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.

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.

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

Statistical Analysis of Research Data | Center for Cancer Research

Cancer.gov

Recent advances in cancer biology have resulted in the need for increased statistical analysis of research data. The Statistical Analysis of Research Data (SARD) course will be held on April 5-6, 2018 from 9 a.m.-5 p.m. at the National Institutes of Health's Natcher Conference Center, Balcony C on the Bethesda Campus. SARD is designed to provide an overview on the general principles of statistical analysis of research data.  The first day will feature univariate data analysis, including descriptive statistics, probability distributions, one- and two-sample inferential statistics.

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.

Advanced statistical energy analysis

NASA Astrophysics Data System (ADS)

Heron, K. H.

1994-09-01

A high-frequency theory (advanced statistical energy analysis (ASEA)) is developed which takes account of the mechanism of tunnelling and uses a ray theory approach to track the power flowing around a plate or a beam network and then uses statistical energy analysis (SEA) to take care of any residual power. ASEA divides the energy of each sub-system into energy that is freely available for transfer to other sub-systems and energy that is fixed within the sub-systems that are physically separate and can be interpreted as a series of mathematical models, the first of which is identical to standard SEA and subsequent higher order models are convergent on an accurate prediction. Using a structural assembly of six rods as an example, ASEA is shown to converge onto the exact results while SEA is shown to overpredict by up to 60 dB.

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

Asymptotic modal analysis and statistical energy analysis

NASA Technical Reports Server (NTRS)

Dowell, Earl H.

1992-01-01

Asymptotic Modal Analysis (AMA) is a method which is used to model linear dynamical systems with many participating modes. The AMA method was originally developed to show the relationship between statistical energy analysis (SEA) and classical modal analysis (CMA). In the limit of a large number of modes of a vibrating system, the classical modal analysis result can be shown to be equivalent to the statistical energy analysis result. As the CMA result evolves into the SEA result, a number of systematic assumptions are made. Most of these assumptions are based upon the supposition that the number of modes approaches infinity. It is for this reason that the term 'asymptotic' is used. AMA is the asymptotic result of taking the limit of CMA as the number of modes approaches infinity. AMA refers to any of the intermediate results between CMA and SEA, as well as the SEA result which is derived from CMA. The main advantage of the AMA method is that individual modal characteristics are not required in the model or computations. By contrast, CMA requires that each modal parameter be evaluated at each frequency. In the latter, contributions from each mode are computed and the final answer is obtained by summing over all the modes in the particular band of interest. AMA evaluates modal parameters only at their center frequency and does not sum the individual contributions from each mode in order to obtain a final result. The method is similar to SEA in this respect. However, SEA is only capable of obtaining spatial averages or means, as it is a statistical method. Since AMA is systematically derived from CMA, it can obtain local spatial information as well.

Statistical power analysis in wildlife research

USGS Publications Warehouse

Steidl, R.J.; Hayes, J.P.

1997-01-01

Statistical power analysis can be used to increase the efficiency of research efforts and to clarify research results. Power analysis is most valuable in the design or planning phases of research efforts. Such prospective (a priori) power analyses can be used to guide research design and to estimate the number of samples necessary to achieve a high probability of detecting biologically significant effects. Retrospective (a posteriori) power analysis has been advocated as a method to increase information about hypothesis tests that were not rejected. However, estimating power for tests of null hypotheses that were not rejected with the effect size observed in the study is incorrect; these power estimates will always be a??0.50 when bias adjusted and have no relation to true power. Therefore, retrospective power estimates based on the observed effect size for hypothesis tests that were not rejected are misleading; retrospective power estimates are only meaningful when based on effect sizes other than the observed effect size, such as those effect sizes hypothesized to be biologically significant. Retrospective power analysis can be used effectively to estimate the number of samples or effect size that would have been necessary for a completed study to have rejected a specific null hypothesis. Simply presenting confidence intervals can provide additional information about null hypotheses that were not rejected, including information about the size of the true effect and whether or not there is adequate evidence to 'accept' a null hypothesis as true. We suggest that (1) statistical power analyses be routinely incorporated into research planning efforts to increase their efficiency, (2) confidence intervals be used in lieu of retrospective power analyses for null hypotheses that were not rejected to assess the likely size of the true effect, (3) minimum biologically significant effect sizes be used for all power analyses, and (4) if retrospective power estimates are to

Methodologies for the Statistical Analysis of Memory Response to Radiation

NASA Astrophysics Data System (ADS)

Bosser, Alexandre L.; Gupta, Viyas; Tsiligiannis, Georgios; Frost, Christopher D.; Zadeh, Ali; Jaatinen, Jukka; Javanainen, Arto; Puchner, Helmut; Saigné, Frédéric; Virtanen, Ari; Wrobel, Frédéric; Dilillo, Luigi

2016-08-01

Methodologies are proposed for in-depth statistical analysis of Single Event Upset data. The motivation for using these methodologies is to obtain precise information on the intrinsic defects and weaknesses of the tested devices, and to gain insight on their failure mechanisms, at no additional cost. The case study is a 65 nm SRAM irradiated with neutrons, protons and heavy ions. This publication is an extended version of a previous study [1].

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

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

Statistical Analysis For Nucleus/Nucleus Collisions

NASA Technical Reports Server (NTRS)

Mcguire, Stephen C.

