision making in urologic oncology].

PubMed

Remzi, M; Djavan, B

2007-06-01

This chapter presents a detailed introduction regarding Artificial Neural Networks (ANNs) and their contribution to modern Urologic Oncology. It includes a description of ANNs methodology and points out the differences between Artifical Intelligence and traditional statistic models in terms of usefulness for patients and clinicians, and its advantages over current statistical analysis.

The Effects of Measurement Error on Statistical Models for Analyzing Change. Final Report.

ERIC Educational Resources Information Center

Dunivant, Noel

The results of six major projects are discussed including a comprehensive mathematical and statistical analysis of the problems caused by errors of measurement in linear models for assessing change. In a general matrix representation of the problem, several new analytic results are proved concerning the parameters which affect bias in…

Nebraska's forests, 2005: statistics, methods, and quality assurance

Treesearch

Patrick D. Miles; Dacia M. Meneguzzo; Charles J. Barnett

2011-01-01

The first full annual inventory of Nebraska's forests was completed in 2005 after 8,335 plots were selected and 274 forested plots were visited and measured. This report includes detailed information on forest inventory methods, and data quality estimates. Tables of various important resource statistics are presented. Detailed analysis of the inventory data are...

Statistical description of tectonic motions

NASA Technical Reports Server (NTRS)

Agnew, Duncan Carr

1993-01-01

This report summarizes investigations regarding tectonic motions. The topics discussed include statistics of crustal deformation, Earth rotation studies, using multitaper spectrum analysis techniques applied to both space-geodetic data and conventional astrometric estimates of the Earth's polar motion, and the development, design, and installation of high-stability geodetic monuments for use with the global positioning system.

Critical discussion of evaluation parameters for inter-observer variability in target definition for radiation therapy.

PubMed

Fotina, I; Lütgendorf-Caucig, C; Stock, M; Pötter, R; Georg, D

2012-02-01

Inter-observer studies represent a valid method for the evaluation of target definition uncertainties and contouring guidelines. However, data from the literature do not yet give clear guidelines for reporting contouring variability. Thus, the purpose of this work was to compare and discuss various methods to determine variability on the basis of clinical cases and a literature review. In this study, 7 prostate and 8 lung cases were contoured on CT images by 8 experienced observers. Analysis of variability included descriptive statistics, calculation of overlap measures, and statistical measures of agreement. Cross tables with ratios and correlations were established for overlap parameters. It was shown that the minimal set of parameters to be reported should include at least one of three volume overlap measures (i.e., generalized conformity index, Jaccard coefficient, or conformation number). High correlation between these parameters and scatter of the results was observed. A combination of descriptive statistics, overlap measure, and statistical measure of agreement or reliability analysis is required to fully report the interrater variability in delineation.

The analysis of morphometric data on rocky mountain wolves and artic wolves using statistical method

NASA Astrophysics Data System (ADS)

Ammar Shafi, Muhammad; Saifullah Rusiman, Mohd; Hamzah, Nor Shamsidah Amir; Nor, Maria Elena; Ahmad, Noor’ani; Azia Hazida Mohamad Azmi, Nur; Latip, Muhammad Faez Ab; Hilmi Azman, Ahmad

2018-04-01

Morphometrics is a quantitative analysis depending on the shape and size of several specimens. Morphometric quantitative analyses are commonly used to analyse fossil record, shape and size of specimens and others. The aim of the study is to find the differences between rocky mountain wolves and arctic wolves based on gender. The sample utilised secondary data which included seven variables as independent variables and two dependent variables. Statistical modelling was used in the analysis such was the analysis of variance (ANOVA) and multivariate analysis of variance (MANOVA). The results showed there exist differentiating results between arctic wolves and rocky mountain wolves based on independent factors and gender.

Mathematics and statistics research progress report, period ending June 30, 1983

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

Beauchamp, J. J.; Denson, M. V.; Heath, M. T.

1983-08-01

This report is the twenty-sixth in the series of progress reports of Mathematics and Statistics Research of the Computer Sciences organization, Union Carbide Corporation Nuclear Division. Part A records research progress in analysis of large data sets, applied analysis, biometrics research, computational statistics, materials science applications, numerical linear algebra, and risk analysis. Collaboration and consulting with others throughout the Oak Ridge Department of Energy complex are recorded in Part B. Included are sections on biological sciences, energy, engineering, environmental sciences, health and safety, and safeguards. Part C summarizes the various educational activities in which the staff was engaged. Part Dmore » lists the presentations of research results, and Part E records the staff's other professional activities during the report period.« less

Mathematical Ties That Bind.

ERIC Educational Resources Information Center

House, Peggy A.

1994-01-01

Describes some mathematical investigations of the necktie which includes applications of geometry, statistics, data analysis, sampling, probability, symmetry, proportion, problem solving, and business. (MKR)

Intrabolus pressure on high-resolution manometry distinguishes fibrostenotic and inflammatory phenotypes of eosinophilic esophagitis.

PubMed

Colizzo, J M; Clayton, S B; Richter, J E

2016-08-01

The aim of this investigation was to determine the motility patterns of inflammatory and fibrostenotic phenotypes of eosinophilic esophagitis (EoE) utilizing high-resolution manometry (HRM). Twenty-nine patients with a confirmed diagnosis of EoE according to clinicopathological criteria currently being managed at the Joy McCann Culverhouse Swallowing Center at the University of South Florida were included in the retrospective analysis. Only patients who completed HRM studies were included in the analysis. Patients were classified into inflammatory or fibrostenotic subtypes based on baseline endoscopic evidence. Their baseline HRM studies prior to therapy were analyzed. Manometric data including distal contractile integral, integrated relaxation pressure, and intrabolus pressure (IBP) values were recorded. HRM results were interpreted according to the Chicago Classification system. Statistical analysis was performed with SPSS software (Version 22, IBM Co., Armonk, NY, USA). Data were compared utilizing Student's t-test, χ(2) test, Pearson correlation, and Spearman correlation tests. Statistical significance was set at P < 0.05. A total of 29 patients with EoE were included into the retrospective analysis. The overall average age among patients was 40 years. Male patients comprised 62% of the overall population. Both groups were similar in age, gender, and overall clinical presentation. Seventeen patients (58%) had fibrostenotic disease, and 12 (42%) displayed inflammatory disease. The average IBP for the fibrostenotic and inflammatory groups were 18.6 ± 6.0 mmHg and 12.6 ± 3.5 mmHg, respectively (P < 0.05). Strictures were only seen in the fibrostenotic group. Of the fibrostenotic group, 6 (35%) demonstrated proximal esophageal strictures, 7 (41%) had distal strictures, 3 (18%) had mid-esophageal strictures, and 1 (6%) patient had pan-esophageal strictures. There was no statistically significant correlation between the level of esophageal stricture and degree of IBP. Integrated relaxation pressure, distal contractile integral, and other HRM metrics did not demonstrate statistical significance between the two subtypes. There also appeared no statistically significant correlation between patient demographics and esophageal metrics. Patients with the fibrostenotic phenotype of EoE demonstrated an IBP that was significantly higher than that of the inflammatory group. © 2015 International Society for Diseases of the Esophagus.

Statistical summaries of selected Iowa streamflow data through September 2013

USGS Publications Warehouse

Eash, David A.; O'Shea, Padraic S.; Weber, Jared R.; Nguyen, Kevin T.; Montgomery, Nicholas L.; Simonson, Adrian J.

2016-01-04

Statistical summaries of streamflow data collected at 184 streamgages in Iowa are presented in this report. All streamgages included for analysis have at least 10 years of continuous record collected before or through September 2013. This report is an update to two previously published reports that presented statistical summaries of selected Iowa streamflow data through September 1988 and September 1996. The statistical summaries include (1) monthly and annual flow durations, (2) annual exceedance probabilities of instantaneous peak discharges (flood frequencies), (3) annual exceedance probabilities of high discharges, and (4) annual nonexceedance probabilities of low discharges and seasonal low discharges. Also presented for each streamgage are graphs of the annual mean discharges, mean annual mean discharges, 50-percent annual flow-duration discharges (median flows), harmonic mean flows, mean daily mean discharges, and flow-duration curves. Two sets of statistical summaries are presented for each streamgage, which include (1) long-term statistics for the entire period of streamflow record and (2) recent-term statistics for or during the 30-year period of record from 1984 to 2013. The recent-term statistics are only calculated for streamgages with streamflow records pre-dating the 1984 water year and with at least 10 years of record during 1984–2013. The streamflow statistics in this report are not adjusted for the effects of water use; although some of this water is used consumptively, most of it is returned to the streams.

A New Methodology for Systematic Exploitation of Technology Databases.

ERIC Educational Resources Information Center

Bedecarrax, Chantal; Huot, Charles

1994-01-01

Presents the theoretical aspects of a data analysis methodology that can help transform sequential raw data from a database into useful information, using the statistical analysis of patents as an example. Topics discussed include relational analysis and a technology watch approach. (Contains 17 references.) (LRW)

The Development of Career Naval Officers from the U.S. Naval Academy: A Statistical Analysis of the Effects of Selectivity and Human Capital

DTIC Science & Technology

1997-06-01

career success for academy graduates relative to officers commissioned from other sources. Favoritism occurs if high-ranking officers who are service... career success as a naval officer? 6 The thesis investigates several databases in an effort to paint a complete statistical picture of naval officer...including both public and private sector career success was conducted by the Standard & Poor’s Corporation with a related analysis by Professor Michael Useem

Statistical analysis of fNIRS data: a comprehensive review.

PubMed

Tak, Sungho; Ye, Jong Chul

2014-01-15

Functional near-infrared spectroscopy (fNIRS) is a non-invasive method to measure brain activities using the changes of optical absorption in the brain through the intact skull. fNIRS has many advantages over other neuroimaging modalities such as positron emission tomography (PET), functional magnetic resonance imaging (fMRI), or magnetoencephalography (MEG), since it can directly measure blood oxygenation level changes related to neural activation with high temporal resolution. However, fNIRS signals are highly corrupted by measurement noises and physiology-based systemic interference. Careful statistical analyses are therefore required to extract neuronal activity-related signals from fNIRS data. In this paper, we provide an extensive review of historical developments of statistical analyses of fNIRS signal, which include motion artifact correction, short source-detector separation correction, principal component analysis (PCA)/independent component analysis (ICA), false discovery rate (FDR), serially-correlated errors, as well as inference techniques such as the standard t-test, F-test, analysis of variance (ANOVA), and statistical parameter mapping (SPM) framework. In addition, to provide a unified view of various existing inference techniques, we explain a linear mixed effect model with restricted maximum likelihood (ReML) variance estimation, and show that most of the existing inference methods for fNIRS analysis can be derived as special cases. Some of the open issues in statistical analysis are also described. Copyright © 2013 Elsevier Inc. All rights reserved.

Analysis of Doppler radar windshear data

NASA Technical Reports Server (NTRS)

Williams, F.; Mckinney, P.; Ozmen, F.

1989-01-01

The objective of this analysis is to process Lincoln Laboratory Doppler radar data obtained during FLOWS testing at Huntsville, Alabama, in the summer of 1986, to characterize windshear events. The processing includes plotting velocity and F-factor profiles, histogram analysis to summarize statistics, and correlation analysis to demonstrate any correlation between different data fields.

Test 6, Test 7, and Gas Standard Analysis Results

NASA Technical Reports Server (NTRS)

Perez, Horacio, III

2007-01-01

This viewgraph presentation shows results of analyses on odor, toxic off gassing and gas standards. The topics include: 1) Statistical Analysis Definitions; 2) Odor Analysis Results NASA Standard 6001 Test 6; 3) Toxic Off gassing Analysis Results NASA Standard 6001 Test 7; and 4) Gas Standard Results NASA Standard 6001 Test 7;

Outcomes Definitions and Statistical Tests in Oncology Studies: A Systematic Review of the Reporting Consistency.

PubMed

Rivoirard, Romain; Duplay, Vianney; Oriol, Mathieu; Tinquaut, Fabien; Chauvin, Franck; Magne, Nicolas; Bourmaud, Aurelie

2016-01-01

Quality of reporting for Randomized Clinical Trials (RCTs) in oncology was analyzed in several systematic reviews, but, in this setting, there is paucity of data for the outcomes definitions and consistency of reporting for statistical tests in RCTs and Observational Studies (OBS). The objective of this review was to describe those two reporting aspects, for OBS and RCTs in oncology. From a list of 19 medical journals, three were retained for analysis, after a random selection: British Medical Journal (BMJ), Annals of Oncology (AoO) and British Journal of Cancer (BJC). All original articles published between March 2009 and March 2014 were screened. Only studies whose main outcome was accompanied by a corresponding statistical test were included in the analysis. Studies based on censored data were excluded. Primary outcome was to assess quality of reporting for description of primary outcome measure in RCTs and of variables of interest in OBS. A logistic regression was performed to identify covariates of studies potentially associated with concordance of tests between Methods and Results parts. 826 studies were included in the review, and 698 were OBS. Variables were described in Methods section for all OBS studies and primary endpoint was clearly detailed in Methods section for 109 RCTs (85.2%). 295 OBS (42.2%) and 43 RCTs (33.6%) had perfect agreement for reported statistical test between Methods and Results parts. In multivariable analysis, variable "number of included patients in study" was associated with test consistency: aOR (adjusted Odds Ratio) for third group compared to first group was equal to: aOR Grp3 = 0.52 [0.31-0.89] (P value = 0.009). Variables in OBS and primary endpoint in RCTs are reported and described with a high frequency. However, statistical tests consistency between methods and Results sections of OBS is not always noted. Therefore, we encourage authors and peer reviewers to verify consistency of statistical tests in oncology studies.

Outcomes Definitions and Statistical Tests in Oncology Studies: A Systematic Review of the Reporting Consistency

PubMed Central

Rivoirard, Romain; Duplay, Vianney; Oriol, Mathieu; Tinquaut, Fabien; Chauvin, Franck; Magne, Nicolas; Bourmaud, Aurelie

2016-01-01

Background Quality of reporting for Randomized Clinical Trials (RCTs) in oncology was analyzed in several systematic reviews, but, in this setting, there is paucity of data for the outcomes definitions and consistency of reporting for statistical tests in RCTs and Observational Studies (OBS). The objective of this review was to describe those two reporting aspects, for OBS and RCTs in oncology. Methods From a list of 19 medical journals, three were retained for analysis, after a random selection: British Medical Journal (BMJ), Annals of Oncology (AoO) and British Journal of Cancer (BJC). All original articles published between March 2009 and March 2014 were screened. Only studies whose main outcome was accompanied by a corresponding statistical test were included in the analysis. Studies based on censored data were excluded. Primary outcome was to assess quality of reporting for description of primary outcome measure in RCTs and of variables of interest in OBS. A logistic regression was performed to identify covariates of studies potentially associated with concordance of tests between Methods and Results parts. Results 826 studies were included in the review, and 698 were OBS. Variables were described in Methods section for all OBS studies and primary endpoint was clearly detailed in Methods section for 109 RCTs (85.2%). 295 OBS (42.2%) and 43 RCTs (33.6%) had perfect agreement for reported statistical test between Methods and Results parts. In multivariable analysis, variable "number of included patients in study" was associated with test consistency: aOR (adjusted Odds Ratio) for third group compared to first group was equal to: aOR Grp3 = 0.52 [0.31–0.89] (P value = 0.009). Conclusion Variables in OBS and primary endpoint in RCTs are reported and described with a high frequency. However, statistical tests consistency between methods and Results sections of OBS is not always noted. Therefore, we encourage authors and peer reviewers to verify consistency of statistical tests in oncology studies. PMID:27716793

SOCR Analyses – an Instructional Java Web-based Statistical Analysis Toolkit

PubMed Central

Chu, Annie; Cui, Jenny; Dinov, Ivo D.

2011-01-01

The Statistical Online Computational Resource (SOCR) designs web-based tools for educational use in a variety of undergraduate courses (Dinov 2006). Several studies have demonstrated that these resources significantly improve students' motivation and learning experiences (Dinov et al. 2008). SOCR Analyses is a new component that concentrates on data modeling and analysis using parametric and non-parametric techniques supported with graphical model diagnostics. Currently implemented analyses include commonly used models in undergraduate statistics courses like linear models (Simple Linear Regression, Multiple Linear Regression, One-Way and Two-Way ANOVA). In addition, we implemented tests for sample comparisons, such as t-test in the parametric category; and Wilcoxon rank sum test, Kruskal-Wallis test, Friedman's test, in the non-parametric category. SOCR Analyses also include several hypothesis test models, such as Contingency tables, Friedman's test and Fisher's exact test. The code itself is open source (http://socr.googlecode.com/), hoping to contribute to the efforts of the statistical computing community. The code includes functionality for each specific analysis model and it has general utilities that can be applied in various statistical computing tasks. For example, concrete methods with API (Application Programming Interface) have been implemented in statistical summary, least square solutions of general linear models, rank calculations, etc. HTML interfaces, tutorials, source code, activities, and data are freely available via the web (www.SOCR.ucla.edu). Code examples for developers and demos for educators are provided on the SOCR Wiki website. In this article, the pedagogical utilization of the SOCR Analyses is discussed, as well as the underlying design framework. As the SOCR project is on-going and more functions and tools are being added to it, these resources are constantly improved. The reader is strongly encouraged to check the SOCR site for most updated information and newly added models. PMID:21546994

Statistical analysis and application of quasi experiments to antimicrobial resistance intervention studies.

PubMed

Shardell, Michelle; Harris, Anthony D; El-Kamary, Samer S; Furuno, Jon P; Miller, Ram R; Perencevich, Eli N

2007-10-01

Quasi-experimental study designs are frequently used to assess interventions that aim to limit the emergence of antimicrobial-resistant pathogens. However, previous studies using these designs have often used suboptimal statistical methods, which may result in researchers making spurious conclusions. Methods used to analyze quasi-experimental data include 2-group tests, regression analysis, and time-series analysis, and they all have specific assumptions, data requirements, strengths, and limitations. An example of a hospital-based intervention to reduce methicillin-resistant Staphylococcus aureus infection rates and reduce overall length of stay is used to explore these methods.

Research Update: Spatially resolved mapping of electronic structure on atomic level by multivariate statistical analysis

NASA Astrophysics Data System (ADS)

Belianinov, Alex; Ganesh, Panchapakesan; Lin, Wenzhi; Sales, Brian C.; Sefat, Athena S.; Jesse, Stephen; Pan, Minghu; Kalinin, Sergei V.

2014-12-01

Atomic level spatial variability of electronic structure in Fe-based superconductor FeTe0.55Se0.45 (Tc = 15 K) is explored using current-imaging tunneling-spectroscopy. Multivariate statistical analysis of the data differentiates regions of dissimilar electronic behavior that can be identified with the segregation of chalcogen atoms, as well as boundaries between terminations and near neighbor interactions. Subsequent clustering analysis allows identification of the spatial localization of these dissimilar regions. Similar statistical analysis of modeled calculated density of states of chemically inhomogeneous FeTe1-xSex structures further confirms that the two types of chalcogens, i.e., Te and Se, can be identified by their electronic signature and differentiated by their local chemical environment. This approach allows detailed chemical discrimination of the scanning tunneling microscopy data including separation of atomic identities, proximity, and local configuration effects and can be universally applicable to chemically and electronically inhomogeneous surfaces.

The potential of statistical shape modelling for geometric morphometric analysis of human teeth in archaeological research

PubMed Central

Fernee, Christianne; Browne, Martin; Zakrzewski, Sonia

2017-01-01

This paper introduces statistical shape modelling (SSM) for use in osteoarchaeology research. SSM is a full field, multi-material analytical technique, and is presented as a supplementary geometric morphometric (GM) tool. Lower mandibular canines from two archaeological populations and one modern population were sampled, digitised using micro-CT, aligned, registered to a baseline and statistically modelled using principal component analysis (PCA). Sample material properties were incorporated as a binary enamel/dentin parameter. Results were assessed qualitatively and quantitatively using anatomical landmarks. Finally, the technique’s application was demonstrated for inter-sample comparison through analysis of the principal component (PC) weights. It was found that SSM could provide high detail qualitative and quantitative insight with respect to archaeological inter- and intra-sample variability. This technique has value for archaeological, biomechanical and forensic applications including identification, finite element analysis (FEA) and reconstruction from partial datasets. PMID:29216199

Multivariate analysis: A statistical approach for computations

NASA Astrophysics Data System (ADS)

Michu, Sachin; Kaushik, Vandana

2014-10-01

Multivariate analysis is a type of multivariate statistical approach commonly used in, automotive diagnosis, education evaluating clusters in finance etc and more recently in the health-related professions. The objective of the paper is to provide a detailed exploratory discussion about factor analysis (FA) in image retrieval method and correlation analysis (CA) of network traffic. Image retrieval methods aim to retrieve relevant images from a collected database, based on their content. The problem is made more difficult due to the high dimension of the variable space in which the images are represented. Multivariate correlation analysis proposes an anomaly detection and analysis method based on the correlation coefficient matrix. Anomaly behaviors in the network include the various attacks on the network like DDOs attacks and network scanning.

Measuring the statistical validity of summary meta‐analysis and meta‐regression results for use in clinical practice

PubMed Central

Riley, Richard D.

2017-01-01

An important question for clinicians appraising a meta‐analysis is: are the findings likely to be valid in their own practice—does the reported effect accurately represent the effect that would occur in their own clinical population? To this end we advance the concept of statistical validity—where the parameter being estimated equals the corresponding parameter for a new independent study. Using a simple (‘leave‐one‐out’) cross‐validation technique, we demonstrate how we may test meta‐analysis estimates for statistical validity using a new validation statistic, Vn, and derive its distribution. We compare this with the usual approach of investigating heterogeneity in meta‐analyses and demonstrate the link between statistical validity and homogeneity. Using a simulation study, the properties of Vn and the Q statistic are compared for univariate random effects meta‐analysis and a tailored meta‐regression model, where information from the setting (included as model covariates) is used to calibrate the summary estimate to the setting of application. Their properties are found to be similar when there are 50 studies or more, but for fewer studies Vn has greater power but a higher type 1 error rate than Q. The power and type 1 error rate of Vn are also shown to depend on the within‐study variance, between‐study variance, study sample size, and the number of studies in the meta‐analysis. Finally, we apply Vn to two published meta‐analyses and conclude that it usefully augments standard methods when deciding upon the likely validity of summary meta‐analysis estimates in clinical practice. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. PMID:28620945

Monitoring and Evaluation: Statistical Support for Life-cycle Studies, Annual Report 2003.