1989-01-01

Report describes use of several statistical techniques to charactertize angular distributions of secondary particles emitted in collisions of atomic nuclei in energy range of 24 to 61 GeV per nucleon. Purpose of statistical analysis to determine correlations between intensities of emitted particles and angles comfirming existence of quark/gluon plasma.

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

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

Improved score statistics for meta-analysis in single-variant and gene-level association studies.

PubMed

Yang, Jingjing; Chen, Sai; Abecasis, Gonçalo

2018-06-01

Meta-analysis is now an essential tool for genetic association studies, allowing them to combine large studies and greatly accelerating the pace of genetic discovery. Although the standard meta-analysis methods perform equivalently as the more cumbersome joint analysis under ideal settings, they result in substantial power loss under unbalanced settings with various case-control ratios. Here, we investigate the power loss problem by the standard meta-analysis methods for unbalanced studies, and further propose novel meta-analysis methods performing equivalently to the joint analysis under both balanced and unbalanced settings. We derive improved meta-score-statistics that can accurately approximate the joint-score-statistics with combined individual-level data, for both linear and logistic regression models, with and without covariates. In addition, we propose a novel approach to adjust for population stratification by correcting for known population structures through minor allele frequencies. In the simulated gene-level association studies under unbalanced settings, our method recovered up to 85% power loss caused by the standard methods. We further showed the power gain of our methods in gene-level tests with 26 unbalanced studies of age-related macular degeneration . In addition, we took the meta-analysis of three unbalanced studies of type 2 diabetes as an example to discuss the challenges of meta-analyzing multi-ethnic samples. In summary, our improved meta-score-statistics with corrections for population stratification can be used to construct both single-variant and gene-level association studies, providing a useful framework for ensuring well-powered, convenient, cross-study analyses. © 2018 WILEY PERIODICALS, INC.

A note on generalized Genome Scan Meta-Analysis statistics

PubMed Central

Koziol, James A; Feng, Anne C

2005-01-01

Background Wise et al. introduced a rank-based statistical technique for meta-analysis of genome scans, the Genome Scan Meta-Analysis (GSMA) method. Levinson et al. recently described two generalizations of the GSMA statistic: (i) a weighted version of the GSMA statistic, so that different studies could be ascribed different weights for analysis; and (ii) an order statistic approach, reflecting the fact that a GSMA statistic can be computed for each chromosomal region or bin width across the various genome scan studies. Results We provide an Edgeworth approximation to the null distribution of the weighted GSMA statistic, and, we examine the limiting distribution of the GSMA statistics under the order statistic formulation, and quantify the relevance of the pairwise correlations of the GSMA statistics across different bins on this limiting distribution. We also remark on aggregate criteria and multiple testing for determining significance of GSMA results. Conclusion Theoretical considerations detailed herein can lead to clarification and simplification of testing criteria for generalizations of the GSMA statistic. PMID:15717930

Statistical Analysis of Compressive and Flexural Test Results on the Sustainable Adobe Reinforced with Steel Wire Mesh

NASA Astrophysics Data System (ADS)

Jokhio, Gul A.; Syed Mohsin, Sharifah M.; Gul, Yasmeen

2018-04-01

It has been established that Adobe provides, in addition to being sustainable and economic, a better indoor air quality without spending extensive amounts of energy as opposed to the modern synthetic materials. The material, however, suffers from weak structural behaviour when subjected to adverse loading conditions. A wide range of mechanical properties has been reported in literature owing to lack of research and standardization. The present paper presents the statistical analysis of the results that were obtained through compressive and flexural tests on Adobe samples. Adobe specimens with and without wire mesh reinforcement were tested and the results were reported. The statistical analysis of these results presents an interesting read. It has been found that the compressive strength of adobe increases by about 43% after adding a single layer of wire mesh reinforcement. This increase is statistically significant. The flexural response of Adobe has also shown improvement with the addition of wire mesh reinforcement, however, the statistical significance of the same cannot be established.

STATISTICAL SAMPLING AND DATA ANALYSIS

EPA Science Inventory

Research is being conducted to develop approaches to improve soil and sediment sampling techniques, measurement design and geostatistics, and data analysis via chemometric, environmetric, and robust statistical methods. Improvements in sampling contaminated soil and other hetero...

Analysis of Variance: What Is Your Statistical Software Actually Doing?

ERIC Educational Resources Information Center

Li, Jian; Lomax, Richard G.

2011-01-01

Users assume statistical software packages produce accurate results. In this article, the authors systematically examined Statistical Package for the Social Sciences (SPSS) and Statistical Analysis System (SAS) for 3 analysis of variance (ANOVA) designs, mixed-effects ANOVA, fixed-effects analysis of covariance (ANCOVA), and nested ANOVA. For each…

Statistical Analysis Techniques for Small Sample Sizes

NASA Technical Reports Server (NTRS)

Navard, S. E.

1984-01-01

The small sample sizes problem which is encountered when dealing with analysis of space-flight data is examined. Because of such a amount of data available, careful analyses are essential to extract the maximum amount of information with acceptable accuracy. Statistical analysis of small samples is described. The background material necessary for understanding statistical hypothesis testing is outlined and the various tests which can be done on small samples are explained. Emphasis is on the underlying assumptions of each test and on considerations needed to choose the most appropriate test for a given type of analysis.

Notes on numerical reliability of several statistical analysis programs

USGS Publications Warehouse

Landwehr, J.M.; Tasker, Gary D.

1999-01-01

This report presents a benchmark analysis of several statistical analysis programs currently in use in the USGS. The benchmark consists of a comparison between the values provided by a statistical analysis program for variables in the reference data set ANASTY and their known or calculated theoretical values. The ANASTY data set is an amendment of the Wilkinson NASTY data set that has been used in the statistical literature to assess the reliability (computational correctness) of calculated analytical results.

Comparing Visual and Statistical Analysis of Multiple Baseline Design Graphs.

PubMed

Wolfe, Katie; Dickenson, Tammiee S; Miller, Bridget; McGrath, Kathleen V

2018-04-01

A growing number of statistical analyses are being developed for single-case research. One important factor in evaluating these methods is the extent to which each corresponds to visual analysis. Few studies have compared statistical and visual analysis, and information about more recently developed statistics is scarce. Therefore, our purpose was to evaluate the agreement between visual analysis and four statistical analyses: improvement rate difference (IRD); Tau-U; Hedges, Pustejovsky, Shadish (HPS) effect size; and between-case standardized mean difference (BC-SMD). Results indicate that IRD and BC-SMD had the strongest overall agreement with visual analysis. Although Tau-U had strong agreement with visual analysis on raw values, it had poorer agreement when those values were dichotomized to represent the presence or absence of a functional relation. Overall, visual analysis appeared to be more conservative than statistical analysis, but further research is needed to evaluate the nature of these disagreements.

Statistical models and NMR analysis of polymer microstructure

USDA-ARS?s Scientific Manuscript database

Statistical models can be used in conjunction with NMR spectroscopy to study polymer microstructure and polymerization mechanisms. Thus, Bernoullian, Markovian, and enantiomorphic-site models are well known. Many additional models have been formulated over the years for additional situations. Typica...

Statistical analysis of early failures in electromigration

NASA Astrophysics Data System (ADS)

Gall, M.; Capasso, C.; Jawarani, D.; Hernandez, R.; Kawasaki, H.; Ho, P. S.

2001-07-01

The detection of early failures in electromigration (EM) and the complicated statistical nature of this important reliability phenomenon have been difficult issues to treat in the past. A satisfactory experimental approach for the detection and the statistical analysis of early failures has not yet been established. This is mainly due to the rare occurrence of early failures and difficulties in testing of large sample populations. Furthermore, experimental data on the EM behavior as a function of varying number of failure links are scarce. In this study, a technique utilizing large interconnect arrays in conjunction with the well-known Wheatstone Bridge is presented. Three types of structures with a varying number of Ti/TiN/Al(Cu)/TiN-based interconnects were used, starting from a small unit of five lines in parallel. A serial arrangement of this unit enabled testing of interconnect arrays encompassing 480 possible failure links. In addition, a Wheatstone Bridge-type wiring using four large arrays in each device enabled simultaneous testing of 1920 interconnects. In conjunction with a statistical deconvolution to the single interconnect level, the results indicate that the electromigration failure mechanism studied here follows perfect lognormal behavior down to the four sigma level. The statistical deconvolution procedure is described in detail. Over a temperature range from 155 to 200 °C, a total of more than 75 000 interconnects were tested. None of the samples have shown an indication of early, or alternate, failure mechanisms. The activation energy of the EM mechanism studied here, namely the Cu incubation time, was determined to be Q=1.08±0.05 eV. We surmise that interface diffusion of Cu along the Al(Cu) sidewalls and along the top and bottom refractory layers, coupled with grain boundary diffusion within the interconnects, constitutes the Cu incubation mechanism.

Quantile regression for the statistical analysis of immunological data with many non-detects.

PubMed

Eilers, Paul H C; Röder, Esther; Savelkoul, Huub F J; van Wijk, Roy Gerth

2012-07-07

Immunological parameters are hard to measure. A well-known problem is the occurrence of values below the detection limit, the non-detects. Non-detects are a nuisance, because classical statistical analyses, like ANOVA and regression, cannot be applied. The more advanced statistical techniques currently available for the analysis of datasets with non-detects can only be used if a small percentage of the data are non-detects. Quantile regression, a generalization of percentiles to regression models, models the median or higher percentiles and tolerates very high numbers of non-detects. We present a non-technical introduction and illustrate it with an implementation to real data from a clinical trial. We show that by using quantile regression, groups can be compared and that meaningful linear trends can be computed, even if more than half of the data consists of non-detects. Quantile regression is a valuable addition to the statistical methods that can be used for the analysis of immunological datasets with non-detects.