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

Skalski, John

2003-11-01

The ongoing mission of this project is the development of statistical tools for analyzing fisheries tagging data in the most precise and appropriate manner possible. This mission also includes providing statistical guidance on the best ways to design large-scale tagging studies. This mission continues because the technologies for conducting fish tagging studies continuously evolve. In just the last decade, fisheries biologists have seen the evolution from freeze-brands and coded wire tags (CWT) to passive integrated transponder (PIT) tags, balloon-tags, radiotelemetry, and now, acoustic-tags. With each advance, the technology holds the promise of more detailed and precise information. However, the technologymore » for analyzing and interpreting the data also becomes more complex as the tagging techniques become more sophisticated. The goal of the project is to develop the analytical tools in parallel with the technical advances in tagging studies, so that maximum information can be extracted on a timely basis. Associated with this mission is the transfer of these analytical capabilities to the field investigators to assure consistency and the highest levels of design and analysis throughout the fisheries community. Consequently, this project provides detailed technical assistance on the design and analysis of tagging studies to groups requesting assistance throughout the fisheries community. Ideally, each project and each investigator would invest in the statistical support needed for the successful completion of their study. However, this is an ideal that is rarely if every attained. Furthermore, there is only a small pool of highly trained scientists in this specialized area of tag analysis here in the Northwest. Project 198910700 provides the financial support to sustain this local expertise on the statistical theory of tag analysis at the University of Washington and make it available to the fisheries community. Piecemeal and fragmented support from various agencies and organizations would be incapable of maintaining a center of expertise. The mission of the project is to help assure tagging studies are designed and analyzed from the onset to extract the best available information using state-of-the-art statistical methods. The overarching goals of the project is to assure statistically sound survival studies so that fish managers can focus on the management implications of their findings and not be distracted by concerns whether the studies are statistically reliable or not. Specific goals and objectives of the study include the following: (1) Provide consistent application of statistical methodologies for survival estimation across all salmon life cycle stages to assure comparable performance measures and assessment of results through time, to maximize learning and adaptive management opportunities, and to improve and maintain the ability to responsibly evaluate the success of implemented Columbia River FWP salmonid mitigation programs and identify future mitigation options. (2) Improve analytical capabilities to conduct research on survival processes of wild and hatchery chinook and steelhead during smolt outmigration, to improve monitoring and evaluation capabilities and assist in-season river management to optimize operational and fish passage strategies to maximize survival. (3) Extend statistical support to estimate ocean survival and in-river survival of returning adults. Provide statistical guidance in implementing a river-wide adult PIT-tag detection capability. (4) Develop statistical methods for survival estimation for all potential users and make this information available through peer-reviewed publications, statistical software, and technology transfers to organizations such as NOAA Fisheries, the Fish Passage Center, US Fish and Wildlife Service, US Geological Survey (USGS), US Army Corps of Engineers (USACE), Public Utility Districts (PUDs), the Independent Scientific Advisory Board (ISAB), and other members of the Northwest fisheries community. (5) Provide and maintain statistical software for tag analysis and user support. (6) Provide improvements in statistical theory and software as requested by user groups. These improvements include extending software capabilities to address new research issues, adapting tagging techniques to new study designs, and extending the analysis capabilities to new technologies such as radio-tags and acoustic-tags.« less

Graphical augmentations to the funnel plot assess the impact of additional evidence on a meta-analysis.

PubMed

Langan, Dean; Higgins, Julian P T; Gregory, Walter; Sutton, Alexander J

2012-05-01

We aim to illustrate the potential impact of a new study on a meta-analysis, which gives an indication of the robustness of the meta-analysis. A number of augmentations are proposed to one of the most widely used of graphical displays, the funnel plot. Namely, 1) statistical significance contours, which define regions of the funnel plot in which a new study would have to be located to change the statistical significance of the meta-analysis; and 2) heterogeneity contours, which show how a new study would affect the extent of heterogeneity in a given meta-analysis. Several other features are also described, and the use of multiple features simultaneously is considered. The statistical significance contours suggest that one additional study, no matter how large, may have a very limited impact on the statistical significance of a meta-analysis. The heterogeneity contours illustrate that one outlying study can increase the level of heterogeneity dramatically. The additional features of the funnel plot have applications including 1) informing sample size calculations for the design of future studies eligible for inclusion in the meta-analysis; and 2) informing the updating prioritization of a portfolio of meta-analyses such as those prepared by the Cochrane Collaboration. Copyright © 2012 Elsevier Inc. All rights reserved.

Utilization of an Enhanced Canonical Correlation Analysis (ECCA) to Predict Daily Precipitation and Temperature in a Semi-Arid Environment

NASA Astrophysics Data System (ADS)

Lopez, S. R.; Hogue, T. S.

2011-12-01

Global climate models (GCMs) are primarily used to generate historical and future large-scale circulation patterns at a coarse resolution (typical order of 50,000 km2) and fail to capture climate variability at the ground level due to localized surface influences (i.e topography, marine, layer, land cover, etc). Their inability to accurately resolve these processes has led to the development of numerous 'downscaling' techniques. The goal of this study is to enhance statistical downscaling of daily precipitation and temperature for regions with heterogeneous land cover and topography. Our analysis was divided into two periods, historical (1961-2000) and contemporary (1980-2000), and tested using sixteen predictand combinations from four GCMs (GFDL CM2.0, GFDL CM2.1, CNRM-CM3 and MRI-CGCM2 3.2a. The Southern California area was separated into five county regions: Santa Barbara, Ventura, Los Angeles, Orange and San Diego. Principle component analysis (PCA) was performed on ground-based observations in order to (1) reduce the number of redundant gauges and minimize dimensionality and (2) cluster gauges that behave statistically similarly for post-analysis. Post-PCA analysis included extensive testing of predictor-predictand relationships using an enhanced canonical correlation analysis (ECCA). The ECCA includes obtaining the optimal predictand sets for all models within each spatial domain (county) as governed by daily and monthly overall statistics. Results show all models maintain mean annual and monthly behavior within each county and daily statistics are improved. The level of improvement highly depends on the vegetation extent within each county and the land-to-ocean ratio within the GCM spatial grid. The utilization of the entire historical period also leads to better statistical representation of observed daily precipitation. The validated ECCA technique is being applied to future climate scenarios distributed by the IPCC in order to provide forcing data for regional hydrologic models and assess future water resources in the Southern California region.

Statistical methods to estimate treatment effects from multichannel electroencephalography (EEG) data in clinical trials.

PubMed

Ma, Junshui; Wang, Shubing; Raubertas, Richard; Svetnik, Vladimir

2010-07-15

With the increasing popularity of using electroencephalography (EEG) to reveal the treatment effect in drug development clinical trials, the vast volume and complex nature of EEG data compose an intriguing, but challenging, topic. In this paper the statistical analysis methods recommended by the EEG community, along with methods frequently used in the published literature, are first reviewed. A straightforward adjustment of the existing methods to handle multichannel EEG data is then introduced. In addition, based on the spatial smoothness property of EEG data, a new category of statistical methods is proposed. The new methods use a linear combination of low-degree spherical harmonic (SPHARM) basis functions to represent a spatially smoothed version of the EEG data on the scalp, which is close to a sphere in shape. In total, seven statistical methods, including both the existing and the newly proposed methods, are applied to two clinical datasets to compare their power to detect a drug effect. Contrary to the EEG community's recommendation, our results suggest that (1) the nonparametric method does not outperform its parametric counterpart; and (2) including baseline data in the analysis does not always improve the statistical power. In addition, our results recommend that (3) simple paired statistical tests should be avoided due to their poor power; and (4) the proposed spatially smoothed methods perform better than their unsmoothed versions. Copyright 2010 Elsevier B.V. All rights reserved.

RepExplore: addressing technical replicate variance in proteomics and metabolomics data analysis.

PubMed

Glaab, Enrico; Schneider, Reinhard

2015-07-01

High-throughput omics datasets often contain technical replicates included to account for technical sources of noise in the measurement process. Although summarizing these replicate measurements by using robust averages may help to reduce the influence of noise on downstream data analysis, the information on the variance across the replicate measurements is lost in the averaging process and therefore typically disregarded in subsequent statistical analyses.We introduce RepExplore, a web-service dedicated to exploit the information captured in the technical replicate variance to provide more reliable and informative differential expression and abundance statistics for omics datasets. The software builds on previously published statistical methods, which have been applied successfully to biomedical omics data but are difficult to use without prior experience in programming or scripting. RepExplore facilitates the analysis by providing a fully automated data processing and interactive ranking tables, whisker plot, heat map and principal component analysis visualizations to interpret omics data and derived statistics. Freely available at http://www.repexplore.tk enrico.glaab@uni.lu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.

Uncertainty Analysis of Inertial Model Attitude Sensor Calibration and Application with a Recommended New Calibration Method

NASA Technical Reports Server (NTRS)

Tripp, John S.; Tcheng, Ping

1999-01-01

Statistical tools, previously developed for nonlinear least-squares estimation of multivariate sensor calibration parameters and the associated calibration uncertainty analysis, have been applied to single- and multiple-axis inertial model attitude sensors used in wind tunnel testing to measure angle of attack and roll angle. The analysis provides confidence and prediction intervals of calibrated sensor measurement uncertainty as functions of applied input pitch and roll angles. A comparative performance study of various experimental designs for inertial sensor calibration is presented along with corroborating experimental data. The importance of replicated calibrations over extended time periods has been emphasized; replication provides independent estimates of calibration precision and bias uncertainties, statistical tests for calibration or modeling bias uncertainty, and statistical tests for sensor parameter drift over time. A set of recommendations for a new standardized model attitude sensor calibration method and usage procedures is included. The statistical information provided by these procedures is necessary for the uncertainty analysis of aerospace test results now required by users of industrial wind tunnel test facilities.

Statistical considerations in the development of injury risk functions.

PubMed

McMurry, Timothy L; Poplin, Gerald S

2015-01-01

We address 4 frequently misunderstood and important statistical ideas in the construction of injury risk functions. These include the similarities of survival analysis and logistic regression, the correct scale on which to construct pointwise confidence intervals for injury risk, the ability to discern which form of injury risk function is optimal, and the handling of repeated tests on the same subject. The statistical models are explored through simulation and examination of the underlying mathematics. We provide recommendations for the statistically valid construction and correct interpretation of single-predictor injury risk functions. This article aims to provide useful and understandable statistical guidance to improve the practice in constructing injury risk functions.

Psychometric Analysis of Role Conflict and Ambiguity Scales in Academia

ERIC Educational Resources Information Center

Khan, Anwar; Yusoff, Rosman Bin Md.; Khan, Muhammad Muddassar; Yasir, Muhammad; Khan, Faisal

2014-01-01

A comprehensive Psychometric Analysis of Rizzo et al.'s (1970) Role Conflict & Ambiguity (RCA) scales were performed after its distribution among 600 academic staff working in six universities of Pakistan. The reliability analysis includes calculation of Cronbach Alpha Coefficients and Inter-Items statistics, whereas validity was determined by…

Meta-Analysis in Stata Using Gllamm

ERIC Educational Resources Information Center

Bagos, Pantelis G.

2015-01-01

There are several user-written programs for performing meta-analysis in Stata (Stata Statistical Software: College Station, TX: Stata Corp LP). These include metan, metareg, mvmeta, and glst. However, there are several cases for which these programs do not suffice. For instance, there is no software for performing univariate meta-analysis with…

Genome-wide scans of genetic variants for psychophysiological endophenotypes: a methodological overview.

PubMed

Iacono, William G; Malone, Stephen M; Vaidyanathan, Uma; Vrieze, Scott I

2014-12-01

This article provides an introductory overview of the investigative strategy employed to evaluate the genetic basis of 17 endophenotypes examined as part of a 20-year data collection effort from the Minnesota Center for Twin and Family Research. Included are characterization of the study samples, descriptive statistics for key properties of the psychophysiological measures, and rationale behind the steps taken in the molecular genetic study design. The statistical approach included (a) biometric analysis of twin and family data, (b) heritability analysis using 527,829 single nucleotide polymorphisms (SNPs), (c) genome-wide association analysis of these SNPs and 17,601 autosomal genes, (d) follow-up analyses of candidate SNPs and genes hypothesized to have an association with each endophenotype, (e) rare variant analysis of nonsynonymous SNPs in the exome, and (f) whole genome sequencing association analysis using 27 million genetic variants. These methods were used in the accompanying empirical articles comprising this special issue, Genome-Wide Scans of Genetic Variants for Psychophysiological Endophenotypes. Copyright © 2014 Society for Psychophysiological Research.

Statistical process control methods allow the analysis and improvement of anesthesia care.

PubMed

Fasting, Sigurd; Gisvold, Sven E

2003-10-01

Quality aspects of the anesthetic process are reflected in the rate of intraoperative adverse events. The purpose of this report is to illustrate how the quality of the anesthesia process can be analyzed using statistical process control methods, and exemplify how this analysis can be used for quality improvement. We prospectively recorded anesthesia-related data from all anesthetics for five years. The data included intraoperative adverse events, which were graded into four levels, according to severity. We selected four adverse events, representing important quality and safety aspects, for statistical process control analysis. These were: inadequate regional anesthesia, difficult emergence from general anesthesia, intubation difficulties and drug errors. We analyzed the underlying process using 'p-charts' for statistical process control. In 65,170 anesthetics we recorded adverse events in 18.3%; mostly of lesser severity. Control charts were used to define statistically the predictable normal variation in problem rate, and then used as a basis for analysis of the selected problems with the following results: Inadequate plexus anesthesia: stable process, but unacceptably high failure rate; Difficult emergence: unstable process, because of quality improvement efforts; Intubation difficulties: stable process, rate acceptable; Medication errors: methodology not suited because of low rate of errors. By applying statistical process control methods to the analysis of adverse events, we have exemplified how this allows us to determine if a process is stable, whether an intervention is required, and if quality improvement efforts have the desired effect.

Assessment of statistical methods used in library-based approaches to microbial source tracking.

PubMed

Ritter, Kerry J; Carruthers, Ethan; Carson, C Andrew; Ellender, R D; Harwood, Valerie J; Kingsley, Kyle; Nakatsu, Cindy; Sadowsky, Michael; Shear, Brian; West, Brian; Whitlock, John E; Wiggins, Bruce A; Wilbur, Jayson D

2003-12-01

Several commonly used statistical methods for fingerprint identification in microbial source tracking (MST) were examined to assess the effectiveness of pattern-matching algorithms to correctly identify sources. Although numerous statistical methods have been employed for source identification, no widespread consensus exists as to which is most appropriate. A large-scale comparison of several MST methods, using identical fecal sources, presented a unique opportunity to assess the utility of several popular statistical methods. These included discriminant analysis, nearest neighbour analysis, maximum similarity and average similarity, along with several measures of distance or similarity. Threshold criteria for excluding uncertain or poorly matched isolates from final analysis were also examined for their ability to reduce false positives and increase prediction success. Six independent libraries used in the study were constructed from indicator bacteria isolated from fecal materials of humans, seagulls, cows and dogs. Three of these libraries were constructed using the rep-PCR technique and three relied on antibiotic resistance analysis (ARA). Five of the libraries were constructed using Escherichia coli and one using Enterococcus spp. (ARA). Overall, the outcome of this study suggests a high degree of variability across statistical methods. Despite large differences in correct classification rates among the statistical methods, no single statistical approach emerged as superior. Thresholds failed to consistently increase rates of correct classification and improvement was often associated with substantial effective sample size reduction. Recommendations are provided to aid in selecting appropriate analyses for these types of data.

Model-Based Linkage Analysis of a Quantitative Trait.

PubMed

Song, Yeunjoo E; Song, Sunah; Schnell, Audrey H

2017-01-01

Linkage Analysis is a family-based method of analysis to examine whether any typed genetic markers cosegregate with a given trait, in this case a quantitative trait. If linkage exists, this is taken as evidence in support of a genetic basis for the trait. Historically, linkage analysis was performed using a binary disease trait, but has been extended to include quantitative disease measures. Quantitative traits are desirable as they provide more information than binary traits. Linkage analysis can be performed using single-marker methods (one marker at a time) or multipoint (using multiple markers simultaneously). In model-based linkage analysis the genetic model for the trait of interest is specified. There are many software options for performing linkage analysis. Here, we use the program package Statistical Analysis for Genetic Epidemiology (S.A.G.E.). S.A.G.E. was chosen because it also includes programs to perform data cleaning procedures and to generate and test genetic models for a quantitative trait, in addition to performing linkage analysis. We demonstrate in detail the process of running the program LODLINK to perform single-marker analysis, and MLOD to perform multipoint analysis using output from SEGREG, where SEGREG was used to determine the best fitting statistical model for the trait.

Statistical Analysis of CFD Solutions from the Fourth AIAA Drag Prediction Workshop

NASA Technical Reports Server (NTRS)

Morrison, Joseph H.

2010-01-01

A graphical framework is used for statistical analysis of the results from an extensive N-version test of a collection of Reynolds-averaged Navier-Stokes computational fluid dynamics codes. The solutions were obtained by code developers and users from the U.S., Europe, Asia, and Russia using a variety of grid systems and turbulence models for the June 2009 4th Drag Prediction Workshop sponsored by the AIAA Applied Aerodynamics Technical Committee. The aerodynamic configuration for this workshop was a new subsonic transport model, the Common Research Model, designed using a modern approach for the wing and included a horizontal tail. The fourth workshop focused on the prediction of both absolute and incremental drag levels for wing-body and wing-body-horizontal tail configurations. This work continues the statistical analysis begun in the earlier workshops and compares the results from the grid convergence study of the most recent workshop with earlier workshops using the statistical framework.

Classification and statistical analysis of mine spoils chemical composition from Oliete basin (Teruel, NE Spain)

NASA Astrophysics Data System (ADS)

Meseguer, S.; Sanfeliu, T.; Jordán, M. M.

2009-02-01

The Oliete basin (Early Cretaceous, NE Teruel, Spain) is one of the most important areas for the supply of mine spoils used as ball clays for the production of white and red stoneware in the Spanish ceramic industry of wall and floor tiles. This study corresponds to the second part of the paper published recently by Meseguer et al. (Environ Geol 2008) about the use of mine spoils from Teruel coal mining district. The present study shows a statistical data analysis from chemical data (major, minor and trace elements). The performed statistical analysis of chemical data included descriptive statistics and cluster analysis (with ANOVA and Scheffé methods). The cluster analysis of chemical data provided three main groups: C3 with the highest mean SiO2 content (66%) and lowest mean Al2O3 content (20%); C2 with lower SiO2 content (48%) and higher mean Al2O3 content (28%); and C1 with medium values for the SiO2 and Al2O3 mean content. The main applications of these materials are refractory, white and red ceramics, stoneware, heavy ceramics (including red earthenware, bricks and roof tiles), and components of white Portland cement and aluminous cement. Clays from group 2 are used in refractories (with higher kaolinite content, and constrictions to CaO + MgO and K2O + Na2O contents). All materials can be used in fine ceramics (white or red, according to the Fe2O3 + TiO2 content).

Cognition, comprehension and application of biostatistics in research by Indian postgraduate students in periodontics

PubMed Central

Swetha, Jonnalagadda Laxmi; Arpita, Ramisetti; Srikanth, Chintalapani; Nutalapati, Rajasekhar

2014-01-01

Background: Biostatistics is an integral part of research protocols. In any field of inquiry or investigation, data obtained is subsequently classified, analyzed and tested for accuracy by statistical methods. Statistical analysis of collected data, thus, forms the basis for all evidence-based conclusions. Aim: The aim of this study is to evaluate the cognition, comprehension and application of biostatistics in research among post graduate students in Periodontics, in India. Materials and Methods: A total of 391 post graduate students registered for a master's course in periodontics at various dental colleges across India were included in the survey. Data regarding the level of knowledge, understanding and its application in design and conduct of the research protocol was collected using a dichotomous questionnaire. A descriptive statistics was used for data analysis. Results: Nearly 79.2% students were aware of the importance of biostatistics in research, 55-65% were familiar with MS-EXCEL spreadsheet for graphical representation of data and with the statistical softwares available on the internet, 26.0% had biostatistics as mandatory subject in their curriculum, 9.5% tried to perform statistical analysis on their own while 3.0% were successful in performing statistical analysis of their studies on their own. Conclusion: Biostatistics should play a central role in planning, conduct, interim analysis, final analysis and reporting of periodontal research especially by the postgraduate students. Indian postgraduate students in periodontics are aware of the importance of biostatistics in research but the level of understanding and application is still basic and needs to be addressed. PMID:24744547

Correlation of RNA secondary structure statistics with thermodynamic stability and applications to folding.

PubMed

Wu, Johnny C; Gardner, David P; Ozer, Stuart; Gutell, Robin R; Ren, Pengyu

2009-08-28

The accurate prediction of the secondary and tertiary structure of an RNA with different folding algorithms is dependent on several factors, including the energy functions. However, an RNA higher-order structure cannot be predicted accurately from its sequence based on a limited set of energy parameters. The inter- and intramolecular forces between this RNA and other small molecules and macromolecules, in addition to other factors in the cell such as pH, ionic strength, and temperature, influence the complex dynamics associated with transition of a single stranded RNA to its secondary and tertiary structure. Since all of the factors that affect the formation of an RNAs 3D structure cannot be determined experimentally, statistically derived potential energy has been used in the prediction of protein structure. In the current work, we evaluate the statistical free energy of various secondary structure motifs, including base-pair stacks, hairpin loops, and internal loops, using their statistical frequency obtained from the comparative analysis of more than 50,000 RNA sequences stored in the RNA Comparative Analysis Database (rCAD) at the Comparative RNA Web (CRW) Site. Statistical energy was computed from the structural statistics for several datasets. While the statistical energy for a base-pair stack correlates with experimentally derived free energy values, suggesting a Boltzmann-like distribution, variation is observed between different molecules and their location on the phylogenetic tree of life. Our statistical energy values calculated for several structural elements were utilized in the Mfold RNA-folding algorithm. The combined statistical energy values for base-pair stacks, hairpins and internal loop flanks result in a significant improvement in the accuracy of secondary structure prediction; the hairpin flanks contribute the most.