Statistical analysis for validating ACO-KNN algorithm as feature selection in sentiment analysis

NASA Astrophysics Data System (ADS)

Ahmad, Siti Rohaidah; Yusop, Nurhafizah Moziyana Mohd; Bakar, Azuraliza Abu; Yaakub, Mohd Ridzwan

2017-10-01

This research paper aims to propose a hybrid of ant colony optimization (ACO) and k-nearest neighbor (KNN) algorithms as feature selections for selecting and choosing relevant features from customer review datasets. Information gain (IG), genetic algorithm (GA), and rough set attribute reduction (RSAR) were used as baseline algorithms in a performance comparison with the proposed algorithm. This paper will also discuss the significance test, which was used to evaluate the performance differences between the ACO-KNN, IG-GA, and IG-RSAR algorithms. This study evaluated the performance of the ACO-KNN algorithm using precision, recall, and F-score, which were validated using the parametric statistical significance tests. The evaluation process has statistically proven that this ACO-KNN algorithm has been significantly improved compared to the baseline algorithms. The evaluation process has statistically proven that this ACO-KNN algorithm has been significantly improved compared to the baseline algorithms. In addition, the experimental results have proven that the ACO-KNN can be used as a feature selection technique in sentiment analysis to obtain quality, optimal feature subset that can represent the actual data in customer review data.

Statistical Analysis of NAS Parallel Benchmarks and LINPACK Results

NASA Technical Reports Server (NTRS)

Meuer, Hans-Werner; Simon, Horst D.; Strohmeier, Erich; Lasinski, T. A. (Technical Monitor)

1994-01-01

In the last three years extensive performance data have been reported for parallel machines both based on the NAS Parallel Benchmarks, and on LINPACK. In this study we have used the reported benchmark results and performed a number of statistical experiments using factor, cluster, and regression analyses. In addition to the performance results of LINPACK and the eight NAS parallel benchmarks, we have also included peak performance of the machine, and the LINPACK n and n(sub 1/2) values. Some of the results and observations can be summarized as follows: 1) All benchmarks are strongly correlated with peak performance. 2) LINPACK and EP have each a unique signature. 3) The remaining NPB can grouped into three groups as follows: (CG and IS), (LU and SP), and (MG, FT, and BT). Hence three (or four with EP) benchmarks are sufficient to characterize the overall NPB performance. Our poster presentation will follow a standard poster format, and will present the data of our statistical analysis in detail.

A statistical analysis of the impact of advertising signs on road safety.

PubMed

Yannis, George; Papadimitriou, Eleonora; Papantoniou, Panagiotis; Voulgari, Chrisoula

2013-01-01

This research aims to investigate the impact of advertising signs on road safety. An exhaustive review of international literature was carried out on the effect of advertising signs on driver behaviour and safety. Moreover, a before-and-after statistical analysis with control groups was applied on several road sites with different characteristics in the Athens metropolitan area, in Greece, in order to investigate the correlation between the placement or removal of advertising signs and the related occurrence of road accidents. Road accident data for the 'before' and 'after' periods on the test sites and the control sites were extracted from the database of the Hellenic Statistical Authority, and the selected 'before' and 'after' periods vary from 2.5 to 6 years. The statistical analysis shows no statistical correlation between road accidents and advertising signs in none of the nine sites examined, as the confidence intervals of the estimated safety effects are non-significant at 95% confidence level. This can be explained by the fact that, in the examined road sites, drivers are overloaded with information (traffic signs, directions signs, labels of shops, pedestrians and other vehicles, etc.) so that the additional information load from advertising signs may not further distract them.

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

Application of Ontology Technology in Health Statistic Data Analysis.

PubMed

Guo, Minjiang; Hu, Hongpu; Lei, Xingyun

2017-01-01

Research Purpose: establish health management ontology for analysis of health statistic data. Proposed Methods: this paper established health management ontology based on the analysis of the concepts in China Health Statistics Yearbook, and used protégé to define the syntactic and semantic structure of health statistical data. six classes of top-level ontology concepts and their subclasses had been extracted and the object properties and data properties were defined to establish the construction of these classes. By ontology instantiation, we can integrate multi-source heterogeneous data and enable administrators to have an overall understanding and analysis of the health statistic data. ontology technology provides a comprehensive and unified information integration structure of the health management domain and lays a foundation for the efficient analysis of multi-source and heterogeneous health system management data and enhancement of the management efficiency.

Explorations in Statistics: The Analysis of Change

ERIC Educational Resources Information Center

Curran-Everett, Douglas; Williams, Calvin L.

2015-01-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This tenth installment of "Explorations in Statistics" explores the analysis of a potential change in some physiological response. As researchers, we often express absolute change as percent change so we can…

Common pitfalls in statistical analysis: “P” values, statistical significance and confidence intervals

PubMed Central

Ranganathan, Priya; Pramesh, C. S.; Buyse, Marc

2015-01-01

In the second part of a series on pitfalls in statistical analysis, we look at various ways in which a statistically significant study result can be expressed. We debunk some of the myths regarding the ‘P’ value, explain the importance of ‘confidence intervals’ and clarify the importance of including both values in a paper PMID:25878958

Exploratory Visual Analysis of Statistical Results from Microarray Experiments Comparing High and Low Grade Glioma

PubMed Central

Reif, David M.; Israel, Mark A.; Moore, Jason H.