Post-operative diffusion weighted imaging as a predictor of posterior fossa syndrome permanence in paediatric medulloblastoma.

PubMed

Chua, Felicia H Z; Thien, Ady; Ng, Lee Ping; Seow, Wan Tew; Low, David C Y; Chang, Kenneth T E; Lian, Derrick W Q; Loh, Eva; Low, Sharon Y Y

2017-03-01

Posterior fossa syndrome (PFS) is a serious complication faced by neurosurgeons and their patients, especially in paediatric medulloblastoma patients. The uncertain aetiology of PFS, myriad of cited risk factors and therapeutic challenges make this phenomenon an elusive entity. The primary objective of this study was to identify associative factors related to the development of PFS in medulloblastoma patient post-tumour resection. This is a retrospective study based at a single institution. Patient data and all related information were collected from the hospital records, in accordance to a list of possible risk factors associated with PFS. These included pre-operative tumour volume, hydrocephalus, age, gender, extent of resection, metastasis, ventriculoperitoneal shunt insertion, post-operative meningitis and radiological changes in MRI. Additional variables included molecular and histological subtypes of each patient's medulloblastoma tumour. Statistical analysis was employed to determine evidence of each variable's significance in PFS permanence. A total of 19 patients with appropriately complete data was identified. Initial univariate analysis did not show any statistical significance. However, multivariate analysis for MRI-specific changes reported bilateral DWI restricted diffusion changes involving both right and left sides of the surgical cavity was of statistical significance for PFS permanence. The authors performed a clinical study that evaluated possible risk factors for permanent PFS in paediatric medulloblastoma patients. Analysis of collated results found that post-operative DWI restriction in bilateral regions within the surgical cavity demonstrated statistical significance as a predictor of PFS permanence-a novel finding in the current literature.

Analysis of traffic accident data in Kentucky (1994-1998)

DOT National Transportation Integrated Search

1999-09-01

This report includes an analysis of traffic accident data in Kentucky for the years of 1994 through 1998. A primary objective of this study was to determine average accident statistics for Kentucky highways. Average and critical numbers and rates of ...

Analysis of traffic crash data in Kentucky : 2002-2006.

DOT National Transportation Integrated Search

2007-09-01

This report includes an analysis of traffic accident data in Kentucky for the years of 2002 through 2006. A primary objective of this study was to determine average accident statistics for Kentucky highways. Average and critical numbers and rates of ...

Analysis of traffic crash data in Kentucky : 2003-2007.

DOT National Transportation Integrated Search

2008-08-01

This report includes an analysis of traffic accident data in Kentucky for the years of 2003 through 2007. A primary objective of this study was to determine average accident statistics for Kentucky highways. Average and critical numbers and rates of ...

Surgical Treatment for Discogenic Low-Back Pain: Lumbar Arthroplasty Results in Superior Pain Reduction and Disability Level Improvement Compared With Lumbar Fusion

PubMed Central

2007-01-01

Background The US Food and Drug Administration approved the Charité artificial disc on October 26, 2004. This approval was based on an extensive analysis and review process; 20 years of disc usage worldwide; and the results of a prospective, randomized, controlled clinical trial that compared lumbar artificial disc replacement to fusion. The results of the investigational device exemption (IDE) study led to a conclusion that clinical outcomes following lumbar arthroplasty were at least as good as outcomes from fusion. Methods The author performed a new analysis of the Visual Analog Scale pain scores and the Oswestry Disability Index scores from the Charité artificial disc IDE study and used a nonparametric statistical test, because observed data distributions were not normal. The analysis included all of the enrolled subjects in both the nonrandomized and randomized phases of the study. Results Subjects from both the treatment and control groups improved from the baseline situation (P < .001) at all follow-up times (6 weeks to 24 months). Additionally, these pain and disability levels with artificial disc replacement were superior (P < .05) to the fusion treatment at all follow-up times including 2 years. Conclusions The a priori statistical plan for an IDE study may not adequately address the final distribution of the data. Therefore, statistical analyses more appropriate to the distribution may be necessary to develop meaningful statistical conclusions from the study. A nonparametric statistical analysis of the Charité artificial disc IDE outcomes scores demonstrates superiority for lumbar arthroplasty versus fusion at all follow-up time points to 24 months. PMID:25802574

Multivariate Meta-Analysis of Heterogeneous Studies Using Only Summary Statistics: Efficiency and Robustness

PubMed Central

Liu, Dungang; Liu, Regina; Xie, Minge

2014-01-01

Meta-analysis has been widely used to synthesize evidence from multiple studies for common hypotheses or parameters of interest. However, it has not yet been fully developed for incorporating heterogeneous studies, which arise often in applications due to different study designs, populations or outcomes. For heterogeneous studies, the parameter of interest may not be estimable for certain studies, and in such a case, these studies are typically excluded from conventional meta-analysis. The exclusion of part of the studies can lead to a non-negligible loss of information. This paper introduces a metaanalysis for heterogeneous studies by combining the confidence density functions derived from the summary statistics of individual studies, hence referred to as the CD approach. It includes all the studies in the analysis and makes use of all information, direct as well as indirect. Under a general likelihood inference framework, this new approach is shown to have several desirable properties, including: i) it is asymptotically as efficient as the maximum likelihood approach using individual participant data (IPD) from all studies; ii) unlike the IPD analysis, it suffices to use summary statistics to carry out the CD approach. Individual-level data are not required; and iii) it is robust against misspecification of the working covariance structure of the parameter estimates. Besides its own theoretical significance, the last property also substantially broadens the applicability of the CD approach. All the properties of the CD approach are further confirmed by data simulated from a randomized clinical trials setting as well as by real data on aircraft landing performance. Overall, one obtains an unifying approach for combining summary statistics, subsuming many of the existing meta-analysis methods as special cases. PMID:26190875

Precipitate statistics in an Al-Mg-Si-Cu alloy from scanning precession electron diffraction data

NASA Astrophysics Data System (ADS)

Sunde, J. K.; Paulsen, Ø.; Wenner, S.; Holmestad, R.

2017-09-01

The key microstructural feature providing strength to age-hardenable Al alloys is nanoscale precipitates. Alloy development requires a reliable statistical assessment of these precipitates, in order to link the microstructure with material properties. Here, it is demonstrated that scanning precession electron diffraction combined with computational analysis enable the semi-automated extraction of precipitate statistics in an Al-Mg-Si-Cu alloy. Among the main findings is the precipitate number density, which agrees well with a conventional method based on manual counting and measurements. By virtue of its data analysis objectivity, our methodology is therefore seen as an advantageous alternative to existing routines, offering reproducibility and efficiency in alloy statistics. Additional results include improved qualitative information on phase distributions. The developed procedure is generic and applicable to any material containing nanoscale precipitates.

Biomedical applications of aerospace technology

NASA Technical Reports Server (NTRS)

Castles, T. R.

1971-01-01

Aerospace technology transfer to biomedical research problems is discussed, including transfer innovations and potential applications. Statistical analysis of the transfer activities and impact is also presented.

New software for statistical analysis of Cambridge Structural Database data

PubMed Central

Sykes, Richard A.; McCabe, Patrick; Allen, Frank H.; Battle, Gary M.; Bruno, Ian J.; Wood, Peter A.

2011-01-01

A collection of new software tools is presented for the analysis of geometrical, chemical and crystallographic data from the Cambridge Structural Database (CSD). This software supersedes the program Vista. The new functionality is integrated into the program Mercury in order to provide statistical, charting and plotting options alongside three-dimensional structural visualization and analysis. The integration also permits immediate access to other information about specific CSD entries through the Mercury framework, a common requirement in CSD data analyses. In addition, the new software includes a range of more advanced features focused towards structural analysis such as principal components analysis, cone-angle correction in hydrogen-bond analyses and the ability to deal with topological symmetry that may be exhibited in molecular search fragments. PMID:22477784

Statistics Clinic

NASA Technical Reports Server (NTRS)

Feiveson, Alan H.; Foy, Millennia; Ploutz-Snyder, Robert; Fiedler, James

2014-01-01

Do you have elevated p-values? Is the data analysis process getting you down? Do you experience anxiety when you need to respond to criticism of statistical methods in your manuscript? You may be suffering from Insufficient Statistical Support Syndrome (ISSS). For symptomatic relief of ISSS, come for a free consultation with JSC biostatisticians at our help desk during the poster sessions at the HRP Investigators Workshop. Get answers to common questions about sample size, missing data, multiple testing, when to trust the results of your analyses and more. Side effects may include sudden loss of statistics anxiety, improved interpretation of your data, and increased confidence in your results.

The Deployment Life Study: Longitudinal Analysis of Military Families Across the Deployment Cycle

DTIC Science & Technology

2016-01-01

psychological and physical aggression than they reported prior to the deployment. 1 H. Fischer, A Guide to U.S. Military Casualty Statistics ...analyses include a large number of statistical tests and thus the results pre- sented in this report should be viewed in terms of patterns, rather...Military Children and Families,” The Future of Children, Vol. 23, No. 2, 2013, pp. 13–39. Fischer, H., A Guide to U.S. Military Casualty Statistics

Trend analysis and selected summary statistics of annual mean streamflow for 38 selected long-term U.S. Geological Survey streamgages in Texas, water years 1916-2012

USGS Publications Warehouse

Asquith, William H.; Barbie, Dana L.

2014-01-01

Selected summary statistics (L-moments) and estimates of respective sampling variances were computed for the 35 streamgages lacking statistically significant trends. From the L-moments and estimated sampling variances, weighted means or regional values were computed for each L-moment. An example application is included demonstrating how the L-moments could be used to evaluate the magnitude and frequency of annual mean streamflow.

A review of empirical research related to the use of small quantitative samples in clinical outcome scale development.

PubMed

Houts, Carrie R; Edwards, Michael C; Wirth, R J; Deal, Linda S

2016-11-01

There has been a notable increase in the advocacy of using small-sample designs as an initial quantitative assessment of item and scale performance during the scale development process. This is particularly true in the development of clinical outcome assessments (COAs), where Rasch analysis has been advanced as an appropriate statistical tool for evaluating the developing COAs using a small sample. We review the benefits such methods are purported to offer from both a practical and statistical standpoint and detail several problematic areas, including both practical and statistical theory concerns, with respect to the use of quantitative methods, including Rasch-consistent methods, with small samples. The feasibility of obtaining accurate information and the potential negative impacts of misusing large-sample statistical methods with small samples during COA development are discussed.

Statistical analysis and interpolation of compositional data in materials science.

PubMed

Pesenson, Misha Z; Suram, Santosh K; Gregoire, John M

2015-02-09

Compositional data are ubiquitous in chemistry and materials science: analysis of elements in multicomponent systems, combinatorial problems, etc., lead to data that are non-negative and sum to a constant (for example, atomic concentrations). The constant sum constraint restricts the sampling space to a simplex instead of the usual Euclidean space. Since statistical measures such as mean and standard deviation are defined for the Euclidean space, traditional correlation studies, multivariate analysis, and hypothesis testing may lead to erroneous dependencies and incorrect inferences when applied to compositional data. Furthermore, composition measurements that are used for data analytics may not include all of the elements contained in the material; that is, the measurements may be subcompositions of a higher-dimensional parent composition. Physically meaningful statistical analysis must yield results that are invariant under the number of composition elements, requiring the application of specialized statistical tools. We present specifics and subtleties of compositional data processing through discussion of illustrative examples. We introduce basic concepts, terminology, and methods required for the analysis of compositional data and utilize them for the spatial interpolation of composition in a sputtered thin film. The results demonstrate the importance of this mathematical framework for compositional data analysis (CDA) in the fields of materials science and chemistry.

Using SPSS to Analyze Book Collection Data.

ERIC Educational Resources Information Center

Townley, Charles T.

1981-01-01

Describes and illustrates Statistical Package for the Social Sciences (SPSS) procedures appropriate for book collection data analysis. Several different procedures for univariate, bivariate, and multivariate analysis are discussed, and applications of procedures for book collection studies are presented. Included are 24 tables illustrating output…

Analysis of traffic accident data in Kentucky (1986-1990)

DOT National Transportation Integrated Search

1991-09-01

This report includes an analysis of traffic accident data in Kentucky for the years of 1986-1990. A primary objectve of this study was to determine average statistics for kentucky highways. Average and critical number and rates of accidents were calc...

Analysis of traffic crash data in Kentucky : 2000-2004.

DOT National Transportation Integrated Search

2005-08-01

This report includes an analysis of traffic accident data in Kentucky for the years of 2000 through 2004. A primary objective of this study was to determine average crash statistics for Kentucky highways. Average and critical numbers and rates of cra...

Analysis of traffic crash data in Kentucky : 2001-2005.

DOT National Transportation Integrated Search

2006-08-01

This report includes an analysis of traffic accident data in Kentucky for the years of 2001 through 2005. A primary objective of this study was to determine average crash statistics for Kentucky highways. Average and critical rates of crashes were ca...

Guidelines for Genome-Scale Analysis of Biological Rhythms.

PubMed

Hughes, Michael E; Abruzzi, Katherine C; Allada, Ravi; Anafi, Ron; Arpat, Alaaddin Bulak; Asher, Gad; Baldi, Pierre; de Bekker, Charissa; Bell-Pedersen, Deborah; Blau, Justin; Brown, Steve; Ceriani, M Fernanda; Chen, Zheng; Chiu, Joanna C; Cox, Juergen; Crowell, Alexander M; DeBruyne, Jason P; Dijk, Derk-Jan; DiTacchio, Luciano; Doyle, Francis J; Duffield, Giles E; Dunlap, Jay C; Eckel-Mahan, Kristin; Esser, Karyn A; FitzGerald, Garret A; Forger, Daniel B; Francey, Lauren J; Fu, Ying-Hui; Gachon, Frédéric; Gatfield, David; de Goede, Paul; Golden, Susan S; Green, Carla; Harer, John; Harmer, Stacey; Haspel, Jeff; Hastings, Michael H; Herzel, Hanspeter; Herzog, Erik D; Hoffmann, Christy; Hong, Christian; Hughey, Jacob J; Hurley, Jennifer M; de la Iglesia, Horacio O; Johnson, Carl; Kay, Steve A; Koike, Nobuya; Kornacker, Karl; Kramer, Achim; Lamia, Katja; Leise, Tanya; Lewis, Scott A; Li, Jiajia; Li, Xiaodong; Liu, Andrew C; Loros, Jennifer J; Martino, Tami A; Menet, Jerome S; Merrow, Martha; Millar, Andrew J; Mockler, Todd; Naef, Felix; Nagoshi, Emi; Nitabach, Michael N; Olmedo, Maria; Nusinow, Dmitri A; Ptáček, Louis J; Rand, David; Reddy, Akhilesh B; Robles, Maria S; Roenneberg, Till; Rosbash, Michael; Ruben, Marc D; Rund, Samuel S C; Sancar, Aziz; Sassone-Corsi, Paolo; Sehgal, Amita; Sherrill-Mix, Scott; Skene, Debra J; Storch, Kai-Florian; Takahashi, Joseph S; Ueda, Hiroki R; Wang, Han; Weitz, Charles; Westermark, Pål O; Wijnen, Herman; Xu, Ying; Wu, Gang; Yoo, Seung-Hee; Young, Michael; Zhang, Eric Erquan; Zielinski, Tomasz; Hogenesch, John B

2017-10-01

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding "big data" that are conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome-scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them.

Guidelines for Genome-Scale Analysis of Biological Rhythms

PubMed Central

Hughes, Michael E.; Abruzzi, Katherine C.; Allada, Ravi; Anafi, Ron; Arpat, Alaaddin Bulak; Asher, Gad; Baldi, Pierre; de Bekker, Charissa; Bell-Pedersen, Deborah; Blau, Justin; Brown, Steve; Ceriani, M. Fernanda; Chen, Zheng; Chiu, Joanna C.; Cox, Juergen; Crowell, Alexander M.; DeBruyne, Jason P.; Dijk, Derk-Jan; DiTacchio, Luciano; Doyle, Francis J.; Duffield, Giles E.; Dunlap, Jay C.; Eckel-Mahan, Kristin; Esser, Karyn A.; FitzGerald, Garret A.; Forger, Daniel B.; Francey, Lauren J.; Fu, Ying-Hui; Gachon, Frédéric; Gatfield, David; de Goede, Paul; Golden, Susan S.; Green, Carla; Harer, John; Harmer, Stacey; Haspel, Jeff; Hastings, Michael H.; Herzel, Hanspeter; Herzog, Erik D.; Hoffmann, Christy; Hong, Christian; Hughey, Jacob J.; Hurley, Jennifer M.; de la Iglesia, Horacio O.; Johnson, Carl; Kay, Steve A.; Koike, Nobuya; Kornacker, Karl; Kramer, Achim; Lamia, Katja; Leise, Tanya; Lewis, Scott A.; Li, Jiajia; Li, Xiaodong; Liu, Andrew C.; Loros, Jennifer J.; Martino, Tami A.; Menet, Jerome S.; Merrow, Martha; Millar, Andrew J.; Mockler, Todd; Naef, Felix; Nagoshi, Emi; Nitabach, Michael N.; Olmedo, Maria; Nusinow, Dmitri A.; Ptáček, Louis J.; Rand, David; Reddy, Akhilesh B.; Robles, Maria S.; Roenneberg, Till; Rosbash, Michael; Ruben, Marc D.; Rund, Samuel S.C.; Sancar, Aziz; Sassone-Corsi, Paolo; Sehgal, Amita; Sherrill-Mix, Scott; Skene, Debra J.; Storch, Kai-Florian; Takahashi, Joseph S.; Ueda, Hiroki R.; Wang, Han; Weitz, Charles; Westermark, Pål O.; Wijnen, Herman; Xu, Ying; Wu, Gang; Yoo, Seung-Hee; Young, Michael; Zhang, Eric Erquan; Zielinski, Tomasz; Hogenesch, John B.

2017-01-01

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding “big data” that are conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome-scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them. PMID:29098954

medplot: a web application for dynamic summary and analysis of longitudinal medical data based on R.

PubMed

Ahlin, Črt; Stupica, Daša; Strle, Franc; Lusa, Lara

2015-01-01

In biomedical studies the patients are often evaluated numerous times and a large number of variables are recorded at each time-point. Data entry and manipulation of longitudinal data can be performed using spreadsheet programs, which usually include some data plotting and analysis capabilities and are straightforward to use, but are not designed for the analyses of complex longitudinal data. Specialized statistical software offers more flexibility and capabilities, but first time users with biomedical background often find its use difficult. We developed medplot, an interactive web application that simplifies the exploration and analysis of longitudinal data. The application can be used to summarize, visualize and analyze data by researchers that are not familiar with statistical programs and whose knowledge of statistics is limited. The summary tools produce publication-ready tables and graphs. The analysis tools include features that are seldom available in spreadsheet software, such as correction for multiple testing, repeated measurement analyses and flexible non-linear modeling of the association of the numerical variables with the outcome. medplot is freely available and open source, it has an intuitive graphical user interface (GUI), it is accessible via the Internet and can be used within a web browser, without the need for installing and maintaining programs locally on the user's computer. This paper describes the application and gives detailed examples describing how to use the application on real data from a clinical study including patients with early Lyme borreliosis.

Gene Level Meta-Analysis of Quantitative Traits by Functional Linear Models.

PubMed

Fan, Ruzong; Wang, Yifan; Boehnke, Michael; Chen, Wei; Li, Yun; Ren, Haobo; Lobach, Iryna; Xiong, Momiao

2015-08-01

Meta-analysis of genetic data must account for differences among studies including study designs, markers genotyped, and covariates. The effects of genetic variants may differ from population to population, i.e., heterogeneity. Thus, meta-analysis of combining data of multiple studies is difficult. Novel statistical methods for meta-analysis are needed. In this article, functional linear models are developed for meta-analyses that connect genetic data to quantitative traits, adjusting for covariates. The models can be used to analyze rare variants, common variants, or a combination of the two. Both likelihood-ratio test (LRT) and F-distributed statistics are introduced to test association between quantitative traits and multiple variants in one genetic region. Extensive simulations are performed to evaluate empirical type I error rates and power performance of the proposed tests. The proposed LRT and F-distributed statistics control the type I error very well and have higher power than the existing methods of the meta-analysis sequence kernel association test (MetaSKAT). We analyze four blood lipid levels in data from a meta-analysis of eight European studies. The proposed methods detect more significant associations than MetaSKAT and the P-values of the proposed LRT and F-distributed statistics are usually much smaller than those of MetaSKAT. The functional linear models and related test statistics can be useful in whole-genome and whole-exome association studies. Copyright © 2015 by the Genetics Society of America.

Web 2.0 in the Professional LIS Literature: An Exploratory Analysis

ERIC Educational Resources Information Center

Aharony, Noa

2011-01-01

This paper presents a statistical descriptive analysis and a thorough content analysis of descriptors and journal titles extracted from the Library and Information Science Abstracts (LISA) database, focusing on the subject of Web 2.0 and its main applications: blog, wiki, social network and tags.The primary research questions include: whether the…

Analyzing the Validity of the Adult-Adolescent Parenting Inventory for Low-Income Populations

ERIC Educational Resources Information Center

Lawson, Michael A.; Alameda-Lawson, Tania; Byrnes, Edward

2017-01-01

Objectives: The purpose of this study was to examine the construct and predictive validity of the Adult-Adolescent Parenting Inventory (AAPI-2). Methods: The validity of the AAPI-2 was evaluated using multiple statistical methods, including exploratory factor analysis, confirmatory factor analysis, and latent class analysis. These analyses were…

HYPOTHESIS SETTING AND ORDER STATISTIC FOR ROBUST GENOMIC META-ANALYSIS.

PubMed

Song, Chi; Tseng, George C

2014-01-01

Meta-analysis techniques have been widely developed and applied in genomic applications, especially for combining multiple transcriptomic studies. In this paper, we propose an order statistic of p-values ( r th ordered p-value, rOP) across combined studies as the test statistic. We illustrate different hypothesis settings that detect gene markers differentially expressed (DE) "in all studies", "in the majority of studies", or "in one or more studies", and specify rOP as a suitable method for detecting DE genes "in the majority of studies". We develop methods to estimate the parameter r in rOP for real applications. Statistical properties such as its asymptotic behavior and a one-sided testing correction for detecting markers of concordant expression changes are explored. Power calculation and simulation show better performance of rOP compared to classical Fisher's method, Stouffer's method, minimum p-value method and maximum p-value method under the focused hypothesis setting. Theoretically, rOP is found connected to the naïve vote counting method and can be viewed as a generalized form of vote counting with better statistical properties. The method is applied to three microarray meta-analysis examples including major depressive disorder, brain cancer and diabetes. The results demonstrate rOP as a more generalizable, robust and sensitive statistical framework to detect disease-related markers.

Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science).

PubMed

Zeng, Irene Sui Lan; Lumley, Thomas

2018-01-01

Integrated omics is becoming a new channel for investigating the complex molecular system in modern biological science and sets a foundation for systematic learning for precision medicine. The statistical/machine learning methods that have emerged in the past decade for integrated omics are not only innovative but also multidisciplinary with integrated knowledge in biology, medicine, statistics, machine learning, and artificial intelligence. Here, we review the nontrivial classes of learning methods from the statistical aspects and streamline these learning methods within the statistical learning framework. The intriguing findings from the review are that the methods used are generalizable to other disciplines with complex systematic structure, and the integrated omics is part of an integrated information science which has collated and integrated different types of information for inferences and decision making. We review the statistical learning methods of exploratory and supervised learning from 42 publications. We also discuss the strengths and limitations of the extended principal component analysis, cluster analysis, network analysis, and regression methods. Statistical techniques such as penalization for sparsity induction when there are fewer observations than the number of features and using Bayesian approach when there are prior knowledge to be integrated are also included in the commentary. For the completeness of the review, a table of currently available software and packages from 23 publications for omics are summarized in the appendix.

Ketamine as a novel treatment for major depressive disorder and bipolar depression: a systematic review and quantitative meta-analysis.

PubMed

Lee, Ellen E; Della Selva, Megan P; Liu, Anson; Himelhoch, Seth

2015-01-01

Given the significant disability, morbidity and mortality associated with depression, the promising recent trials of ketamine highlight a novel intervention. A meta-analysis was conducted to assess the efficacy of ketamine in comparison with placebo for the reduction of depressive symptoms in patients who meet criteria for a major depressive episode. Two electronic databases were searched in September 2013 for English-language studies that were randomized placebo-controlled trials of ketamine treatment for patients with major depressive disorder or bipolar depression and utilized a standardized rating scale. Studies including participants receiving electroconvulsive therapy and adolescent/child participants were excluded. Five studies were included in the quantitative meta-analysis. The quantitative meta-analysis showed that ketamine significantly reduced depressive symptoms. The overall effect size at day 1 was large and statistically significant with an overall standardized mean difference of 1.01 (95% confidence interval 0.69-1.34) (P<.001), with the effects sustained at 7 days postinfusion. The heterogeneity of the studies was low and not statistically significant, and the funnel plot showed no publication bias. The large and statistically significant effect of ketamine on depressive symptoms supports a promising, new and effective pharmacotherapy with rapid onset, high efficacy and good tolerability. Copyright © 2015. Published by Elsevier Inc.

Statistics teaching in medical school: opinions of practising doctors.

PubMed

Miles, Susan; Price, Gill M; Swift, Louise; Shepstone, Lee; Leinster, Sam J

2010-11-04

The General Medical Council expects UK medical graduates to gain some statistical knowledge during their undergraduate education; but provides no specific guidance as to amount, content or teaching method. Published work on statistics teaching for medical undergraduates has been dominated by medical statisticians, with little input from the doctors who will actually be using this knowledge and these skills after graduation. Furthermore, doctor's statistical training needs may have changed due to advances in information technology and the increasing importance of evidence-based medicine. Thus there exists a need to investigate the views of practising medical doctors as to the statistical training required for undergraduate medical students, based on their own use of these skills in daily practice. A questionnaire was designed to investigate doctors' views about undergraduate training in statistics and the need for these skills in daily practice, with a view to informing future teaching. The questionnaire was emailed to all clinicians with a link to the University of East Anglia Medical School. Open ended questions were included to elicit doctors' opinions about both their own undergraduate training in statistics and recommendations for the training of current medical students. Content analysis was performed by two of the authors to systematically categorize and describe all the responses provided by participants. 130 doctors responded, including both hospital consultants and general practitioners. The findings indicated that most had not recognised the value of their undergraduate teaching in statistics and probability at the time, but had subsequently found the skills relevant to their career. Suggestions for improving undergraduate teaching in these areas included referring to actual research and ensuring relevance to, and integration with, clinical practice. Grounding the teaching of statistics in the context of real research studies and including examples of typical clinical work may better prepare medical students for their subsequent career.

Challenges of Big Data Analysis.

PubMed

Fan, Jianqing; Han, Fang; Liu, Han

2014-06-01

Big Data bring new opportunities to modern society and challenges to data scientists. On one hand, Big Data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data. On the other hand, the massive sample size and high dimensionality of Big Data introduce unique computational and statistical challenges, including scalability and storage bottleneck, noise accumulation, spurious correlation, incidental endogeneity, and measurement errors. These challenges are distinguished and require new computational and statistical paradigm. This article gives overviews on the salient features of Big Data and how these features impact on paradigm change on statistical and computational methods as well as computing architectures. We also provide various new perspectives on the Big Data analysis and computation. In particular, we emphasize on the viability of the sparsest solution in high-confidence set and point out that exogeneous assumptions in most statistical methods for Big Data can not be validated due to incidental endogeneity. They can lead to wrong statistical inferences and consequently wrong scientific conclusions.

Challenges of Big Data Analysis

PubMed Central

Fan, Jianqing; Han, Fang; Liu, Han

2014-01-01

Big Data bring new opportunities to modern society and challenges to data scientists. On one hand, Big Data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data. On the other hand, the massive sample size and high dimensionality of Big Data introduce unique computational and statistical challenges, including scalability and storage bottleneck, noise accumulation, spurious correlation, incidental endogeneity, and measurement errors. These challenges are distinguished and require new computational and statistical paradigm. This article gives overviews on the salient features of Big Data and how these features impact on paradigm change on statistical and computational methods as well as computing architectures. We also provide various new perspectives on the Big Data analysis and computation. In particular, we emphasize on the viability of the sparsest solution in high-confidence set and point out that exogeneous assumptions in most statistical methods for Big Data can not be validated due to incidental endogeneity. They can lead to wrong statistical inferences and consequently wrong scientific conclusions. PMID:25419469

A Meta-Meta-Analysis: Empirical Review of Statistical Power, Type I Error Rates, Effect Sizes, and Model Selection of Meta-Analyses Published in Psychology

ERIC Educational Resources Information Center

Cafri, Guy; Kromrey, Jeffrey D.; Brannick, Michael T.

2010-01-01

This article uses meta-analyses published in "Psychological Bulletin" from 1995 to 2005 to describe meta-analyses in psychology, including examination of statistical power, Type I errors resulting from multiple comparisons, and model choice. Retrospective power estimates indicated that univariate categorical and continuous moderators, individual…

Effects of Cognitive Load on Trust

DTIC Science & Technology

2013-10-01

that may be affected by load  Build a parsing tool to extract relevant features  Statistical analysis of results (by load components) Achieved...for a business application. Participants assessed potential job candidates and reviewed the applicants’ virtual resume which included standard...substantially different from each other that would make any confounding problems or other issues. Some statistics of the Australian data collection are

Why Don't All Professors Use Computers?

ERIC Educational Resources Information Center

Drew, David Eli

1989-01-01

Discusses the adoption of computer technology at universities and examines reasons why some professors don't use computers. Topics discussed include computer applications, including artificial intelligence, social science research, statistical analysis, and cooperative research; appropriateness of the technology for the task; the Computer Aptitude…

APOD Data Release of Social Network Footprint for 2015

NASA Astrophysics Data System (ADS)

Nemiroff, Robert J.; Russell, David; Allen, Alice; Connelly, Paul; Lowe, Stuart R.; Petz, Sydney; Haring, Ralf; Bonnell, Jerry T.; APOD Team

2017-01-01

APOD data for 2015 are being made freely available for download and analysis. The data includes page view statistics for the main NASA APOD website at https://apod.nasa.gov, as well as for APOD's social media sites on Facebook, Instagram, Google Plus, and Twitter. General APOD-specific demographic information for each site is included. Popularity statistics that have been archived including Page Views, Likes, Shares, Hearts, and Retweets. The downloadable Excel-type spreadsheet also includes the APOD title and (unlinked) explanation. This data is released not to highlight APOD's popularity but to encourage analyses, with potential examples involving which astronomy topics trend the best and whether popularity is social group dependent.

Global Sensitivity Analysis of Environmental Systems via Multiple Indices based on Statistical Moments of Model Outputs

NASA Astrophysics Data System (ADS)

Guadagnini, A.; Riva, M.; Dell'Oca, A.

2017-12-01

We propose to ground sensitivity of uncertain parameters of environmental models on a set of indices based on the main (statistical) moments, i.e., mean, variance, skewness and kurtosis, of the probability density function (pdf) of a target model output. This enables us to perform Global Sensitivity Analysis (GSA) of a model in terms of multiple statistical moments and yields a quantification of the impact of model parameters on features driving the shape of the pdf of model output. Our GSA approach includes the possibility of being coupled with the construction of a reduced complexity model that allows approximating the full model response at a reduced computational cost. We demonstrate our approach through a variety of test cases. These include a commonly used analytical benchmark, a simplified model representing pumping in a coastal aquifer, a laboratory-scale tracer experiment, and the migration of fracturing fluid through a naturally fractured reservoir (source) to reach an overlying formation (target). Our strategy allows discriminating the relative importance of model parameters to the four statistical moments considered. We also provide an appraisal of the error associated with the evaluation of our sensitivity metrics by replacing the original system model through the selected surrogate model. Our results suggest that one might need to construct a surrogate model with increasing level of accuracy depending on the statistical moment considered in the GSA. The methodological framework we propose can assist the development of analysis techniques targeted to model calibration, design of experiment, uncertainty quantification and risk assessment.

Statistical trends of episiotomy around the world: Comparative systematic review of changing practices.

PubMed

Clesse, Christophe; Lighezzolo-Alnot, Joëlle; De Lavergne, Sylvie; Hamlin, Sandrine; Scheffler, Michèle

2018-06-01

The authors' purpose for this article is to identify, review and interpret all publications about the episiotomy rates worldwide. Based on the criteria from the PRISMA guidelines, twenty databases were scrutinized. All studies which include national statistics related to episiotomy were selected, as well as studies presenting estimated data. Sixty-one papers were selected with publication dates between 1995 and 2016. A static and dynamic analysis of all the results was carried out. The assumption for the decline in the number of episiotomies is discussed and confirmed, recalling that nowadays high rates of episiotomy remain in less industrialized countries and East Asia. Finally, our analysis aims to investigate the potential determinants which influence apparent statistical disparities.

Statistical research on the bioactivity of new marine natural products discovered during the 28 years from 1985 to 2012.

PubMed

Hu, Yiwen; Chen, Jiahui; Hu, Guping; Yu, Jianchen; Zhu, Xun; Lin, Yongcheng; Chen, Shengping; Yuan, Jie

2015-01-07

Every year, hundreds of new compounds are discovered from the metabolites of marine organisms. Finding new and useful compounds is one of the crucial drivers for this field of research. Here we describe the statistics of bioactive compounds discovered from marine organisms from 1985 to 2012. This work is based on our database, which contains information on more than 15,000 chemical substances including 4196 bioactive marine natural products. We performed a comprehensive statistical analysis to understand the characteristics of the novel bioactive compounds and detail temporal trends, chemical structures, species distribution, and research progress. We hope this meta-analysis will provide useful information for research into the bioactivity of marine natural products and drug development.

Building the Community Online Resource for Statistical Seismicity Analysis (CORSSA)

NASA Astrophysics Data System (ADS)

Michael, A. J.; Wiemer, S.; Zechar, J. D.; Hardebeck, J. L.; Naylor, M.; Zhuang, J.; Steacy, S.; Corssa Executive Committee

2010-12-01

Statistical seismology is critical to the understanding of seismicity, the testing of proposed earthquake prediction and forecasting methods, and the assessment of seismic hazard. Unfortunately, despite its importance to seismology - especially to those aspects with great impact on public policy - statistical seismology is mostly ignored in the education of seismologists, and there is no central repository for the existing open-source software tools. To remedy these deficiencies, and with the broader goal to enhance the quality of statistical seismology research, we have begun building the Community Online Resource for Statistical Seismicity Analysis (CORSSA). CORSSA is a web-based educational platform that is authoritative, up-to-date, prominent, and user-friendly. We anticipate that the users of CORSSA will range from beginning graduate students to experienced researchers. More than 20 scientists from around the world met for a week in Zurich in May 2010 to kick-start the creation of CORSSA: the format and initial table of contents were defined; a governing structure was organized; and workshop participants began drafting articles. CORSSA materials are organized with respect to six themes, each containing between four and eight articles. The CORSSA web page, www.corssa.org, officially unveiled on September 6, 2010, debuts with an initial set of approximately 10 to 15 articles available online for viewing and commenting with additional articles to be added over the coming months. Each article will be peer-reviewed and will present a balanced discussion, including illustrative examples and code snippets. Topics in the initial set of articles will include: introductions to both CORSSA and statistical seismology, basic statistical tests and their role in seismology; understanding seismicity catalogs and their problems; basic techniques for modeling seismicity; and methods for testing earthquake predictability hypotheses. A special article will compare and review available statistical seismology software packages.

The impact of mother's literacy on child dental caries: Individual data or aggregate data analysis?

PubMed

Haghdoost, Ali-Akbar; Hessari, Hossein; Baneshi, Mohammad Reza; Rad, Maryam; Shahravan, Arash

2017-01-01

To evaluate the impact of mother's literacy on child dental caries based on a national oral health survey in Iran and to investigate the possibility of ecological fallacy in aggregate data analysis. Existing data were from second national oral health survey that was carried out in 2004, which including 8725 6 years old participants. The association of mother's literacy with caries occurrence (DMF (Decayed, Missing, Filling) total score >0) of her child was assessed using individual data by logistic regression model. Then the association of the percentages of mother's literacy and the percentages of decayed teeth in each 30 provinces of Iran was assessed using aggregated data retrieved from the data of second national oral health survey of Iran and alternatively from census of "Statistical Center of Iran" using linear regression model. The significance level was set at 0.05 for all analysis. Individual data analysis showed a statistically significant association between mother's literacy and decayed teeth of children ( P = 0.02, odds ratio = 0.83). There were not statistical significant association between mother's literacy and child dental caries in aggregate data analysis of oral health survey ( P = 0.79, B = 0.03) and census of "Statistical Center of Statistics" ( P = 0.60, B = 0.14). Literate mothers have a preventive effect on occurring dental caries of children. According to the high percentage of illiterate parents in Iran, it's logical to consider suitable methods of oral health education which do not need reading or writing. Aggregate data analysis and individual data analysis had completely different results in this study.

Methodological quality of behavioural weight loss studies: a systematic review

PubMed Central

Lemon, S. C.; Wang, M. L.; Haughton, C. F.; Estabrook, D. P.; Frisard, C. F.; Pagoto, S. L.

2018-01-01

Summary This systematic review assessed the methodological quality of behavioural weight loss intervention studies conducted among adults and associations between quality and statistically significant weight loss outcome, strength of intervention effectiveness and sample size. Searches for trials published between January, 2009 and December, 2014 were conducted using PUBMED, MEDLINE and PSYCINFO and identified ninety studies. Methodological quality indicators included study design, anthropometric measurement approach, sample size calculations, intent-to-treat (ITT) analysis, loss to follow-up rate, missing data strategy, sampling strategy, report of treatment receipt and report of intervention fidelity (mean = 6.3). Indicators most commonly utilized included randomized design (100%), objectively measured anthropometrics (96.7%), ITT analysis (86.7%) and reporting treatment adherence (76.7%). Most studies (62.2%) had a follow-up rate >75% and reported a loss to follow-up analytic strategy or minimal missing data (69.9%). Describing intervention fidelity (34.4%) and sampling from a known population (41.1%) were least common. Methodological quality was not associated with reporting a statistically significant result, effect size or sample size. This review found the published literature of behavioural weight loss trials to be of high quality for specific indicators, including study design and measurement. Identified for improvement include utilization of more rigorous statistical approaches to loss to follow up and better fidelity reporting. PMID:27071775

Meta-analysis of correlated traits via summary statistics from GWASs with an application in hypertension.

PubMed

Zhu, Xiaofeng; Feng, Tao; Tayo, Bamidele O; Liang, Jingjing; Young, J Hunter; Franceschini, Nora; Smith, Jennifer A; Yanek, Lisa R; Sun, Yan V; Edwards, Todd L; Chen, Wei; Nalls, Mike; Fox, Ervin; Sale, Michele; Bottinger, Erwin; Rotimi, Charles; Liu, Yongmei; McKnight, Barbara; Liu, Kiang; Arnett, Donna K; Chakravati, Aravinda; Cooper, Richard S; Redline, Susan

2015-01-08

Genome-wide association studies (GWASs) have identified many genetic variants underlying complex traits. Many detected genetic loci harbor variants that associate with multiple-even distinct-traits. Most current analysis approaches focus on single traits, even though the final results from multiple traits are evaluated together. Such approaches miss the opportunity to systemically integrate the phenome-wide data available for genetic association analysis. In this study, we propose a general approach that can integrate association evidence from summary statistics of multiple traits, either correlated, independent, continuous, or binary traits, which might come from the same or different studies. We allow for trait heterogeneity effects. Population structure and cryptic relatedness can also be controlled. Our simulations suggest that the proposed method has improved statistical power over single-trait analysis in most of the cases we studied. We applied our method to the Continental Origins and Genetic Epidemiology Network (COGENT) African ancestry samples for three blood pressure traits and identified four loci (CHIC2, HOXA-EVX1, IGFBP1/IGFBP3, and CDH17; p < 5.0 × 10(-8)) associated with hypertension-related traits that were missed by a single-trait analysis in the original report. Six additional loci with suggestive association evidence (p < 5.0 × 10(-7)) were also observed, including CACNA1D and WNT3. Our study strongly suggests that analyzing multiple phenotypes can improve statistical power and that such analysis can be executed with the summary statistics from GWASs. Our method also provides a way to study a cross phenotype (CP) association by using summary statistics from GWASs of multiple phenotypes. Copyright © 2015 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

UNITY: Confronting Supernova Cosmology's Statistical and Systematic Uncertainties in a Unified Bayesian Framework

NASA Astrophysics Data System (ADS)

Rubin, D.; Aldering, G.; Barbary, K.; Boone, K.; Chappell, G.; Currie, M.; Deustua, S.; Fagrelius, P.; Fruchter, A.; Hayden, B.; Lidman, C.; Nordin, J.; Perlmutter, S.; Saunders, C.; Sofiatti, C.; Supernova Cosmology Project, The

2015-11-01

While recent supernova (SN) cosmology research has benefited from improved measurements, current analysis approaches are not statistically optimal and will prove insufficient for future surveys. This paper discusses the limitations of current SN cosmological analyses in treating outliers, selection effects, shape- and color-standardization relations, unexplained dispersion, and heterogeneous observations. We present a new Bayesian framework, called UNITY (Unified Nonlinear Inference for Type-Ia cosmologY), that incorporates significant improvements in our ability to confront these effects. We apply the framework to real SN observations and demonstrate smaller statistical and systematic uncertainties. We verify earlier results that SNe Ia require nonlinear shape and color standardizations, but we now include these nonlinear relations in a statistically well-justified way. This analysis was primarily performed blinded, in that the basic framework was first validated on simulated data before transitioning to real data. We also discuss possible extensions of the method.

Standard deviation and standard error of the mean.

PubMed

Lee, Dong Kyu; In, Junyong; Lee, Sangseok

2015-06-01

In most clinical and experimental studies, the standard deviation (SD) and the estimated standard error of the mean (SEM) are used to present the characteristics of sample data and to explain statistical analysis results. However, some authors occasionally muddle the distinctive usage between the SD and SEM in medical literature. Because the process of calculating the SD and SEM includes different statistical inferences, each of them has its own meaning. SD is the dispersion of data in a normal distribution. In other words, SD indicates how accurately the mean represents sample data. However the meaning of SEM includes statistical inference based on the sampling distribution. SEM is the SD of the theoretical distribution of the sample means (the sampling distribution). While either SD or SEM can be applied to describe data and statistical results, one should be aware of reasonable methods with which to use SD and SEM. We aim to elucidate the distinctions between SD and SEM and to provide proper usage guidelines for both, which summarize data and describe statistical results.

Standard deviation and standard error of the mean

PubMed Central

In, Junyong; Lee, Sangseok

2015-01-01

In most clinical and experimental studies, the standard deviation (SD) and the estimated standard error of the mean (SEM) are used to present the characteristics of sample data and to explain statistical analysis results. However, some authors occasionally muddle the distinctive usage between the SD and SEM in medical literature. Because the process of calculating the SD and SEM includes different statistical inferences, each of them has its own meaning. SD is the dispersion of data in a normal distribution. In other words, SD indicates how accurately the mean represents sample data. However the meaning of SEM includes statistical inference based on the sampling distribution. SEM is the SD of the theoretical distribution of the sample means (the sampling distribution). While either SD or SEM can be applied to describe data and statistical results, one should be aware of reasonable methods with which to use SD and SEM. We aim to elucidate the distinctions between SD and SEM and to provide proper usage guidelines for both, which summarize data and describe statistical results. PMID:26045923

Association of obesity with hypertension and type 2 diabetes mellitus in India: A meta-analysis of observational studies

PubMed Central

Babu, Giridhara R; Murthy, G V S; Ana, Yamuna; Patel, Prital; Deepa, R; Neelon, Sara E Benjamin; Kinra, Sanjay; Reddy, K Srinath

2018-01-01

AIM To perform a meta-analysis of the association of obesity with hypertension and type 2 diabetes mellitus (T2DM) in India among adults. METHODS To conduct meta-analysis, we performed comprehensive, electronic literature search in the PubMed, CINAHL Plus, and Google Scholar. We restricted the analysis to studies with documentation of some measure of obesity namely; body mass index, waist-hip ratio, waist circumference and diagnosis of hypertension or diagnosis of T2DM. By obtaining summary estimates of all included studies, the meta-analysis was performed using both RevMan version 5 and “metan” command STATA version 11. Heterogeneity was measured by I2 statistic. Funnel plot analysis has been done to assess the study publication bias. RESULTS Of the 956 studies screened, 18 met the eligibility criteria. The pooled odds ratio between obesity and hypertension was 3.82 (95%CI: 3.39 to 4.25). The heterogeneity around this estimate (I2 statistic) was 0%, indicating low variability. The pooled odds ratio from the included studies showed a statistically significant association between obesity and T2DM (OR = 1.14, 95%CI: 1.04 to 1.24) with a high degree of variability. CONCLUSION Despite methodological differences, obesity showed significant, potentially plausible association with hypertension and T2DM in studies conducted in India. Being a modifiable risk factor, our study informs setting policy priority and intervention efforts to prevent debilitating complications. PMID:29359028

Association of obesity with hypertension and type 2 diabetes mellitus in India: A meta-analysis of observational studies.