2007-01-01

The biological interpretation of gene expression microarray results is a daunting challenge. For complex diseases such as cancer, wherein the body of published research is extensive, the incorporation of expert knowledge provides a useful analytical framework. We have previously developed the Exploratory Visual Analysis (EVA) software for exploring data analysis results in the context of annotation information about each gene, as well as biologically relevant groups of genes. We present EVA as a flexible combination of statistics and biological annotation that provides a straightforward visual interface for the interpretation of microarray analyses of gene expression in the most commonly occuring class of brain tumors, glioma. We demonstrate the utility of EVA for the biological interpretation of statistical results by analyzing publicly available gene expression profiles of two important glial tumors. The results of a statistical comparison between 21 malignant, high-grade glioblastoma multiforme (GBM) tumors and 19 indolent, low-grade pilocytic astrocytomas were analyzed using EVA. By using EVA to examine the results of a relatively simple statistical analysis, we were able to identify tumor class-specific gene expression patterns having both statistical and biological significance. Our interactive analysis highlighted the potential importance of genes involved in cell cycle progression, proliferation, signaling, adhesion, migration, motility, and structure, as well as candidate gene loci on a region of Chromosome 7 that has been implicated in glioma. Because EVA does not require statistical or computational expertise and has the flexibility to accommodate any type of statistical analysis, we anticipate EVA will prove a useful addition to the repertoire of computational methods used for microarray data analysis. EVA is available at no charge to academic users and can be found at http://www.epistasis.org. PMID:19390666

Using Statistical Analysis Software to Advance Nitro Plasticizer Wettability

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

Shear, Trevor Allan

Statistical analysis in science is an extremely powerful tool that is often underutilized. Additionally, it is frequently the case that data is misinterpreted or not used to its fullest extent. Utilizing the advanced software JMP®, many aspects of experimental design and data analysis can be evaluated and improved. This overview will detail the features of JMP® and how they were used to advance a project, resulting in time and cost savings, as well as the collection of scientifically sound data. The project analyzed in this report addresses the inability of a nitro plasticizer to coat a gold coated quartz crystalmore » sensor used in a quartz crystal microbalance. Through the use of the JMP® software, the wettability of the nitro plasticizer was increased by over 200% using an atmospheric plasma pen, ensuring good sample preparation and reliable results.« less

Multivariate statistical analysis software technologies for astrophysical research involving large data bases

NASA Technical Reports Server (NTRS)

Djorgovski, George

1993-01-01

The existing and forthcoming data bases from NASA missions contain an abundance of information whose complexity cannot be efficiently tapped with simple statistical techniques. Powerful multivariate statistical methods already exist which can be used to harness much of the richness of these data. Automatic classification techniques have been developed to solve the problem of identifying known types of objects in multiparameter data sets, in addition to leading to the discovery of new physical phenomena and classes of objects. We propose an exploratory study and integration of promising techniques in the development of a general and modular classification/analysis system for very large data bases, which would enhance and optimize data management and the use of human research resource.

Multivariate statistical analysis software technologies for astrophysical research involving large data bases

NASA Technical Reports Server (NTRS)

Djorgovski, Stanislav

1992-01-01

The existing and forthcoming data bases from NASA missions contain an abundance of information whose complexity cannot be efficiently tapped with simple statistical techniques. Powerful multivariate statistical methods already exist which can be used to harness much of the richness of these data. Automatic classification techniques have been developed to solve the problem of identifying known types of objects in multi parameter data sets, in addition to leading to the discovery of new physical phenomena and classes of objects. We propose an exploratory study and integration of promising techniques in the development of a general and modular classification/analysis system for very large data bases, which would enhance and optimize data management and the use of human research resources.

Statistical performance and information content of time lag analysis and redundancy analysis in time series modeling.

PubMed

Angeler, David G; Viedma, Olga; Moreno, José M

2009-11-01

Time lag analysis (TLA) is a distance-based approach used to study temporal dynamics of ecological communities by measuring community dissimilarity over increasing time lags. Despite its increased use in recent years, its performance in comparison with other more direct methods (i.e., canonical ordination) has not been evaluated. This study fills this gap using extensive simulations and real data sets from experimental temporary ponds (true zooplankton communities) and landscape studies (landscape categories as pseudo-communities) that differ in community structure and anthropogenic stress history. Modeling time with a principal coordinate of neighborhood matrices (PCNM) approach, the canonical ordination technique (redundancy analysis; RDA) consistently outperformed the other statistical tests (i.e., TLAs, Mantel test, and RDA based on linear time trends) using all real data. In addition, the RDA-PCNM revealed different patterns of temporal change, and the strength of each individual time pattern, in terms of adjusted variance explained, could be evaluated, It also identified species contributions to these patterns of temporal change. This additional information is not provided by distance-based methods. The simulation study revealed better Type I error properties of the canonical ordination techniques compared with the distance-based approaches when no deterministic component of change was imposed on the communities. The simulation also revealed that strong emphasis on uniform deterministic change and low variability at other temporal scales is needed to result in decreased statistical power of the RDA-PCNM approach relative to the other methods. Based on the statistical performance of and information content provided by RDA-PCNM models, this technique serves ecologists as a powerful tool for modeling temporal change of ecological (pseudo-) communities.

Do-it-yourself statistics: A computer-assisted likelihood approach to analysis of data from genetic crosses.