PubMed

Babu, Giridhara R; Murthy, G V S; Ana, Yamuna; Patel, Prital; Deepa, R; Neelon, Sara E Benjamin; Kinra, Sanjay; Reddy, K Srinath

2018-01-15

To perform a meta-analysis of the association of obesity with hypertension and type 2 diabetes mellitus (T2DM) in India among adults. To conduct meta-analysis, we performed comprehensive, electronic literature search in the PubMed, CINAHL Plus, and Google Scholar. We restricted the analysis to studies with documentation of some measure of obesity namely; body mass index, waist-hip ratio, waist circumference and diagnosis of hypertension or diagnosis of T2DM. By obtaining summary estimates of all included studies, the meta-analysis was performed using both RevMan version 5 and "metan" command STATA version 11. Heterogeneity was measured by I 2 statistic. Funnel plot analysis has been done to assess the study publication bias. Of the 956 studies screened, 18 met the eligibility criteria. The pooled odds ratio between obesity and hypertension was 3.82 (95%CI: 3.39 to 4.25). The heterogeneity around this estimate (I2 statistic) was 0%, indicating low variability. The pooled odds ratio from the included studies showed a statistically significant association between obesity and T2DM (OR = 1.14, 95%CI: 1.04 to 1.24) with a high degree of variability. Despite methodological differences, obesity showed significant, potentially plausible association with hypertension and T2DM in studies conducted in India. Being a modifiable risk factor, our study informs setting policy priority and intervention efforts to prevent debilitating complications.

HydroClimATe: hydrologic and climatic analysis toolkit

USGS Publications Warehouse

Dickinson, Jesse; Hanson, Randall T.; Predmore, Steven K.

2014-01-01

The potential consequences of climate variability and climate change have been identified as major issues for the sustainability and availability of the worldwide water resources. Unlike global climate change, climate variability represents deviations from the long-term state of the climate over periods of a few years to several decades. Currently, rich hydrologic time-series data are available, but the combination of data preparation and statistical methods developed by the U.S. Geological Survey as part of the Groundwater Resources Program is relatively unavailable to hydrologists and engineers who could benefit from estimates of climate variability and its effects on periodic recharge and water-resource availability. This report documents HydroClimATe, a computer program for assessing the relations between variable climatic and hydrologic time-series data. HydroClimATe was developed for a Windows operating system. The software includes statistical tools for (1) time-series preprocessing, (2) spectral analysis, (3) spatial and temporal analysis, (4) correlation analysis, and (5) projections. The time-series preprocessing tools include spline fitting, standardization using a normal or gamma distribution, and transformation by a cumulative departure. The spectral analysis tools include discrete Fourier transform, maximum entropy method, and singular spectrum analysis. The spatial and temporal analysis tool is empirical orthogonal function analysis. The correlation analysis tools are linear regression and lag correlation. The projection tools include autoregressive time-series modeling and generation of many realizations. These tools are demonstrated in four examples that use stream-flow discharge data, groundwater-level records, gridded time series of precipitation data, and the Multivariate ENSO Index.

A perceptual space of local image statistics.

PubMed

Victor, Jonathan D; Thengone, Daniel J; Rizvi, Syed M; Conte, Mary M

2015-12-01

Local image statistics are important for visual analysis of textures, surfaces, and form. There are many kinds of local statistics, including those that capture luminance distributions, spatial contrast, oriented segments, and corners. While sensitivity to each of these kinds of statistics have been well-studied, much less is known about visual processing when multiple kinds of statistics are relevant, in large part because the dimensionality of the problem is high and different kinds of statistics interact. To approach this problem, we focused on binary images on a square lattice - a reduced set of stimuli which nevertheless taps many kinds of local statistics. In this 10-parameter space, we determined psychophysical thresholds to each kind of statistic (16 observers) and all of their pairwise combinations (4 observers). Sensitivities and isodiscrimination contours were consistent across observers. Isodiscrimination contours were elliptical, implying a quadratic interaction rule, which in turn determined ellipsoidal isodiscrimination surfaces in the full 10-dimensional space, and made predictions for sensitivities to complex combinations of statistics. These predictions, including the prediction of a combination of statistics that was metameric to random, were verified experimentally. Finally, check size had only a mild effect on sensitivities over the range from 2.8 to 14min, but sensitivities to second- and higher-order statistics was substantially lower at 1.4min. In sum, local image statistics form a perceptual space that is highly stereotyped across observers, in which different kinds of statistics interact according to simple rules. Copyright © 2015 Elsevier Ltd. All rights reserved.

A perceptual space of local image statistics

PubMed Central

Victor, Jonathan D.; Thengone, Daniel J.; Rizvi, Syed M.; Conte, Mary M.

2015-01-01

Local image statistics are important for visual analysis of textures, surfaces, and form. There are many kinds of local statistics, including those that capture luminance distributions, spatial contrast, oriented segments, and corners. While sensitivity to each of these kinds of statistics have been well-studied, much less is known about visual processing when multiple kinds of statistics are relevant, in large part because the dimensionality of the problem is high and different kinds of statistics interact. To approach this problem, we focused on binary images on a square lattice – a reduced set of stimuli which nevertheless taps many kinds of local statistics. In this 10-parameter space, we determined psychophysical thresholds to each kind of statistic (16 observers) and all of their pairwise combinations (4 observers). Sensitivities and isodiscrimination contours were consistent across observers. Isodiscrimination contours were elliptical, implying a quadratic interaction rule, which in turn determined ellipsoidal isodiscrimination surfaces in the full 10-dimensional space, and made predictions for sensitivities to complex combinations of statistics. These predictions, including the prediction of a combination of statistics that was metameric to random, were verified experimentally. Finally, check size had only a mild effect on sensitivities over the range from 2.8 to 14 min, but sensitivities to second- and higher-order statistics was substantially lower at 1.4 min. In sum, local image statistics forms a perceptual space that is highly stereotyped across observers, in which different kinds of statistics interact according to simple rules. PMID:26130606

Rare-Variant Association Analysis: Study Designs and Statistical Tests

PubMed Central

Lee, Seunggeung; Abecasis, Gonçalo R.; Boehnke, Michael; Lin, Xihong

2014-01-01

Despite the extensive discovery of trait- and disease-associated common variants, much of the genetic contribution to complex traits remains unexplained. Rare variants can explain additional disease risk or trait variability. An increasing number of studies are underway to identify trait- and disease-associated rare variants. In this review, we provide an overview of statistical issues in rare-variant association studies with a focus on study designs and statistical tests. We present the design and analysis pipeline of rare-variant studies and review cost-effective sequencing designs and genotyping platforms. We compare various gene- or region-based association tests, including burden tests, variance-component tests, and combined omnibus tests, in terms of their assumptions and performance. Also discussed are the related topics of meta-analysis, population-stratification adjustment, genotype imputation, follow-up studies, and heritability due to rare variants. We provide guidelines for analysis and discuss some of the challenges inherent in these studies and future research directions. PMID:24995866

Research Update: Spatially resolved mapping of electronic structure on atomic level by multivariate statistical analysis

DOE PAGES

Belianinov, Alex; Panchapakesan, G.; Lin, Wenzhi; ...

2014-12-02

Atomic level spatial variability of electronic structure in Fe-based superconductor FeTe0.55Se0.45 (Tc = 15 K) is explored using current-imaging tunneling-spectroscopy. Multivariate statistical analysis of the data differentiates regions of dissimilar electronic behavior that can be identified with the segregation of chalcogen atoms, as well as boundaries between terminations and near neighbor interactions. Subsequent clustering analysis allows identification of the spatial localization of these dissimilar regions. Similar statistical analysis of modeled calculated density of states of chemically inhomogeneous FeTe1 x Sex structures further confirms that the two types of chalcogens, i.e., Te and Se, can be identified by their electronic signaturemore » and differentiated by their local chemical environment. This approach allows detailed chemical discrimination of the scanning tunneling microscopy data including separation of atomic identities, proximity, and local configuration effects and can be universally applicable to chemically and electronically inhomogeneous surfaces.« less

Research Update: Spatially resolved mapping of electronic structure on atomic level by multivariate statistical analysis

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

Belianinov, Alex, E-mail: belianinova@ornl.gov; Ganesh, Panchapakesan; Lin, Wenzhi

2014-12-01

Atomic level spatial variability of electronic structure in Fe-based superconductor FeTe{sub 0.55}Se{sub 0.45} (T{sub c} = 15 K) is explored using current-imaging tunneling-spectroscopy. Multivariate statistical analysis of the data differentiates regions of dissimilar electronic behavior that can be identified with the segregation of chalcogen atoms, as well as boundaries between terminations and near neighbor interactions. Subsequent clustering analysis allows identification of the spatial localization of these dissimilar regions. Similar statistical analysis of modeled calculated density of states of chemically inhomogeneous FeTe{sub 1−x}Se{sub x} structures further confirms that the two types of chalcogens, i.e., Te and Se, can be identified bymore » their electronic signature and differentiated by their local chemical environment. This approach allows detailed chemical discrimination of the scanning tunneling microscopy data including separation of atomic identities, proximity, and local configuration effects and can be universally applicable to chemically and electronically inhomogeneous surfaces.« less

Active Nonlinear Feedback Control for Aerospace Systems. Processor

DTIC Science & Technology

1990-12-01

relating to the role of nonlinearities in feedback control. These area include Lyapunov function theory, chaotic controllers, statistical energy analysis , phase robustness, and optimal nonlinear control theory.

Data management in large-scale collaborative toxicity studies: how to file experimental data for automated statistical analysis.

PubMed

Stanzel, Sven; Weimer, Marc; Kopp-Schneider, Annette

2013-06-01

High-throughput screening approaches are carried out for the toxicity assessment of a large number of chemical compounds. In such large-scale in vitro toxicity studies several hundred or thousand concentration-response experiments are conducted. The automated evaluation of concentration-response data using statistical analysis scripts saves time and yields more consistent results in comparison to data analysis performed by the use of menu-driven statistical software. Automated statistical analysis requires that concentration-response data are available in a standardised data format across all compounds. To obtain consistent data formats, a standardised data management workflow must be established, including guidelines for data storage, data handling and data extraction. In this paper two procedures for data management within large-scale toxicological projects are proposed. Both procedures are based on Microsoft Excel files as the researcher's primary data format and use a computer programme to automate the handling of data files. The first procedure assumes that data collection has not yet started whereas the second procedure can be used when data files already exist. Successful implementation of the two approaches into the European project ACuteTox is illustrated. Copyright © 2012 Elsevier Ltd. All rights reserved.

Computer program documentation for the pasture/range condition assessment processor

NASA Technical Reports Server (NTRS)

Mcintyre, K. S.; Miller, T. G. (Principal Investigator)

1982-01-01

The processor which drives for the RANGE software allows the user to analyze LANDSAT data containing pasture and rangeland. Analysis includes mapping, generating statistics, calculating vegetative indexes, and plotting vegetative indexes. Routines for using the processor are given. A flow diagram is included.

EHME: a new word database for research in Basque language.

PubMed

Acha, Joana; Laka, Itziar; Landa, Josu; Salaburu, Pello

2014-11-14

This article presents EHME, the frequency dictionary of Basque structure, an online program that enables researchers in psycholinguistics to extract word and nonword stimuli, based on a broad range of statistics concerning the properties of Basque words. The database consists of 22.7 million tokens, and properties available include morphological structure frequency and word-similarity measures, apart from classical indexes: word frequency, orthographic structure, orthographic similarity, bigram and biphone frequency, and syllable-based measures. Measures are indexed at the lemma, morpheme and word level. We include reliability and validation analysis. The application is freely available, and enables the user to extract words based on concrete statistical criteria 1 , as well as to obtain statistical characteristics from a list of words

Specialized data analysis of SSME and advanced propulsion system vibration measurements

NASA Technical Reports Server (NTRS)

Coffin, Thomas; Swanson, Wayne L.; Jong, Yen-Yi

1993-01-01

The basic objectives of this contract were to perform detailed analysis and evaluation of dynamic data obtained during Space Shuttle Main Engine (SSME) test and flight operations, including analytical/statistical assessment of component dynamic performance, and to continue the development and implementation of analytical/statistical models to effectively define nominal component dynamic characteristics, detect anomalous behavior, and assess machinery operational conditions. This study was to provide timely assessment of engine component operational status, identify probable causes of malfunction, and define feasible engineering solutions. The work was performed under three broad tasks: (1) Analysis, Evaluation, and Documentation of SSME Dynamic Test Results; (2) Data Base and Analytical Model Development and Application; and (3) Development and Application of Vibration Signature Analysis Techniques.

A Novel Genome-Information Content-Based Statistic for Genome-Wide Association Analysis Designed for Next-Generation Sequencing Data

PubMed Central

Luo, Li; Zhu, Yun

2012-01-01

Abstract The genome-wide association studies (GWAS) designed for next-generation sequencing data involve testing association of genomic variants, including common, low frequency, and rare variants. The current strategies for association studies are well developed for identifying association of common variants with the common diseases, but may be ill-suited when large amounts of allelic heterogeneity are present in sequence data. Recently, group tests that analyze their collective frequency differences between cases and controls shift the current variant-by-variant analysis paradigm for GWAS of common variants to the collective test of multiple variants in the association analysis of rare variants. However, group tests ignore differences in genetic effects among SNPs at different genomic locations. As an alternative to group tests, we developed a novel genome-information content-based statistics for testing association of the entire allele frequency spectrum of genomic variation with the diseases. To evaluate the performance of the proposed statistics, we use large-scale simulations based on whole genome low coverage pilot data in the 1000 Genomes Project to calculate the type 1 error rates and power of seven alternative statistics: a genome-information content-based statistic, the generalized T2, collapsing method, multivariate and collapsing (CMC) method, individual χ2 test, weighted-sum statistic, and variable threshold statistic. Finally, we apply the seven statistics to published resequencing dataset from ANGPTL3, ANGPTL4, ANGPTL5, and ANGPTL6 genes in the Dallas Heart Study. We report that the genome-information content-based statistic has significantly improved type 1 error rates and higher power than the other six statistics in both simulated and empirical datasets. PMID:22651812

A novel genome-information content-based statistic for genome-wide association analysis designed for next-generation sequencing data.

PubMed

Luo, Li; Zhu, Yun; Xiong, Momiao

2012-06-01

The genome-wide association studies (GWAS) designed for next-generation sequencing data involve testing association of genomic variants, including common, low frequency, and rare variants. The current strategies for association studies are well developed for identifying association of common variants with the common diseases, but may be ill-suited when large amounts of allelic heterogeneity are present in sequence data. Recently, group tests that analyze their collective frequency differences between cases and controls shift the current variant-by-variant analysis paradigm for GWAS of common variants to the collective test of multiple variants in the association analysis of rare variants. However, group tests ignore differences in genetic effects among SNPs at different genomic locations. As an alternative to group tests, we developed a novel genome-information content-based statistics for testing association of the entire allele frequency spectrum of genomic variation with the diseases. To evaluate the performance of the proposed statistics, we use large-scale simulations based on whole genome low coverage pilot data in the 1000 Genomes Project to calculate the type 1 error rates and power of seven alternative statistics: a genome-information content-based statistic, the generalized T(2), collapsing method, multivariate and collapsing (CMC) method, individual χ(2) test, weighted-sum statistic, and variable threshold statistic. Finally, we apply the seven statistics to published resequencing dataset from ANGPTL3, ANGPTL4, ANGPTL5, and ANGPTL6 genes in the Dallas Heart Study. We report that the genome-information content-based statistic has significantly improved type 1 error rates and higher power than the other six statistics in both simulated and empirical datasets.

Enhancing predictive accuracy and reproducibility in clinical evaluation research: Commentary on the special section of the Journal of Evaluation in Clinical Practice.

PubMed

Bryant, Fred B

2016-12-01

This paper introduces a special section of the current issue of the Journal of Evaluation in Clinical Practice that includes a set of 6 empirical articles showcasing a versatile, new machine-learning statistical method, known as optimal data (or discriminant) analysis (ODA), specifically designed to produce statistical models that maximize predictive accuracy. As this set of papers clearly illustrates, ODA offers numerous important advantages over traditional statistical methods-advantages that enhance the validity and reproducibility of statistical conclusions in empirical research. This issue of the journal also includes a review of a recently published book that provides a comprehensive introduction to the logic, theory, and application of ODA in empirical research. It is argued that researchers have much to gain by using ODA to analyze their data. © 2016 John Wiley & Sons, Ltd.

The classification of gunshot residue using laser electrospray mass spectrometry and offline multivariate statistical analysis

USDA-ARS?s Scientific Manuscript database

Nonresonant laser vaporization combined with high-resolution electrospray time-of-flight mass spectrometry enables analysis of a casing after discharge of a firearm revealing organic signature molecules including methyl centralite (MC), diphenylamine (DPA), N-nitrosodiphenylamine (N-NO-DPA), 4-nitro...

An Analysis of Methods Used to Examine Gender Differences in Computer-Related Behavior.

ERIC Educational Resources Information Center

Kay, Robin

1992-01-01

Review of research investigating gender differences in computer-related behavior examines statistical and methodological flaws. Issues addressed include sample selection, sample size, scale development, scale quality, the use of univariate and multivariate analyses, regressional analysis, construct definition, construct testing, and the…

Links between Characteristics of Collaborative Peer Video Analysis Events and Literacy Teachers' Outcomes

ERIC Educational Resources Information Center

Arya, Poonam; Christ, Tanya; Chiu, Ming

2015-01-01

This study examined how characteristics of Collaborative Peer Video Analysis (CPVA) events are related to teachers' pedagogical outcomes. Data included 39 transcribed literacy video events, in which 14 in-service teachers engaged in discussions of their video clips. Emergent coding and Statistical Discourse Analysis were used to analyze the data.…

Comprehension-Based versus Production-Based Grammar Instruction: A Meta-Analysis of Comparative Studies

ERIC Educational Resources Information Center

Shintani, Natsuko; Li, Shaofeng; Ellis, Rod

2013-01-01

This article reports a meta-analysis of studies that investigated the relative effectiveness of comprehension-based instruction (CBI) and production-based instruction (PBI). The meta-analysis only included studies that featured a direct comparison of CBI and PBI in order to ensure methodological and statistical robustness. A total of 35 research…

Combining the Bourne-Shell, sed and awk in the UNIX Environment for Language Analysis.

ERIC Educational Resources Information Center

Schmitt, Lothar M.; Christianson, Kiel T.

This document describes how to construct tools for language analysis in research and teaching using the Bourne-shell, sed, and awk, three search tools, in the UNIX operating system. Applications include: searches for words, phrases, grammatical patterns, and phonemic patterns in text; statistical analysis of text in regard to such searches,…

Review of U.S. Army Unmanned Aerial Systems Accident Reports: Analysis of Human Error Contributions

DTIC Science & Technology

2018-03-20

USAARL Report No. 2018-08 Review of U.S. Army Unmanned Aerial Systems Accident Reports: Analysis of Human Error Contributions By Kathryn A...3 Statistical Analysis Approach ..............................................................................................3 Results...1 Introduction The success of unmanned aerial systems (UAS) operations relies upon a variety of factors, including, but not limited to

Putting the "But" Back in Meta-Analysis: Issues Affecting the Validity of Quantitative Reviews.

ERIC Educational Resources Information Center

L'Hommedieu, Randi; And Others

Some of the frustrations inherent in trying to incorporate qualifications of statistical results into meta-analysis are reviewed, and some solutions are proposed to prevent the loss of information in meta-analytic reports. The validity of a meta-analysis depends on several factors, including the: thoroughness of the literature search; selection of…

Use of check lists in assessing the statistical content of medical studies.

PubMed Central

Gardner, M J; Machin, D; Campbell, M J

1986-01-01

Two check lists are used routinely in the statistical assessment of manuscripts submitted to the "BMJ." One is for papers of a general nature and the other specifically for reports on clinical trials. Each check list includes questions on the design, conduct, analysis, and presentation of studies, and answers to these contribute to the overall statistical evaluation. Only a small proportion of submitted papers are assessed statistically, and these are selected at the refereeing or editorial stage. Examination of the use of the check lists showed that most papers contained statistical failings, many of which could easily be remedied. It is recommended that the check lists should be used by statistical referees, editorial staff, and authors and also during the design stage of studies. PMID:3082452

Report: Scientific Software.

ERIC Educational Resources Information Center

Borman, Stuart A.

1985-01-01

Discusses various aspects of scientific software, including evaluation and selection of commercial software products; program exchanges, catalogs, and other information sources; major data analysis packages; statistics and chemometrics software; and artificial intelligence. (JN)

Marketing of Personalized Cancer Care on the Web: An Analysis of Internet Websites

PubMed Central

Cronin, Angel; Bair, Elizabeth; Lindeman, Neal; Viswanath, Vish; Janeway, Katherine A.