PubMed Central

Robbins, L G

2000-01-01

Graduate school programs in genetics have become so full that courses in statistics have often been eliminated. In addition, typical introductory statistics courses for the "statistics user" rather than the nascent statistician are laden with methods for analysis of measured variables while genetic data are most often discrete numbers. These courses are often seen by students and genetics professors alike as largely irrelevant cookbook courses. The powerful methods of likelihood analysis, although commonly employed in human genetics, are much less often used in other areas of genetics, even though current computational tools make this approach readily accessible. This article introduces the MLIKELY.PAS computer program and the logic of do-it-yourself maximum-likelihood statistics. The program itself, course materials, and expanded discussions of some examples that are only summarized here are available at http://www.unisi. it/ricerca/dip/bio_evol/sitomlikely/mlikely.h tml. PMID:10628965

Study designs, use of statistical tests, and statistical analysis software choice in 2015: Results from two Pakistani monthly Medline indexed journals.

PubMed

Shaikh, Masood Ali

2017-09-01

Assessment of research articles in terms of study designs used, statistical tests applied and the use of statistical analysis programmes help determine research activity profile and trends in the country. In this descriptive study, all original articles published by Journal of Pakistan Medical Association (JPMA) and Journal of the College of Physicians and Surgeons Pakistan (JCPSP), in the year 2015 were reviewed in terms of study designs used, application of statistical tests, and the use of statistical analysis programmes. JPMA and JCPSP published 192 and 128 original articles, respectively, in the year 2015. Results of this study indicate that cross-sectional study design, bivariate inferential statistical analysis entailing comparison between two variables/groups, and use of statistical software programme SPSS to be the most common study design, inferential statistical analysis, and statistical analysis software programmes, respectively. These results echo previously published assessment of these two journals for the year 2014.

Statistical analysis of tire treadwear data

DOT National Transportation Integrated Search

1985-03-01

This report describes the results of a statistical analysis of the treadwear : variability of radial tires subjected to the Uniform Tire Quality Grading (UTQG) : standard. Because unexplained variability in the treadwear portion of the standard : cou...

GIS and statistical analysis for landslide susceptibility mapping in the Daunia area, Italy

NASA Astrophysics Data System (ADS)

Mancini, F.; Ceppi, C.; Ritrovato, G.

2010-09-01

This study focuses on landslide susceptibility mapping in the Daunia area (Apulian Apennines, Italy) and achieves this by using a multivariate statistical method and data processing in a Geographical Information System (GIS). The Logistic Regression (hereafter LR) method was chosen to produce a susceptibility map over an area of 130 000 ha where small settlements are historically threatened by landslide phenomena. By means of LR analysis, the tendency to landslide occurrences was, therefore, assessed by relating a landslide inventory (dependent variable) to a series of causal factors (independent variables) which were managed in the GIS, while the statistical analyses were performed by means of the SPSS (Statistical Package for the Social Sciences) software. The LR analysis produced a reliable susceptibility map of the investigated area and the probability level of landslide occurrence was ranked in four classes. The overall performance achieved by the LR analysis was assessed by local comparison between the expected susceptibility and an independent dataset extrapolated from the landslide inventory. Of the samples classified as susceptible to landslide occurrences, 85% correspond to areas where landslide phenomena have actually occurred. In addition, the consideration of the regression coefficients provided by the analysis demonstrated that a major role is played by the "land cover" and "lithology" causal factors in determining the occurrence and distribution of landslide phenomena in the Apulian Apennines.

Transit safety & security statistics & analysis 2003 annual report (formerly SAMIS)

DOT National Transportation Integrated Search

2005-12-01

The Transit Safety & Security Statistics & Analysis 2003 Annual Report (formerly SAMIS) is a compilation and analysis of mass transit accident, casualty, and crime statistics reported under the Federal Transit Administrations (FTAs) National Tr...

Transit safety & security statistics & analysis 2002 annual report (formerly SAMIS)

DOT National Transportation Integrated Search

2004-12-01

The Transit Safety & Security Statistics & Analysis 2002 Annual Report (formerly SAMIS) is a compilation and analysis of mass transit accident, casualty, and crime statistics reported under the Federal Transit Administrations (FTAs) National Tr...

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

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.

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

CORSSA: The Community Online Resource for Statistical Seismicity Analysis

USGS Publications Warehouse

Michael, Andrew J.; Wiemer, Stefan

2010-01-01

Statistical seismology is the application of rigorous statistical methods to earthquake science with the goal of improving our knowledge of how the earth works. Within statistical seismology there is a strong emphasis on the analysis of seismicity data in order to improve our scientific understanding of earthquakes and to improve the evaluation and testing of earthquake forecasts, earthquake early warning, and seismic hazards assessments. Given the societal importance of these applications, statistical seismology must be done well. Unfortunately, a lack of educational resources and available software tools make it difficult for students and new practitioners to learn about this discipline. The goal of the Community Online Resource for Statistical Seismicity Analysis (CORSSA) is to promote excellence in statistical seismology by providing the knowledge and resources necessary to understand and implement the best practices, so that the reader can apply these methods to their own research. This introduction describes the motivation for and vision of CORRSA. It also describes its structure and contents.

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

Statistical evaluation of vibration analysis techniques

NASA Technical Reports Server (NTRS)

Milner, G. Martin; Miller, Patrice S.

1987-01-01

An evaluation methodology is presented for a selection of candidate vibration analysis techniques applicable to machinery representative of the environmental control and life support system of advanced spacecraft; illustrative results are given. Attention is given to the statistical analysis of small sample experiments, the quantification of detection performance for diverse techniques through the computation of probability of detection versus probability of false alarm, and the quantification of diagnostic performance.

SOCR: Statistics Online Computational Resource

PubMed Central

Dinov, Ivo D.

2011-01-01

The need for hands-on computer laboratory experience in undergraduate and graduate statistics education has been firmly established in the past decade. As a result a number of attempts have been undertaken to develop novel approaches for problem-driven statistical thinking, data analysis and result interpretation. In this paper we describe an integrated educational web-based framework for: interactive distribution modeling, virtual online probability experimentation, statistical data analysis, visualization and integration. Following years of experience in statistical teaching at all college levels using established licensed statistical software packages, like STATA, S-PLUS, R, SPSS, SAS, Systat, etc., we have attempted to engineer a new statistics education environment, the Statistics Online Computational Resource (SOCR). This resource performs many of the standard types of statistical analysis, much like other classical tools. In addition, it is designed in a plug-in object-oriented architecture and is completely platform independent, web-based, interactive, extensible and secure. Over the past 4 years we have tested, fine-tuned and reanalyzed the SOCR framework in many of our undergraduate and graduate probability and statistics courses and have evidence that SOCR resources build student’s intuition and enhance their learning. PMID:21451741

Statistical energy analysis computer program, user's guide

NASA Technical Reports Server (NTRS)

Trudell, R. W.; Yano, L. I.

1981-01-01

A high frequency random vibration analysis, (statistical energy analysis (SEA) method) is examined. The SEA method accomplishes high frequency prediction of arbitrary structural configurations. A general SEA computer program is described. A summary of SEA theory, example problems of SEA program application, and complete program listing are presented.

Interfaces between statistical analysis packages and the ESRI geographic information system

NASA Technical Reports Server (NTRS)

Masuoka, E.

1980-01-01

Interfaces between ESRI's geographic information system (GIS) data files and real valued data files written to facilitate statistical analysis and display of spatially referenced multivariable data are described. An example of data analysis which utilized the GIS and the statistical analysis system is presented to illustrate the utility of combining the analytic capability of a statistical package with the data management and display features of the GIS.

Time Series Analysis Based on Running Mann Whitney Z Statistics

USDA-ARS?s Scientific Manuscript database

A sensitive and objective time series analysis method based on the calculation of Mann Whitney U statistics is described. This method samples data rankings over moving time windows, converts those samples to Mann-Whitney U statistics, and then normalizes the U statistics to Z statistics using Monte-...

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.

FADTTSter: accelerating hypothesis testing with functional analysis of diffusion tensor tract statistics

NASA Astrophysics Data System (ADS)

Noel, Jean; Prieto, Juan C.; Styner, Martin

2017-03-01

Functional Analysis of Diffusion Tensor Tract Statistics (FADTTS) is a toolbox for analysis of white matter (WM) fiber tracts. It allows associating diffusion properties along major WM bundles with a set of covariates of interest, such as age, diagnostic status and gender, and the structure of the variability of these WM tract properties. However, to use this toolbox, a user must have an intermediate knowledge in scripting languages (MATLAB). FADTTSter was created to overcome this issue and make the statistical analysis accessible to any non-technical researcher. FADTTSter is actively being used by researchers at the University of North Carolina. FADTTSter guides non-technical users through a series of steps including quality control of subjects and fibers in order to setup the necessary parameters to run FADTTS. Additionally, FADTTSter implements interactive charts for FADTTS' outputs. This interactive chart enhances the researcher experience and facilitates the analysis of the results. FADTTSter's motivation is to improve usability and provide a new analysis tool to the community that complements FADTTS. Ultimately, by enabling FADTTS to a broader audience, FADTTSter seeks to accelerate hypothesis testing in neuroimaging studies involving heterogeneous clinical data and diffusion tensor imaging. This work is submitted to the Biomedical Applications in Molecular, Structural, and Functional Imaging conference. The source code of this application is available in NITRC.

Statistical Analysis of Research Data | Center for Cancer Research

Cancer.gov

Recent advances in cancer biology have resulted in the need for increased statistical analysis of research data. The Statistical Analysis of Research Data (SARD) course will be held on April 5-6, 2018 from 9 a.m.-5 p.m. at the National Institutes of Health's Natcher Conference Center, Balcony C on the Bethesda Campus. SARD is designed to provide an overview on the general

A statistical package for computing time and frequency domain analysis

NASA Technical Reports Server (NTRS)

Brownlow, J.

1978-01-01

The spectrum analysis (SPA) program is a general purpose digital computer program designed to aid in data analysis. The program does time and frequency domain statistical analyses as well as some preanalysis data preparation. The capabilities of the SPA program include linear trend removal and/or digital filtering of data, plotting and/or listing of both filtered and unfiltered data, time domain statistical characterization of data, and frequency domain statistical characterization of data.

A Realistic Experimental Design and Statistical Analysis Project

ERIC Educational Resources Information Center

Muske, Kenneth R.; Myers, John A.