2015-01-01

Internet marketing may accelerate the use of care based on genomic or tumor-derived data. However, online marketing may be detrimental if it endorses products of unproven benefit. We conducted an analysis of Internet websites to identify personalized cancer medicine (PCM) products and claims. A Delphi Panel categorized PCM as standard or nonstandard based on evidence of clinical utility. Fifty-five websites, sponsored by commercial entities, academic institutions, physicians, research institutes, and organizations, that marketed PCM included somatic (58%) and germline (20%) analysis, interpretive services (15%), and physicians/institutions offering personalized care (44%). Of 32 sites offering somatic analysis, 56% included specific test information (range 1–152 tests). All statistical tests were two-sided, and comparisons of website content were conducted using McNemar’s test. More websites contained information about the benefits than limitations of PCM (85% vs 27%, P < .001). Websites specifying somatic analysis were statistically significantly more likely to market one or more nonstandard tests as compared with standard tests (88% vs 44%, P = .04). PMID:25745021

Technical Brief for the final report presentation for Statistical summaries of selected Iowa streamflow data through September 2013, U.S. Geological Survey Open-File Report 2015-1214, Iowa DOT Research Project TR-669.

DOT National Transportation Integrated Search

2015-01-01

Statistical summaries of streamflow data collected at 184 streamgages in Iowa are presented in this report. All streamgages included for analysis have at least 10 years of continuous record collected before or through September 2013. This report is a...

Developing statistical wildlife habitat relationships for assessing cumulative effects of fuels treatments: Final Report for Joint Fire Science Program Project

Treesearch

Samuel A. Cushman; Kevin S. McKelvey

2006-01-01

The primary weakness in our current ability to evaluate future landscapes in terms of wildlife lies in the lack of quantitative models linking wildlife to forest stand conditions, including fuels treatments. This project focuses on 1) developing statistical wildlife habitat relationships models (WHR) utilizing Forest Inventory and Analysis (FIA) and National Vegetation...

Statistical modeling of space shuttle environmental data

NASA Technical Reports Server (NTRS)

Tubbs, J. D.; Brewer, D. W.

1983-01-01

Statistical models which use a class of bivariate gamma distribution are examined. Topics discussed include: (1) the ratio of positively correlated gamma varieties; (2) a method to determine if unequal shape parameters are necessary in bivariate gamma distribution; (3) differential equations for modal location of a family of bivariate gamma distribution; and (4) analysis of some wind gust data using the analytical results developed for modeling application.

Methodological Standards for Meta-Analyses and Qualitative Systematic Reviews of Cardiac Prevention and Treatment Studies: A Scientific Statement From the American Heart Association.

PubMed

Rao, Goutham; Lopez-Jimenez, Francisco; Boyd, Jack; D'Amico, Frank; Durant, Nefertiti H; Hlatky, Mark A; Howard, George; Kirley, Katherine; Masi, Christopher; Powell-Wiley, Tiffany M; Solomonides, Anthony E; West, Colin P; Wessel, Jennifer

2017-09-05

Meta-analyses are becoming increasingly popular, especially in the fields of cardiovascular disease prevention and treatment. They are often considered to be a reliable source of evidence for making healthcare decisions. Unfortunately, problems among meta-analyses such as the misapplication and misinterpretation of statistical methods and tests are long-standing and widespread. The purposes of this statement are to review key steps in the development of a meta-analysis and to provide recommendations that will be useful for carrying out meta-analyses and for readers and journal editors, who must interpret the findings and gauge methodological quality. To make the statement practical and accessible, detailed descriptions of statistical methods have been omitted. Based on a survey of cardiovascular meta-analyses, published literature on methodology, expert consultation, and consensus among the writing group, key recommendations are provided. Recommendations reinforce several current practices, including protocol registration; comprehensive search strategies; methods for data extraction and abstraction; methods for identifying, measuring, and dealing with heterogeneity; and statistical methods for pooling results. Other practices should be discontinued, including the use of levels of evidence and evidence hierarchies to gauge the value and impact of different study designs (including meta-analyses) and the use of structured tools to assess the quality of studies to be included in a meta-analysis. We also recommend choosing a pooling model for conventional meta-analyses (fixed effect or random effects) on the basis of clinical and methodological similarities among studies to be included, rather than the results of a test for statistical heterogeneity. © 2017 American Heart Association, Inc.

A guide to understanding meta-analysis.

PubMed

Israel, Heidi; Richter, Randy R

2011-07-01

With the focus on evidence-based practice in healthcare, a well-conducted systematic review that includes a meta-analysis where indicated represents a high level of evidence for treatment effectiveness. The purpose of this commentary is to assist clinicians in understanding meta-analysis as a statistical tool using both published articles and explanations of components of the technique. We describe what meta-analysis is, what heterogeneity is, and how it affects meta-analysis, effect size, the modeling techniques of meta-analysis, and strengths and weaknesses of meta-analysis. Common components like forest plot interpretation, software that may be used, special cases for meta-analysis, such as subgroup analysis, individual patient data, and meta-regression, and a discussion of criticisms, are included.

A probabilistic analysis of electrical equipment vulnerability to carbon fibers

NASA Technical Reports Server (NTRS)

Elber, W.

1980-01-01

The statistical problems of airborne carbon fibers falling onto electrical circuits were idealized and analyzed. The probability of making contact between randomly oriented finite length fibers and sets of parallel conductors with various spacings and lengths was developed theoretically. The probability of multiple fibers joining to bridge a single gap between conductors, or forming continuous networks is included. From these theoretical considerations, practical statistical analyses to assess the likelihood of causing electrical malfunctions was produced. The statistics obtained were confirmed by comparison with results of controlled experiments.

Statistical Modeling for Radiation Hardness Assurance

NASA Technical Reports Server (NTRS)

Ladbury, Raymond L.

2014-01-01

We cover the models and statistics associated with single event effects (and total ionizing dose), why we need them, and how to use them: What models are used, what errors exist in real test data, and what the model allows us to say about the DUT will be discussed. In addition, how to use other sources of data such as historical, heritage, and similar part and how to apply experience, physics, and expert opinion to the analysis will be covered. Also included will be concepts of Bayesian statistics, data fitting, and bounding rates.

The skeletal maturation status estimated by statistical shape analysis: axial images of Japanese cervical vertebra.

PubMed

Shin, S M; Kim, Y-I; Choi, Y-S; Yamaguchi, T; Maki, K; Cho, B-H; Park, S-B

2015-01-01

To evaluate axial cervical vertebral (ACV) shape quantitatively and to build a prediction model for skeletal maturation level using statistical shape analysis for Japanese individuals. The sample included 24 female and 19 male patients with hand-wrist radiographs and CBCT images. Through generalized Procrustes analysis and principal components (PCs) analysis, the meaningful PCs were extracted from each ACV shape and analysed for the estimation regression model. Each ACV shape had meaningful PCs, except for the second axial cervical vertebra. Based on these models, the smallest prediction intervals (PIs) were from the combination of the shape space PCs, age and gender. Overall, the PIs of the male group were smaller than those of the female group. There was no significant correlation between centroid size as a size factor and skeletal maturation level. Our findings suggest that the ACV maturation method, which was applied by statistical shape analysis, could confirm information about skeletal maturation in Japanese individuals as an available quantifier of skeletal maturation and could be as useful a quantitative method as the skeletal maturation index.

The skeletal maturation status estimated by statistical shape analysis: axial images of Japanese cervical vertebra

PubMed Central

Shin, S M; Choi, Y-S; Yamaguchi, T; Maki, K; Cho, B-H; Park, S-B

2015-01-01

Objectives: To evaluate axial cervical vertebral (ACV) shape quantitatively and to build a prediction model for skeletal maturation level using statistical shape analysis for Japanese individuals. Methods: The sample included 24 female and 19 male patients with hand–wrist radiographs and CBCT images. Through generalized Procrustes analysis and principal components (PCs) analysis, the meaningful PCs were extracted from each ACV shape and analysed for the estimation regression model. Results: Each ACV shape had meaningful PCs, except for the second axial cervical vertebra. Based on these models, the smallest prediction intervals (PIs) were from the combination of the shape space PCs, age and gender. Overall, the PIs of the male group were smaller than those of the female group. There was no significant correlation between centroid size as a size factor and skeletal maturation level. Conclusions: Our findings suggest that the ACV maturation method, which was applied by statistical shape analysis, could confirm information about skeletal maturation in Japanese individuals as an available quantifier of skeletal maturation and could be as useful a quantitative method as the skeletal maturation index. PMID:25411713

Access and Ownership in the Academic Environment: One Library's Progress Report.

ERIC Educational Resources Information Center

Brin, Beth; Cochran, Elissa

1994-01-01

Describes the methodology used at the University of Arizona Library to address the issue of access versus ownership of library materials. Topics discussed include participatory management; data collection, including focus groups, interlibrary loan statistics, and graduate research citation analysis; and resulting recommendations, including…

Statistical Methods for the Analysis of Discrete Choice Experiments: A Report of the ISPOR Conjoint Analysis Good Research Practices Task Force.

PubMed

Hauber, A Brett; González, Juan Marcos; Groothuis-Oudshoorn, Catharina G M; Prior, Thomas; Marshall, Deborah A; Cunningham, Charles; IJzerman, Maarten J; Bridges, John F P

2016-06-01

Conjoint analysis is a stated-preference survey method that can be used to elicit responses that reveal preferences, priorities, and the relative importance of individual features associated with health care interventions or services. Conjoint analysis methods, particularly discrete choice experiments (DCEs), have been increasingly used to quantify preferences of patients, caregivers, physicians, and other stakeholders. Recent consensus-based guidance on good research practices, including two recent task force reports from the International Society for Pharmacoeconomics and Outcomes Research, has aided in improving the quality of conjoint analyses and DCEs in outcomes research. Nevertheless, uncertainty regarding good research practices for the statistical analysis of data from DCEs persists. There are multiple methods for analyzing DCE data. Understanding the characteristics and appropriate use of different analysis methods is critical to conducting a well-designed DCE study. This report will assist researchers in evaluating and selecting among alternative approaches to conducting statistical analysis of DCE data. We first present a simplistic DCE example and a simple method for using the resulting data. We then present a pedagogical example of a DCE and one of the most common approaches to analyzing data from such a question format-conditional logit. We then describe some common alternative methods for analyzing these data and the strengths and weaknesses of each alternative. We present the ESTIMATE checklist, which includes a list of questions to consider when justifying the choice of analysis method, describing the analysis, and interpreting the results. Copyright © 2016 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

RADSS: an integration of GIS, spatial statistics, and network service for regional data mining

NASA Astrophysics Data System (ADS)

Hu, Haitang; Bao, Shuming; Lin, Hui; Zhu, Qing

2005-10-01

Regional data mining, which aims at the discovery of knowledge about spatial patterns, clusters or association between regions, has widely applications nowadays in social science, such as sociology, economics, epidemiology, crime, and so on. Many applications in the regional or other social sciences are more concerned with the spatial relationship, rather than the precise geographical location. Based on the spatial continuity rule derived from Tobler's first law of geography: observations at two sites tend to be more similar to each other if the sites are close together than if far apart, spatial statistics, as an important means for spatial data mining, allow the users to extract the interesting and useful information like spatial pattern, spatial structure, spatial association, spatial outlier and spatial interaction, from the vast amount of spatial data or non-spatial data. Therefore, by integrating with the spatial statistical methods, the geographical information systems will become more powerful in gaining further insights into the nature of spatial structure of regional system, and help the researchers to be more careful when selecting appropriate models. However, the lack of such tools holds back the application of spatial data analysis techniques and development of new methods and models (e.g., spatio-temporal models). Herein, we make an attempt to develop such an integrated software and apply it into the complex system analysis for the Poyang Lake Basin. This paper presents a framework for integrating GIS, spatial statistics and network service in regional data mining, as well as their implementation. After discussing the spatial statistics methods involved in regional complex system analysis, we introduce RADSS (Regional Analysis and Decision Support System), our new regional data mining tool, by integrating GIS, spatial statistics and network service. RADSS includes the functions of spatial data visualization, exploratory spatial data analysis, and spatial statistics. The tool also includes some fundamental spatial and non-spatial database in regional population and environment, which can be updated by external database via CD or network. Utilizing this data mining and exploratory analytical tool, the users can easily and quickly analyse the huge mount of the interrelated regional data, and better understand the spatial patterns and trends of the regional development, so as to make a credible and scientific decision. Moreover, it can be used as an educational tool for spatial data analysis and environmental studies. In this paper, we also present a case study on Poyang Lake Basin as an application of the tool and spatial data mining in complex environmental studies. At last, several concluding remarks are discussed.

Clinical periodontal variables in patients with and without dementia-a systematic review and meta-analysis.

PubMed

Maldonado, Alejandra; Laugisch, Oliver; Bürgin, Walter; Sculean, Anton; Eick, Sigrun

2018-06-22

Considering the increasing number of elderly people, dementia has gained an important role in today's society. Although the contributing factors for dementia have not been fully understood, chronic periodontitis (CP) seems to have a possible link to dementia. To conduct a systematic review including meta-analysis in order to assess potential differences in clinical periodontal variables between patients with dementia and non-demented individuals. The following focused question was evaluated: is periodontitis associated with dementia? Electronic searches in two databases, MEDLINE and EMBASE, were conducted. Meta-analysis was performed with the collected data in order to find a statistically significant difference in clinical periodontal variables between the group of dementia and the cognitive normal controls. Forty-two articles remained for full text reading. Finally, seven articles met the inclusion criteria and only five studies provided data suitable for meta-analysis. Periodontal probing depth (PPD), bleeding on probing (BOP), gingival bleeding index (GBI), clinical attachment level (CAL), and plaque index (PI) were included as periodontal variables in the meta-analysis. Each variable revealed a statistically significant difference between the groups. In an attempt to reveal an overall difference between the periodontal variables in dementia patients and non-demented individuals, the chosen variables were transformed into units that resulted in a statistically significant overall difference (p < 0.00001). The current findings indicate that compared to systemically healthy individuals, demented patients show significantly worse clinical periodontal variables. However, further epidemiological studies including a high numbers of participants, the use of exact definitions both for dementia and chronic periodontitis and adjusted for cofounders is warranted. These findings appear to support the putative link between CP and dementia. Consequently, the need for periodontal screening and treatment of elderly demented people should be emphasized.

The Effectiveness of Computer-Assisted Instruction to Teach Physical Examination to Students and Trainees in the Health Sciences Professions: A Systematic Review and Meta-Analysis.

PubMed

Tomesko, Jennifer; Touger-Decker, Riva; Dreker, Margaret; Zelig, Rena; Parrott, James Scott

2017-01-01

To explore knowledge and skill acquisition outcomes related to learning physical examination (PE) through computer-assisted instruction (CAI) compared with a face-to-face (F2F) approach. A systematic literature review and meta-analysis published between January 2001 and December 2016 was conducted. Databases searched included Medline, Cochrane, CINAHL, ERIC, Ebsco, Scopus, and Web of Science. Studies were synthesized by study design, intervention, and outcomes. Statistical analyses included DerSimonian-Laird random-effects model. In total, 7 studies were included in the review, and 5 in the meta-analysis. There were no statistically significant differences for knowledge (mean difference [MD] = 5.39, 95% confidence interval [CI]: -2.05 to 12.84) or skill acquisition (MD = 0.35, 95% CI: -5.30 to 6.01). The evidence does not suggest a strong consistent preference for either CAI or F2F instruction to teach students/trainees PE. Further research is needed to identify conditions which examine knowledge and skill acquisition outcomes that favor one mode of instruction over the other.

New Trends in Mathematics Teaching, Volume III.

ERIC Educational Resources Information Center

United Nations Educational, Scientific, and Cultural Organization, Paris (France).

Each of the ten chapters in this volume is intended to present an objective analysis of the trends of some important subtopic in mathematics education and each includes a bibliography for fuller study. The chapters cover primary school mathematics, algebra, geometry, probability and statistics, analysis, logic, applications of mathematics, methods…

Analysis of Alaska transportation sectors to assess energy use and impacts of price shocks and climate change legislation.

DOT National Transportation Integrated Search

2013-04-01

We analyzed the use of energy by Alaskas transportation sectors to assess the impact of sudden fuel prices changes. : We conducted three types of analysis: 1) Development of broad energy use statistics for each transportation sector, : including t...

Interpreting Bivariate Regression Coefficients: Going beyond the Average

ERIC Educational Resources Information Center

Halcoussis, Dennis; Phillips, G. Michael

2010-01-01

Statistics, econometrics, investment analysis, and data analysis classes often review the calculation of several types of averages, including the arithmetic mean, geometric mean, harmonic mean, and various weighted averages. This note shows how each of these can be computed using a basic regression framework. By recognizing when a regression model…

INVESTIGATION OF THE USE OF STATISTICS IN COUNSELING STUDENTS.

ERIC Educational Resources Information Center

HEWES, ROBERT F.

THE OBJECTIVE WAS TO EMPLOY TECHNIQUES OF PROFILE ANALYSIS TO DEVELOP THE JOINT PROBABILITY OF SELECTING A SUITABLE SUBJECT MAJOR AND OF ASSURING TO A HIGH DEGREE GRADUATION FROM COLLEGE WITH THAT MAJOR. THE SAMPLE INCLUDED 1,197 MIT FRESHMEN STUDENTS IN 1952-53, AND THE VALIDATION GROUP INCLUDED 699 ENTRANTS IN 1954. DATA INCLUDED SECONDARY…

Clustering, randomness and regularity in cloud fields. I - Theoretical considerations. II - Cumulus cloud fields

NASA Technical Reports Server (NTRS)

Weger, R. C.; Lee, J.; Zhu, Tianri; Welch, R. M.

1992-01-01

The current controversy existing in reference to the regularity vs. clustering in cloud fields is examined by means of analysis and simulation studies based upon nearest-neighbor cumulative distribution statistics. It is shown that the Poisson representation of random point processes is superior to pseudorandom-number-generated models and that pseudorandom-number-generated models bias the observed nearest-neighbor statistics towards regularity. Interpretation of this nearest-neighbor statistics is discussed for many cases of superpositions of clustering, randomness, and regularity. A detailed analysis is carried out of cumulus cloud field spatial distributions based upon Landsat, AVHRR, and Skylab data, showing that, when both large and small clouds are included in the cloud field distributions, the cloud field always has a strong clustering signal.

Statistical Research on the Bioactivity of New Marine Natural Products Discovered during the 28 Years from 1985 to 2012

PubMed Central

Hu, Yiwen; Chen, Jiahui; Hu, Guping; Yu, Jianchen; Zhu, Xun; Lin, Yongcheng; Chen, Shengping; Yuan, Jie

2015-01-01

Every year, hundreds of new compounds are discovered from the metabolites of marine organisms. Finding new and useful compounds is one of the crucial drivers for this field of research. Here we describe the statistics of bioactive compounds discovered from marine organisms from 1985 to 2012. This work is based on our database, which contains information on more than 15,000 chemical substances including 4196 bioactive marine natural products. We performed a comprehensive statistical analysis to understand the characteristics of the novel bioactive compounds and detail temporal trends, chemical structures, species distribution, and research progress. We hope this meta-analysis will provide useful information for research into the bioactivity of marine natural products and drug development. PMID:25574736

[Statistical validity of the Mexican Food Security Scale and the Latin American and Caribbean Food Security Scale].

PubMed

Villagómez-Ornelas, Paloma; Hernández-López, Pedro; Carrasco-Enríquez, Brenda; Barrios-Sánchez, Karina; Pérez-Escamilla, Rafael; Melgar-Quiñónez, Hugo

2014-01-01

This article validates the statistical consistency of two food security scales: the Mexican Food Security Scale (EMSA) and the Latin American and Caribbean Food Security Scale (ELCSA). Validity tests were conducted in order to verify that both scales were consistent instruments, conformed by independent, properly calibrated and adequately sorted items, arranged in a continuum of severity. The following tests were developed: sorting of items; Cronbach's alpha analysis; parallelism of prevalence curves; Rasch models; sensitivity analysis through mean differences' hypothesis test. The tests showed that both scales meet the required attributes and are robust statistical instruments for food security measurement. This is relevant given that the lack of access to food indicator, included in multidimensional poverty measurement in Mexico, is calculated with EMSA.

Shift work, long working hours and preterm birth: a systematic review and meta-analysis.

PubMed

van Melick, M J G J; van Beukering, M D M; Mol, B W; Frings-Dresen, M H W; Hulshof, C T J

2014-11-01

Specific physical activities or working conditions are suspected for increasing the risk of preterm birth (PTB). The aim of this meta-analysis is to review and summarize the pre-existing evidence on the effect of shift work or long working hours on the risk of PTB. We conducted a systematic search in MEDLINE and EMBASE (1990-2013) for observational and intervention studies with original data. We only included articles that met our specific criteria for language, exposure, outcome, data collection and original data that were of at least of moderate quality. The data of the included studies were pooled. Eight high-quality studies and eight moderate-quality studies were included in the meta-analysis. In these studies, no clear or statistically significant relationship between shift work and PTB was found. The summary estimate OR for performing shift work during pregnancy and the risk of PTB were 1.04 (95% CI 0.90-1.20). For long working hours during pregnancy, the summary estimate OR was 1.25 (95% CI 1.01-1.54), indicating a marginally statistically significant relationship but an only slightly elevated risk. Although in many of the included studies a positive association between long working hours and PTB was seen this did reach only marginal statistical significance. In the studies included in this review, working in shifts or in night shifts during pregnancy was not significantly associated with an increased risk for PTB. For both risk factors, due to the lack of high-quality studies focusing on the risks per trimester, in particular the third trimester, a firm conclusion about an association cannot be stated.

Trends in selected streamflow statistics at 19 long-term streamflow-gaging stations indicative of outflows from Texas to Arkansas, Louisiana, Galveston Bay, and the Gulf of Mexico, 1922-2009

USGS Publications Warehouse

Barbie, Dana L.; Wehmeyer, Loren L.

2012-01-01

Trends in selected streamflow statistics during 1922-2009 were evaluated at 19 long-term streamflow-gaging stations considered indicative of outflows from Texas to Arkansas, Louisiana, Galveston Bay, and the Gulf of Mexico. The U.S. Geological Survey, in cooperation with the Texas Water Development Board, evaluated streamflow data from streamflow-gaging stations with more than 50 years of record that were active as of 2009. The outflows into Arkansas and Louisiana were represented by 3 streamflow-gaging stations, and outflows into the Gulf of Mexico, including Galveston Bay, were represented by 16 streamflow-gaging stations. Monotonic trend analyses were done using the following three streamflow statistics generated from daily mean values of streamflow: (1) annual mean daily discharge, (2) annual maximum daily discharge, and (3) annual minimum daily discharge. The trend analyses were based on the nonparametric Kendall's Tau test, which is useful for the detection of monotonic upward or downward trends with time. A total of 69 trend analyses by Kendall's Tau were computed - 19 periods of streamflow multiplied by the 3 streamflow statistics plus 12 additional trend analyses because the periods of record for 2 streamflow-gaging stations were divided into periods representing pre- and post-reservoir impoundment. Unless otherwise described, each trend analysis used the entire period of record for each streamflow-gaging station. The monotonic trend analysis detected 11 statistically significant downward trends, 37 instances of no trend, and 21 statistically significant upward trends. One general region studied, which seemingly has relatively more upward trends for many of the streamflow statistics analyzed, includes the rivers and associated creeks and bayous to Galveston Bay in the Houston metropolitan area. Lastly, the most western river basins considered (the Nueces and Rio Grande) had statistically significant downward trends for many of the streamflow statistics analyzed.

A retrospective analysis of hyperthermic intraperitoneal chemotherapy for gastric cancer with peritoneal metastasis

PubMed Central

Yuan, Meiqin; Wang, Zeng; Hu, Guinv; Yang, Yunshan; Lv, Wangxia; Lu, Fangxiao; Zhong, Haijun

2016-01-01

Peritoneal metastasis (PM) is a poor prognostic factor in patients with gastric cancer. The aim of this study was to evaluate the efficacy and safety of hyperthermic intraperitoneal chemotherapy (HIPEC) in patients with advanced gastric cancer with PM by retrospective analysis. A total of 54 gastric cancer patients with positive ascitic fluid cytology were included in this study: 23 patients were treated with systemic chemotherapy combined with HIPEC (HIPEC+ group) and 31 received systemic chemotherapy alone (HIPEC- group). The patients were divided into 4 categories according to the changes of ascites, namely disappear, decrease, stable and increase. The disappear + decrease rate in the HIPEC+ group was 82.60%, which was statistically significantly superior to that of the HIPEC- group (54.80%). The disappear + decrease + stable rate was 95.70% in the HIPEC+ group and 74.20% in the HIPEC- group, but the difference was not statistically significant. In 33 patients with complete survival data, including 12 from the HIPEC+ and 21 from the HIPEC- group, the median progression-free survival was 164 and 129 days, respectively, and the median overall survival (OS) was 494 and 223 days, respectively. In patients with ascites disappear/decrease/stable, the OS appeared to be better compared with that in patients with ascites increase, but the difference was not statistically significant. Further analysis revealed that patients with controlled disease (complete response + partial response + stable disease) may have a better OS compared with patients with progressive disease, with a statistically significant difference. The toxicities were well tolerated in both groups. Therefore, HIPEC was found to improve survival in advanced gastric cancer patients with PM, but the difference was not statistically significant, which may be attributed to the small number of cases. Further studies with larger samples are required to confirm our data. PMID:27446587

A statistical simulation model for field testing of non-target organisms in environmental risk assessment of genetically modified plants.

PubMed

Goedhart, Paul W; van der Voet, Hilko; Baldacchino, Ferdinando; Arpaia, Salvatore

2014-04-01

Genetic modification of plants may result in unintended effects causing potentially adverse effects on the environment. A comparative safety assessment is therefore required by authorities, such as the European Food Safety Authority, in which the genetically modified plant is compared with its conventional counterpart. Part of the environmental risk assessment is a comparative field experiment in which the effect on non-target organisms is compared. Statistical analysis of such trials come in two flavors: difference testing and equivalence testing. It is important to know the statistical properties of these, for example, the power to detect environmental change of a given magnitude, before the start of an experiment. Such prospective power analysis can best be studied by means of a statistical simulation model. This paper describes a general framework for simulating data typically encountered in environmental risk assessment of genetically modified plants. The simulation model, available as Supplementary Material, can be used to generate count data having different statistical distributions possibly with excess-zeros. In addition the model employs completely randomized or randomized block experiments, can be used to simulate single or multiple trials across environments, enables genotype by environment interaction by adding random variety effects, and finally includes repeated measures in time following a constant, linear or quadratic pattern in time possibly with some form of autocorrelation. The model also allows to add a set of reference varieties to the GM plants and its comparator to assess the natural variation which can then be used to set limits of concern for equivalence testing. The different count distributions are described in some detail and some examples of how to use the simulation model to study various aspects, including a prospective power analysis, are provided.

A statistical simulation model for field testing of non-target organisms in environmental risk assessment of genetically modified plants

PubMed Central

Goedhart, Paul W; van der Voet, Hilko; Baldacchino, Ferdinando; Arpaia, Salvatore

2014-01-01

Genetic modification of plants may result in unintended effects causing potentially adverse effects on the environment. A comparative safety assessment is therefore required by authorities, such as the European Food Safety Authority, in which the genetically modified plant is compared with its conventional counterpart. Part of the environmental risk assessment is a comparative field experiment in which the effect on non-target organisms is compared. Statistical analysis of such trials come in two flavors: difference testing and equivalence testing. It is important to know the statistical properties of these, for example, the power to detect environmental change of a given magnitude, before the start of an experiment. Such prospective power analysis can best be studied by means of a statistical simulation model. This paper describes a general framework for simulating data typically encountered in environmental risk assessment of genetically modified plants. The simulation model, available as Supplementary Material, can be used to generate count data having different statistical distributions possibly with excess-zeros. In addition the model employs completely randomized or randomized block experiments, can be used to simulate single or multiple trials across environments, enables genotype by environment interaction by adding random variety effects, and finally includes repeated measures in time following a constant, linear or quadratic pattern in time possibly with some form of autocorrelation. The model also allows to add a set of reference varieties to the GM plants and its comparator to assess the natural variation which can then be used to set limits of concern for equivalence testing. The different count distributions are described in some detail and some examples of how to use the simulation model to study various aspects, including a prospective power analysis, are provided. PMID:24834325

The Spreadsheet in an Educational Setting. Microcomputing Working Paper Series F 84-4.

ERIC Educational Resources Information Center

Wozny, Lucy

This overview of a specific spreadsheet, Microsoft's Multiplan for the Apple Macintosh microcomputer, emphasizes specific features that are important to the academic community, including the mathematical functions of algebra, trigonometry, and statistical analysis. Additional features are summarized, including data formats for both numerical and…

Quantitative Microbial Risk Assessment Tutorial: Publishing a Microbial Density Time Series as a Txt File

EPA Science Inventory

A SARA Timeseries Utility supports analysis and management of time-varying environmental data including listing, graphing, computing statistics, computing meteorological data and saving in a WDM or text file. File formats supported include WDM, HSPF Binary (.hbn), USGS RDB, and T...

Johnson Space Center's Risk and Reliability Analysis Group 2008 Annual Report

NASA Technical Reports Server (NTRS)

Valentine, Mark; Boyer, Roger; Cross, Bob; Hamlin, Teri; Roelant, Henk; Stewart, Mike; Bigler, Mark; Winter, Scott; Reistle, Bruce; Heydorn,Dick

2009-01-01

The Johnson Space Center (JSC) Safety & Mission Assurance (S&MA) Directorate s Risk and Reliability Analysis Group provides both mathematical and engineering analysis expertise in the areas of Probabilistic Risk Assessment (PRA), Reliability and Maintainability (R&M) analysis, and data collection and analysis. The fundamental goal of this group is to provide National Aeronautics and Space Administration (NASA) decisionmakers with the necessary information to make informed decisions when evaluating personnel, flight hardware, and public safety concerns associated with current operating systems as well as with any future systems. The Analysis Group includes a staff of statistical and reliability experts with valuable backgrounds in the statistical, reliability, and engineering fields. This group includes JSC S&MA Analysis Branch personnel as well as S&MA support services contractors, such as Science Applications International Corporation (SAIC) and SoHaR. The Analysis Group s experience base includes nuclear power (both commercial and navy), manufacturing, Department of Defense, chemical, and shipping industries, as well as significant aerospace experience specifically in the Shuttle, International Space Station (ISS), and Constellation Programs. The Analysis Group partners with project and program offices, other NASA centers, NASA contractors, and universities to provide additional resources or information to the group when performing various analysis tasks. The JSC S&MA Analysis Group is recognized as a leader in risk and reliability analysis within the NASA community. Therefore, the Analysis Group is in high demand to help the Space Shuttle Program (SSP) continue to fly safely, assist in designing the next generation spacecraft for the Constellation Program (CxP), and promote advanced analytical techniques. The Analysis Section s tasks include teaching classes and instituting personnel qualification processes to enhance the professional abilities of our analysts as well as performing major probabilistic assessments used to support flight rationale and help establish program requirements. During 2008, the Analysis Group performed more than 70 assessments. Although all these assessments were important, some were instrumental in the decisionmaking processes for the Shuttle and Constellation Programs. Two of the more significant tasks were the Space Transportation System (STS)-122 Low Level Cutoff PRA for the SSP and the Orion Pad Abort One (PA-1) PRA for the CxP. These two activities, along with the numerous other tasks the Analysis Group performed in 2008, are summarized in this report. This report also highlights several ongoing and upcoming efforts to provide crucial statistical and probabilistic assessments, such as the Extravehicular Activity (EVA) PRA for the Hubble Space Telescope service mission and the first fully integrated PRAs for the CxP's Lunar Sortie and ISS missions.

System analysis for the Huntsville Operational Support Center distributed computer system

NASA Technical Reports Server (NTRS)

Ingels, E. M.

1983-01-01

A simulation model was developed and programmed in three languages BASIC, PASCAL, and SLAM. Two of the programs are included in this report, the BASIC and the PASCAL language programs. SLAM is not supported by NASA/MSFC facilities and hence was not included. The statistical comparison of simulations of the same HOSC system configurations are in good agreement and are in agreement with the operational statistics of HOSC that were obtained. Three variations of the most recent HOSC configuration was run and some conclusions drawn as to the system performance under these variations.

Job Satisfaction DEOCS 4.1 Construct Validity Summary

DTIC Science & Technology

2017-08-01

focuses more specifically on satisfaction with the job. Included is a review of the 4.0 description and items, followed by the proposed modifications to...the factor. The DEOCS 4.0 description provided for job satisfaction is “the perception of personal fulfillment in a specific vocation, and sense of...piloting items on the DEOCS; (4) examining the descriptive statistics, exploratory factor analysis results, and aggregation statistics; and (5

The Statistical Analysis Techniques to Support the NGNP Fuel Performance Experiments

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

Bihn T. Pham; Jeffrey J. Einerson

2010-06-01

This paper describes the development and application of statistical analysis techniques to support the AGR experimental program on NGNP fuel performance. The experiments conducted in the Idaho National Laboratory’s Advanced Test Reactor employ fuel compacts placed in a graphite cylinder shrouded by a steel capsule. The tests are instrumented with thermocouples embedded in graphite blocks and the target quantity (fuel/graphite temperature) is regulated by the He-Ne gas mixture that fills the gap volume. Three techniques for statistical analysis, namely control charting, correlation analysis, and regression analysis, are implemented in the SAS-based NGNP Data Management and Analysis System (NDMAS) for automatedmore » processing and qualification of the AGR measured data. The NDMAS also stores daily neutronic (power) and thermal (heat transfer) code simulation results along with the measurement data, allowing for their combined use and comparative scrutiny. The ultimate objective of this work includes (a) a multi-faceted system for data monitoring and data accuracy testing, (b) identification of possible modes of diagnostics deterioration and changes in experimental conditions, (c) qualification of data for use in code validation, and (d) identification and use of data trends to support effective control of test conditions with respect to the test target. Analysis results and examples given in the paper show the three statistical analysis techniques providing a complementary capability to warn of thermocouple failures. It also suggests that the regression analysis models relating calculated fuel temperatures and thermocouple readings can enable online regulation of experimental parameters (i.e. gas mixture content), to effectively maintain the target quantity (fuel temperature) within a given range.« less

Dietary Inflammatory Potential Score and Risk of Breast Cancer: Systematic Review and Meta-analysis.

PubMed

Zahedi, Hoda; Djalalinia, Shirin; Sadeghi, Omid; Asayesh, Hamid; Noroozi, Mehdi; Gorabi, Armita Mahdavi; Mohammadi, Rasool; Qorbani, Mostafa

2018-02-07

Several studies have been conducted on the relationship between dietary inflammatory potential (DIP) and breast cancer. However, the findings are conflicting. This systematic review and meta-analysis summarizes the findings on the association between DIP and the risk of breast cancer. We used relevant keywords and searched online international electronic databases, including PubMed and NLM Gateway (for Medline), Institute for Scientific Information (ISI), and Scopus for articles published through February 2017. All cross-sectional, case-control, and cohort studies were included in this meta-analysis. Meta-analysis was performed using the random effects meta-analysis method to address heterogeneity among studies. Findings were analyzed statistically. Nine studies were included in the present systematic review and meta-analysis. The total sample size of these studies was 296,102, and the number of participants varied from 1453 to 122,788. The random effects meta-analysis showed a positive and significant association between DIP and the risk of breast cancer (pooled odds ratio, 1.14; 95% confidence interval, 1.01-1.27). The pooled effect size was not statistically significant because of the type of studies, including cohort (pooled relative risk, 1.04; 95% confidence interval, 0.98-1.10) and case-control (pooled odds ratio, 1.63; 95% confidence interval, 0.89-2.37) studies. We found a significant and positive association between higher DIP score and risk of breast cancer. Modifying inflammatory characteristics of diet can substantially reduce the risk of breast cancer. Copyright © 2018 Elsevier Inc. All rights reserved.

Machine learning to analyze images of shocked materials for precise and accurate measurements

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

Dresselhaus-Cooper, Leora; Howard, Marylesa; Hock, Margaret C.

A supervised machine learning algorithm, called locally adaptive discriminant analysis (LADA), has been developed to locate boundaries between identifiable image features that have varying intensities. LADA is an adaptation of image segmentation, which includes techniques that find the positions of image features (classes) using statistical intensity distributions for each class in the image. In order to place a pixel in the proper class, LADA considers the intensity at that pixel and the distribution of intensities in local (nearby) pixels. This paper presents the use of LADA to provide, with statistical uncertainties, the positions and shapes of features within ultrafast imagesmore » of shock waves. We demonstrate the ability to locate image features including crystals, density changes associated with shock waves, and material jetting caused by shock waves. This algorithm can analyze images that exhibit a wide range of physical phenomena because it does not rely on comparison to a model. LADA enables analysis of images from shock physics with statistical rigor independent of underlying models or simulations.« less

Exploring the Link Between Streamflow Trends and Climate Change in Indiana, USA

NASA Astrophysics Data System (ADS)

Kumar, S.; Kam, J.; Thurner, K.; Merwade, V.

2007-12-01

Streamflow trends in Indiana are evaluated for 85 USGS streamflow gaging stations that have continuous unregulated streamflow records varying from 10 to 80 years. The trends are analyzed by using the non-parametric Mann-Kendall test with prior trend-free pre-whitening to remove serial correlation in the data. Bootstrap method is used to establish field significance of the results. Trends are computed for 12 streamflow statistics to include low-, medium- (median and mean flow), and high-flow conditions on annual and seasonal time step. The analysis is done for six study periods, ranging from 10 years to more than 65 years, all ending in 2003. The trends in annual average streamflow, for 50 years study period, are compared with annual average precipitation trends from 14 National Climatic Data Center (NCDC) stations in Indiana, that have 50 years of continuous daily record. The results show field significant positive trends in annual low and medium streamflow statistics at majority of gaging stations for study periods that include 40 or more years of records. In seasonal analysis, all flow statistics in summer and fall (low flow seasons), and only low flow statistics in winter and spring (high flow seasons) are showing positive trends. No field significant trends in annual and seasonal flow statistics are observed for study periods that include 25 or fewer years of records, except for northern Indiana where localized negative trends are observed in 10 and 15 years study periods. Further, stream flow trends are found to be highly correlated with precipitation trends on annual time step. No apparent climate change signal is observed in Indiana stream flow records.

Bayesian test for colocalisation between pairs of genetic association studies using summary statistics.

PubMed

Giambartolomei, Claudia; Vukcevic, Damjan; Schadt, Eric E; Franke, Lude; Hingorani, Aroon D; Wallace, Chris; Plagnol, Vincent

2014-05-01

Genetic association studies, in particular the genome-wide association study (GWAS) design, have provided a wealth of novel insights into the aetiology of a wide range of human diseases and traits, in particular cardiovascular diseases and lipid biomarkers. The next challenge consists of understanding the molecular basis of these associations. The integration of multiple association datasets, including gene expression datasets, can contribute to this goal. We have developed a novel statistical methodology to assess whether two association signals are consistent with a shared causal variant. An application is the integration of disease scans with expression quantitative trait locus (eQTL) studies, but any pair of GWAS datasets can be integrated in this framework. We demonstrate the value of the approach by re-analysing a gene expression dataset in 966 liver samples with a published meta-analysis of lipid traits including >100,000 individuals of European ancestry. Combining all lipid biomarkers, our re-analysis supported 26 out of 38 reported colocalisation results with eQTLs and identified 14 new colocalisation results, hence highlighting the value of a formal statistical test. In three cases of reported eQTL-lipid pairs (SYPL2, IFT172, TBKBP1) for which our analysis suggests that the eQTL pattern is not consistent with the lipid association, we identify alternative colocalisation results with SORT1, GCKR, and KPNB1, indicating that these genes are more likely to be causal in these genomic intervals. A key feature of the method is the ability to derive the output statistics from single SNP summary statistics, hence making it possible to perform systematic meta-analysis type comparisons across multiple GWAS datasets (implemented online at http://coloc.cs.ucl.ac.uk/coloc/). Our methodology provides information about candidate causal genes in associated intervals and has direct implications for the understanding of complex diseases as well as the design of drugs to target disease pathways.

Single-row, double-row, and transosseous equivalent techniques for isolated supraspinatus tendon tears with minimal atrophy: A retrospective comparative outcome and radiographic analysis at minimum 2-year followup

PubMed Central

McCormick, Frank; Gupta, Anil; Bruce, Ben; Harris, Josh; Abrams, Geoff; Wilson, Hillary; Hussey, Kristen; Cole, Brian J.

2014-01-01

Purpose: The purpose of this study was to measure and compare the subjective, objective, and radiographic healing outcomes of single-row (SR), double-row (DR), and transosseous equivalent (TOE) suture techniques for arthroscopic rotator cuff repair. Materials and Methods: A retrospective comparative analysis of arthroscopic rotator cuff repairs by one surgeon from 2004 to 2010 at minimum 2-year followup was performed. Cohorts were matched for age, sex, and tear size. Subjective outcome variables included ASES, Constant, SST, UCLA, and SF-12 scores. Objective outcome variables included strength, active range of motion (ROM). Radiographic healing was assessed by magnetic resonance imaging (MRI). Statistical analysis was performed using analysis of variance (ANOVA), Mann — Whitney and Kruskal — Wallis tests with significance, and the Fisher exact probability test <0.05. Results: Sixty-three patients completed the study requirements (20 SR, 21 DR, 22 TOE). There was a clinically and statistically significant improvement in outcomes with all repair techniques (ASES mean improvement P = <0.0001). The mean final ASES scores were: SR 83; (SD 21.4); DR 87 (SD 18.2); TOE 87 (SD 13.2); (P = 0.73). There was a statistically significant improvement in strength for each repair technique (P < 0.001). There was no significant difference between techniques across all secondary outcome assessments: ASES improvement, Constant, SST, UCLA, SF-12, ROM, Strength, and MRI re-tear rates. There was a decrease in re-tear rates from single row (22%) to double-row (18%) to transosseous equivalent (11%); however, this difference was not statistically significant (P = 0.6). Conclusions: Compared to preoperatively, arthroscopic rotator cuff repair, using SR, DR, or TOE techniques, yielded a clinically and statistically significant improvement in subjective and objective outcomes at a minimum 2-year follow-up. Level of Evidence: Therapeutic level 3. PMID:24926159

Conducting Simulation Studies in the R Programming Environment.

PubMed

Hallgren, Kevin A

2013-10-12

Simulation studies allow researchers to answer specific questions about data analysis, statistical power, and best-practices for obtaining accurate results in empirical research. Despite the benefits that simulation research can provide, many researchers are unfamiliar with available tools for conducting their own simulation studies. The use of simulation studies need not be restricted to researchers with advanced skills in statistics and computer programming, and such methods can be implemented by researchers with a variety of abilities and interests. The present paper provides an introduction to methods used for running simulation studies using the R statistical programming environment and is written for individuals with minimal experience running simulation studies or using R. The paper describes the rationale and benefits of using simulations and introduces R functions relevant for many simulation studies. Three examples illustrate different applications for simulation studies, including (a) the use of simulations to answer a novel question about statistical analysis, (b) the use of simulations to estimate statistical power, and (c) the use of simulations to obtain confidence intervals of parameter estimates through bootstrapping. Results and fully annotated syntax from these examples are provided.

Phenotypic mapping of metabolic profiles using self-organizing maps of high-dimensional mass spectrometry data.

PubMed

Goodwin, Cody R; Sherrod, Stacy D; Marasco, Christina C; Bachmann, Brian O; Schramm-Sapyta, Nicole; Wikswo, John P; McLean, John A

2014-07-01

A metabolic system is composed of inherently interconnected metabolic precursors, intermediates, and products. The analysis of untargeted metabolomics data has conventionally been performed through the use of comparative statistics or multivariate statistical analysis-based approaches; however, each falls short in representing the related nature of metabolic perturbations. Herein, we describe a complementary method for the analysis of large metabolite inventories using a data-driven approach based upon a self-organizing map algorithm. This workflow allows for the unsupervised clustering, and subsequent prioritization of, correlated features through Gestalt comparisons of metabolic heat maps. We describe this methodology in detail, including a comparison to conventional metabolomics approaches, and demonstrate the application of this method to the analysis of the metabolic repercussions of prolonged cocaine exposure in rat sera profiles.

Potential errors and misuse of statistics in studies on leakage in endodontics.

PubMed

Lucena, C; Lopez, J M; Pulgar, R; Abalos, C; Valderrama, M J

2013-04-01

To assess the quality of the statistical methodology used in studies of leakage in Endodontics, and to compare the results found using appropriate versus inappropriate inferential statistical methods. The search strategy used the descriptors 'root filling' 'microleakage', 'dye penetration', 'dye leakage', 'polymicrobial leakage' and 'fluid filtration' for the time interval 2001-2010 in journals within the categories 'Dentistry, Oral Surgery and Medicine' and 'Materials Science, Biomaterials' of the Journal Citation Report. All retrieved articles were reviewed to find potential pitfalls in statistical methodology that may be encountered during study design, data management or data analysis. The database included 209 papers. In all the studies reviewed, the statistical methods used were appropriate for the category attributed to the outcome variable, but in 41% of the cases, the chi-square test or parametric methods were inappropriately selected subsequently. In 2% of the papers, no statistical test was used. In 99% of cases, a statistically 'significant' or 'not significant' effect was reported as a main finding, whilst only 1% also presented an estimation of the magnitude of the effect. When the appropriate statistical methods were applied in the studies with originally inappropriate data analysis, the conclusions changed in 19% of the cases. Statistical deficiencies in leakage studies may affect their results and interpretation and might be one of the reasons for the poor agreement amongst the reported findings. Therefore, more effort should be made to standardize statistical methodology. © 2012 International Endodontic Journal.

eSACP - a new Nordic initiative towards developing statistical climate services

NASA Astrophysics Data System (ADS)

Thorarinsdottir, Thordis; Thejll, Peter; Drews, Martin; Guttorp, Peter; Venälainen, Ari; Uotila, Petteri; Benestad, Rasmus; Mesquita, Michel d. S.; Madsen, Henrik; Fox Maule, Cathrine

2015-04-01

The Nordic research council NordForsk has recently announced its support for a new 3-year research initiative on "statistical analysis of climate projections" (eSACP). eSACP will focus on developing e-science tools and services based on statistical analysis of climate projections for the purpose of helping decision-makers and planners in the face of expected future challenges in regional climate change. The motivation behind the project is the growing recognition in our society that forecasts of future climate change is associated with various sources of uncertainty, and that any long-term planning and decision-making dependent on a changing climate must account for this. At the same time there is an obvious gap between scientists from different fields and between practitioners in terms of understanding how climate information relates to different parts of the "uncertainty cascade". In eSACP we will develop generic e-science tools and statistical climate services to facilitate the use of climate projections by decision-makers and scientists from all fields for climate impact analyses and for the development of robust adaptation strategies, which properly (in a statistical sense) account for the inherent uncertainty. The new tool will be publically available and include functionality to utilize the extensive and dynamically growing repositories of data and use state-of-the-art statistical techniques to quantify the uncertainty and innovative approaches to visualize the results. Such a tool will not only be valuable for future assessments and underpin the development of dedicated climate services, but will also assist the scientific community in making more clearly its case on the consequences of our changing climate to policy makers and the general public. The eSACP project is led by Thordis Thorarinsdottir, Norwegian Computing Center, and also includes the Finnish Meteorological Institute, the Norwegian Meteorological Institute, the Technical University of Denmark and the Bjerknes Centre for Climate Research, Norway. This poster will present details of focus areas in the project and show some examples of the expected analysis tools.

An analysis of science versus pseudoscience

NASA Astrophysics Data System (ADS)

Hooten, James T.

2011-12-01

This quantitative study identified distinctive features in archival datasets commissioned by the National Science Foundation (NSF) for Science and Engineering Indicators reports. The dependent variables included education level, and scores for science fact knowledge, science process knowledge, and pseudoscience beliefs. The dependent variables were aggregated into nine NSF-defined geographic regions and examined for the years 2004 and 2006. The variables were also examined over all years available in the dataset. Descriptive statistics were determined and tests for normality and homogeneity of variances were performed using Statistical Package for the Social Sciences. Analysis of Variance was used to test for statistically significant differences between the nine geographic regions for each of the four dependent variables. Statistical significance of 0.05 was used. Tukey post-hoc analysis was used to compute practical significance of differences between regions. Post-hoc power analysis using G*Power was used to calculate the probability of Type II errors. Tests for correlations across all years of the dependent variables were also performed. Pearson's r was used to indicate the strength of the relationship between the dependent variables. Small to medium differences in science literacy and education level were observed between many of the nine U.S. geographic regions. The most significant differences occurred when the West South Central region was compared to the New England and the Pacific regions. Belief in pseudoscience appeared to be distributed evenly across all U.S. geographic regions. Education level was a strong indicator of science literacy regardless of a respondent's region of residence. Recommendations for further study include more in-depth investigation to uncover the nature of the relationship between education level and belief in pseudoscience.

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

Apte, A; Veeraraghavan, H; Oh, J

Purpose: To present an open source and free platform to facilitate radiomics research — The “Radiomics toolbox” in CERR. Method: There is scarcity of open source tools that support end-to-end modeling of image features to predict patient outcomes. The “Radiomics toolbox” strives to fill the need for such a software platform. The platform supports (1) import of various kinds of image modalities like CT, PET, MR, SPECT, US. (2) Contouring tools to delineate structures of interest. (3) Extraction and storage of image based features like 1st order statistics, gray-scale co-occurrence and zonesize matrix based texture features and shape features andmore » (4) Statistical Analysis. Statistical analysis of the extracted features is supported with basic functionality that includes univariate correlations, Kaplan-Meir curves and advanced functionality that includes feature reduction and multivariate modeling. The graphical user interface and the data management are performed with Matlab for the ease of development and readability of code and features for wide audience. Open-source software developed with other programming languages is integrated to enhance various components of this toolbox. For example: Java-based DCM4CHE for import of DICOM, R for statistical analysis. Results: The Radiomics toolbox will be distributed as an open source, GNU copyrighted software. The toolbox was prototyped for modeling Oropharyngeal PET dataset at MSKCC. The analysis will be presented in a separate paper. Conclusion: The Radiomics Toolbox provides an extensible platform for extracting and modeling image features. To emphasize new uses of CERR for radiomics and image-based research, we have changed the name from the “Computational Environment for Radiotherapy Research” to the “Computational Environment for Radiological Research”.« less

Ground-Based Telescope Parametric Cost Model

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Rowell, Ginger Holmes

2004-01-01

A parametric cost model for ground-based telescopes is developed using multi-variable statistical analysis, The model includes both engineering and performance parameters. While diameter continues to be the dominant cost driver, other significant factors include primary mirror radius of curvature and diffraction limited wavelength. The model includes an explicit factor for primary mirror segmentation and/or duplication (i.e.. multi-telescope phased-array systems). Additionally, single variable models based on aperture diameter are derived. This analysis indicates that recent mirror technology advances have indeed reduced the historical telescope cost curve.

Assigning statistical significance to proteotypic peptides via database searches

PubMed Central

Alves, Gelio; Ogurtsov, Aleksey Y.; Yu, Yi-Kuo

2011-01-01

Querying MS/MS spectra against a database containing only proteotypic peptides reduces data analysis time due to reduction of database size. Despite the speed advantage, this search strategy is challenged by issues of statistical significance and coverage. The former requires separating systematically significant identifications from less confident identifications, while the latter arises when the underlying peptide is not present, due to single amino acid polymorphisms (SAPs) or post-translational modifications (PTMs), in the proteotypic peptide libraries searched. To address both issues simultaneously, we have extended RAId’s knowledge database to include proteotypic information, utilized RAId’s statistical strategy to assign statistical significance to proteotypic peptides, and modified RAId’s programs to allow for consideration of proteotypic information during database searches. The extended database alleviates the coverage problem since all annotated modifications, even those occurred within proteotypic peptides, may be considered. Taking into account the likelihoods of observation, the statistical strategy of RAId provides accurate E-value assignments regardless whether a candidate peptide is proteotypic or not. The advantage of including proteotypic information is evidenced by its superior retrieval performance when compared to regular database searches. PMID:21055489

Parasites as valuable stock markers for fisheries in Australasia, East Asia and the Pacific Islands.

PubMed

Lester, R J G; Moore, B R

2015-01-01

Over 30 studies in Australasia, East Asia and the Pacific Islands region have collected and analysed parasite data to determine the ranges of individual fish, many leading to conclusions about stock delineation. Parasites used as biological tags have included both those known to have long residence times in the fish and those thought to be relatively transient. In many cases the parasitological conclusions have been supported by other methods especially analysis of the chemical constituents of otoliths, and to a lesser extent, genetic data. In analysing parasite data, authors have applied multiple different statistical methodologies, including summary statistics, and univariate and multivariate approaches. Recently, a growing number of researchers have found non-parametric methods, such as analysis of similarities and cluster analysis, to be valuable. Future studies into the residence times, life cycles and geographical distributions of parasites together with more robust analytical methods will yield much important information to clarify stock structures in the area.

Effects of Instructional Design with Mental Model Analysis on Learning.

ERIC Educational Resources Information Center

Hong, Eunsook

This paper presents a model for systematic instructional design that includes mental model analysis together with the procedures used in developing computer-based instructional materials in the area of statistical hypothesis testing. The instructional design model is based on the premise that the objective for learning is to achieve expert-like…

An Investigation of Civilians Preparedness to Compete with Individuals with Military Experience for Army Board Select Acquisition Positions

DTIC Science & Technology

2017-05-25

37 Research Design ... research employed a mixed research methodology – quantitative with descriptive statistical analysis and qualitative with a thematic analysis approach...mixed research methodology – quantitative and qualitative, using interviews to collect the data. The interviews included demographic and open-ended

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…

The Effects of Ability Grouping: A Meta-Analysis of Research Findings.

ERIC Educational Resources Information Center

Noland, Theresa Koontz; Taylor, Bob L.

The study reported in this paper quantitatively integrated the recent research findings on ability grouping in order to generalize about these effects on student achievement and student self-concept. Meta-analysis was used to statistically integrate the empirical data. The relationships among various experimental variables including grade level,…

Principal Component Analysis: Resources for an Essential Application of Linear Algebra

ERIC Educational Resources Information Center

Pankavich, Stephen; Swanson, Rebecca

2015-01-01

Principal Component Analysis (PCA) is a highly useful topic within an introductory Linear Algebra course, especially since it can be used to incorporate a number of applied projects. This method represents an essential application and extension of the Spectral Theorem and is commonly used within a variety of fields, including statistics,…

A Program Evaluation of a Summer Research Training Institute for American Indian and Alaska Native Health Professionals

ERIC Educational Resources Information Center

Zaback, Tosha; Becker, Thomas M.; Dignan, Mark B.; Lambert, William E.

2010-01-01

In this article, the authors describe a unique summer program to train American Indian/Alaska Native (AI/AN) health professionals in a variety of health research-related skills, including epidemiology, data management, statistical analysis, program evaluation, cost-benefit analysis, community-based participatory research, grant writing, and…

A Meta-Analysis: The Relationship between Father Involvement and Student Academic Achievement

ERIC Educational Resources Information Center

Jeynes, William H.

2015-01-01

A meta-analysis was undertaken, including 66 studies, to determine the relationship between father involvement and the educational outcomes of urban school children. Statistical analyses were done to determine the overall impact and specific components of father involvement. The possible differing effects of paternal involvement by race were also…

A Meta-Analysis of Writing Instruction for Students in the Elementary Grades

ERIC Educational Resources Information Center

Graham, Steve; McKeown, Debra; Kiuhara, Sharlene; Harris, Karen R.

2012-01-01

In an effort to identify effective instructional practices for teaching writing to elementary grade students, we conducted a meta-analysis of the writing intervention literature, focusing our efforts on true and quasi-experiments. We located 115 documents that included the statistics for computing an effect size (ES). We calculated an average…

QPROT: Statistical method for testing differential expression using protein-level intensity data in label-free quantitative proteomics.

PubMed

Choi, Hyungwon; Kim, Sinae; Fermin, Damian; Tsou, Chih-Chiang; Nesvizhskii, Alexey I

2015-11-03

We introduce QPROT, a statistical framework and computational tool for differential protein expression analysis using protein intensity data. QPROT is an extension of the QSPEC suite, originally developed for spectral count data, adapted for the analysis using continuously measured protein-level intensity data. QPROT offers a new intensity normalization procedure and model-based differential expression analysis, both of which account for missing data. Determination of differential expression of each protein is based on the standardized Z-statistic based on the posterior distribution of the log fold change parameter, guided by the false discovery rate estimated by a well-known Empirical Bayes method. We evaluated the classification performance of QPROT using the quantification calibration data from the clinical proteomic technology assessment for cancer (CPTAC) study and a recently published Escherichia coli benchmark dataset, with evaluation of FDR accuracy in the latter. QPROT is a statistical framework with computational software tool for comparative quantitative proteomics analysis. It features various extensions of QSPEC method originally built for spectral count data analysis, including probabilistic treatment of missing values in protein intensity data. With the increasing popularity of label-free quantitative proteomics data, the proposed method and accompanying software suite will be immediately useful for many proteomics laboratories. This article is part of a Special Issue entitled: Computational Proteomics. Copyright © 2015 Elsevier B.V. All rights reserved.

Lower back pain and absenteeism among professional public transport drivers.

PubMed

Kresal, Friderika; Roblek, Vasja; Jerman, Andrej; Meško, Maja

2015-01-01

Drivers in public transport are subjected to lower back pain. The reason for the pain is associated with the characteristics of the physical position imposed on the worker while performing the job. Lower back pain is the main cause of absenteeism among drivers. The present study includes 145 public transport drivers employed as professional drivers for an average of 14.14 years. Analysis of the data obtained in the study includes the basic descriptive statistics, χ(2) test and multiple regression analysis. Analysis of the incidence of lower back pain showed that the majority of our sample population suffered from pain in the lower back. We found that there are no statistically significant differences between the groups formed by the length of service as a professional driver and incidence of lower back pain; we were also interested in whether or not the risk factors of lower back pain affects the absenteeism of city bus drivers. Analysis of the data has shown that the risk factors of pain in the lower part of the spine do affect the absenteeism of city bus drivers.

Critical Analysis of Primary Literature in a Master’s-Level Class: Effects on Self-Efficacy and Science-Process Skills

PubMed Central

Abdullah, Christopher; Parris, Julian; Lie, Richard; Guzdar, Amy; Tour, Ella

2015-01-01

The ability to think analytically and creatively is crucial for success in the modern workforce, particularly for graduate students, who often aim to become physicians or researchers. Analysis of the primary literature provides an excellent opportunity to practice these skills. We describe a course that includes a structured analysis of four research papers from diverse fields of biology and group exercises in proposing experiments that would follow up on these papers. To facilitate a critical approach to primary literature, we included a paper with questionable data interpretation and two papers investigating the same biological question yet reaching opposite conclusions. We report a significant increase in students’ self-efficacy in analyzing data from research papers, evaluating authors’ conclusions, and designing experiments. Using our science-process skills test, we observe a statistically significant increase in students’ ability to propose an experiment that matches the goal of investigation. We also detect gains in interpretation of controls and quantitative analysis of data. No statistically significant changes were observed in questions that tested the skills of interpretation, inference, and evaluation. PMID:26250564

Selected papers in the hydrologic sciences, 1986

USGS Publications Warehouse

Subitzky, Seymour

1987-01-01

Water-quality data from long-term (24 years), fixed- station monitoring at the Cape Fear River at Lock 1 near Kelly, N.C., and various measures of basin development are correlated. Subbasin population, number of acres of cropland in the subbasin, number of people employed in manufacturing, and tons of fertilizer applied in the basin are considered as measures of basinwide development activity. Linear correlations show statistically significant posi- tive relations between both population and manufacturing activity and most of the dissolved constituents considered. Negative correlations were found between the acres of harvested cropland and most of the water-quality measures. The amount of fertilizer sold in the subbasin was not statistically related to the water-quality measures considered in this report. The statistical analysis was limited to several commonly used measures of water quality including specific conductance, pH, dissolved solids, several major dissolved ions, and a few nutrients. The major dissolved ions included in the analysis were calcium, sodium, potassium, magnesium, chloride, sulfate, silica, bicarbonate, and fluoride. The nutrients included were dissolved nitrite plus nitrate nitrogen, dissolved ammonia nitrogen, total nitrogen, dissolved phosphates, and total phosphorus. For the chemicals evaluated, manufacturing and population sources are more closely associated with water quality in the Cape Fear River at Lock 1 than are agricultural variables.

Statistical Surrogate Modeling of Atmospheric Dispersion Events Using Bayesian Adaptive Splines

NASA Astrophysics Data System (ADS)

Francom, D.; Sansó, B.; Bulaevskaya, V.; Lucas, D. D.

2016-12-01

Uncertainty in the inputs of complex computer models, including atmospheric dispersion and transport codes, is often assessed via statistical surrogate models. Surrogate models are computationally efficient statistical approximations of expensive computer models that enable uncertainty analysis. We introduce Bayesian adaptive spline methods for producing surrogate models that capture the major spatiotemporal patterns of the parent model, while satisfying all the necessities of flexibility, accuracy and computational feasibility. We present novel methodological and computational approaches motivated by a controlled atmospheric tracer release experiment conducted at the Diablo Canyon nuclear power plant in California. Traditional methods for building statistical surrogate models often do not scale well to experiments with large amounts of data. Our approach is well suited to experiments involving large numbers of model inputs, large numbers of simulations, and functional output for each simulation. Our approach allows us to perform global sensitivity analysis with ease. We also present an approach to calibration of simulators using field data.

Statistical analysis of NaOH pretreatment effects on sweet sorghum bagasse characteristics

NASA Astrophysics Data System (ADS)

Putri, Ary Mauliva Hada; Wahyuni, Eka Tri; Sudiyani, Yanni

2017-01-01

We analyze the behavior of sweet sorghum bagasse characteristics before and after NaOH pretreatments by statistical analysis. These characteristics include the percentages of lignocellulosic materials and the degree of crystallinity. We use the chi-square method to get the values of fitted parameters, and then deploy student's t-test to check whether they are significantly different from zero at 99.73% confidence level (C.L.). We obtain, in the cases of hemicellulose and lignin, that their percentages after pretreatment decrease statistically. On the other hand, crystallinity does not possess similar behavior as the data proves that all fitted parameters in this case might be consistent with zero. Our statistical result is then cross examined with the observations from X-ray diffraction (XRD) and Fourier Transform Infrared (FTIR) Spectroscopy, showing pretty good agreement. This result may indicate that the 10% NaOH pretreatment might not be sufficient in changing the crystallinity index of the sweet sorghum bagasse.

Hybrid statistics-simulations based method for atom-counting from ADF STEM images.

PubMed

De Wael, Annelies; De Backer, Annick; Jones, Lewys; Nellist, Peter D; Van Aert, Sandra

2017-06-01

A hybrid statistics-simulations based method for atom-counting from annular dark field scanning transmission electron microscopy (ADF STEM) images of monotype crystalline nanostructures is presented. Different atom-counting methods already exist for model-like systems. However, the increasing relevance of radiation damage in the study of nanostructures demands a method that allows atom-counting from low dose images with a low signal-to-noise ratio. Therefore, the hybrid method directly includes prior knowledge from image simulations into the existing statistics-based method for atom-counting, and accounts in this manner for possible discrepancies between actual and simulated experimental conditions. It is shown by means of simulations and experiments that this hybrid method outperforms the statistics-based method, especially for low electron doses and small nanoparticles. The analysis of a simulated low dose image of a small nanoparticle suggests that this method allows for far more reliable quantitative analysis of beam-sensitive materials. Copyright © 2017 Elsevier B.V. All rights reserved.

Cosmology constraints from shear peak statistics in Dark Energy Survey Science Verification data

NASA Astrophysics Data System (ADS)

Kacprzak, T.; Kirk, D.; Friedrich, O.; Amara, A.; Refregier, A.; Marian, L.; Dietrich, J. P.; Suchyta, E.; Aleksić, J.; Bacon, D.; Becker, M. R.; Bonnett, C.; Bridle, S. L.; Chang, C.; Eifler, T. F.; Hartley, W. G.; Huff, E. M.; Krause, E.; MacCrann, N.; Melchior, P.; Nicola, A.; Samuroff, S.; Sheldon, E.; Troxel, M. A.; Weller, J.; Zuntz, J.; Abbott, T. M. C.; Abdalla, F. B.; Armstrong, R.; Benoit-Lévy, A.; Bernstein, G. M.; Bernstein, R. A.; Bertin, E.; Brooks, D.; Burke, D. L.; Carnero Rosell, A.; Carrasco Kind, M.; Carretero, J.; Castander, F. J.; Crocce, M.; D'Andrea, C. B.; da Costa, L. N.; Desai, S.; Diehl, H. T.; Evrard, A. E.; Neto, A. Fausti; Flaugher, B.; Fosalba, P.; Frieman, J.; Gerdes, D. W.; Goldstein, D. A.; Gruen, D.; Gruendl, R. A.; Gutierrez, G.; Honscheid, K.; Jain, B.; James, D. J.; Jarvis, M.; Kuehn, K.; Kuropatkin, N.; Lahav, O.; Lima, M.; March, M.; Marshall, J. L.; Martini, P.; Miller, C. J.; Miquel, R.; Mohr, J. J.; Nichol, R. C.; Nord, B.; Plazas, A. A.; Romer, A. K.; Roodman, A.; Rykoff, E. S.; Sanchez, E.; Scarpine, V.; Schubnell, M.; Sevilla-Noarbe, I.; Smith, R. C.; Soares-Santos, M.; Sobreira, F.; Swanson, M. E. C.; Tarle, G.; Thomas, D.; Vikram, V.; Walker, A. R.; Zhang, Y.; DES Collaboration

2016-12-01

Shear peak statistics has gained a lot of attention recently as a practical alternative to the two-point statistics for constraining cosmological parameters. We perform a shear peak statistics analysis of the Dark Energy Survey (DES) Science Verification (SV) data, using weak gravitational lensing measurements from a 139 deg2 field. We measure the abundance of peaks identified in aperture mass maps, as a function of their signal-to-noise ratio, in the signal-to-noise range 04 would require significant corrections, which is why we do not include them in our analysis. We compare our results to the cosmological constraints from the two-point analysis on the SV field and find them to be in good agreement in both the central value and its uncertainty. We discuss prospects for future peak statistics analysis with upcoming DES data.