2007-01-01

A realistic applied chemical engineering experimental design and statistical analysis project is documented in this article. This project has been implemented as part of the professional development and applied statistics courses at Villanova University over the past five years. The novel aspects of this project are that the students are given a…

Internet Data Analysis for the Undergraduate Statistics Curriculum

ERIC Educational Resources Information Center

Sanchez, Juana; He, Yan

2005-01-01

Statistics textbooks for undergraduates have not caught up with the enormous amount of analysis of Internet data that is taking place these days. Case studies that use Web server log data or Internet network traffic data are rare in undergraduate Statistics education. And yet these data provide numerous examples of skewed and bimodal…

Feature-Based Statistical Analysis of Combustion Simulation Data

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

Bennett, J; Krishnamoorthy, V; Liu, S

2011-11-18

We present a new framework for feature-based statistical analysis of large-scale scientific data and demonstrate its effectiveness by analyzing features from Direct Numerical Simulations (DNS) of turbulent combustion. Turbulent flows are ubiquitous and account for transport and mixing processes in combustion, astrophysics, fusion, and climate modeling among other disciplines. They are also characterized by coherent structure or organized motion, i.e. nonlocal entities whose geometrical features can directly impact molecular mixing and reactive processes. While traditional multi-point statistics provide correlative information, they lack nonlocal structural information, and hence, fail to provide mechanistic causality information between organized fluid motion and mixing andmore » reactive processes. Hence, it is of great interest to capture and track flow features and their statistics together with their correlation with relevant scalar quantities, e.g. temperature or species concentrations. In our approach we encode the set of all possible flow features by pre-computing merge trees augmented with attributes, such as statistical moments of various scalar fields, e.g. temperature, as well as length-scales computed via spectral analysis. The computation is performed in an efficient streaming manner in a pre-processing step and results in a collection of meta-data that is orders of magnitude smaller than the original simulation data. This meta-data is sufficient to support a fully flexible and interactive analysis of the features, allowing for arbitrary thresholds, providing per-feature statistics, and creating various global diagnostics such as Cumulative Density Functions (CDFs), histograms, or time-series. We combine the analysis with a rendering of the features in a linked-view browser that enables scientists to interactively explore, visualize, and analyze the equivalent of one terabyte of simulation data. We highlight the utility of this new framework for

Bias, precision and statistical power of analysis of covariance in the analysis of randomized trials with baseline imbalance: a simulation study.

PubMed

Egbewale, Bolaji E; Lewis, Martyn; Sim, Julius

2014-04-09

Analysis of variance (ANOVA), change-score analysis (CSA) and analysis of covariance (ANCOVA) respond differently to baseline imbalance in randomized controlled trials. However, no empirical studies appear to have quantified the differential bias and precision of estimates derived from these methods of analysis, and their relative statistical power, in relation to combinations of levels of key trial characteristics. This simulation study therefore examined the relative bias, precision and statistical power of these three analyses using simulated trial data. 126 hypothetical trial scenarios were evaluated (126,000 datasets), each with continuous data simulated by using a combination of levels of: treatment effect; pretest-posttest correlation; direction and magnitude of baseline imbalance. The bias, precision and power of each method of analysis were calculated for each scenario. Compared to the unbiased estimates produced by ANCOVA, both ANOVA and CSA are subject to bias, in relation to pretest-posttest correlation and the direction of baseline imbalance. Additionally, ANOVA and CSA are less precise than ANCOVA, especially when pretest-posttest correlation ≥ 0.3. When groups are balanced at baseline, ANCOVA is at least as powerful as the other analyses. Apparently greater power of ANOVA and CSA at certain imbalances is achieved in respect of a biased treatment effect. Across a range of correlations between pre- and post-treatment scores and at varying levels and direction of baseline imbalance, ANCOVA remains the optimum statistical method for the analysis of continuous outcomes in RCTs, in terms of bias, precision and statistical power.

Bias, precision and statistical power of analysis of covariance in the analysis of randomized trials with baseline imbalance: a simulation study

PubMed Central

2014-01-01

Background Analysis of variance (ANOVA), change-score analysis (CSA) and analysis of covariance (ANCOVA) respond differently to baseline imbalance in randomized controlled trials. However, no empirical studies appear to have quantified the differential bias and precision of estimates derived from these methods of analysis, and their relative statistical power, in relation to combinations of levels of key trial characteristics. This simulation study therefore examined the relative bias, precision and statistical power of these three analyses using simulated trial data. Methods 126 hypothetical trial scenarios were evaluated (126 000 datasets), each with continuous data simulated by using a combination of levels of: treatment effect; pretest-posttest correlation; direction and magnitude of baseline imbalance. The bias, precision and power of each method of analysis were calculated for each scenario. Results Compared to the unbiased estimates produced by ANCOVA, both ANOVA and CSA are subject to bias, in relation to pretest-posttest correlation and the direction of baseline imbalance. Additionally, ANOVA and CSA are less precise than ANCOVA, especially when pretest-posttest correlation ≥ 0.3. When groups are balanced at baseline, ANCOVA is at least as powerful as the other analyses. Apparently greater power of ANOVA and CSA at certain imbalances is achieved in respect of a biased treatment effect. Conclusions Across a range of correlations between pre- and post-treatment scores and at varying levels and direction of baseline imbalance, ANCOVA remains the optimum statistical method for the analysis of continuous outcomes in RCTs, in terms of bias, precision and statistical power. PMID:24712304

Statistical methodology for the analysis of dye-switch microarray experiments

PubMed Central

Mary-Huard, Tristan; Aubert, Julie; Mansouri-Attia, Nadera; Sandra, Olivier; Daudin, Jean-Jacques

2008-01-01

Background In individually dye-balanced microarray designs, each biological sample is hybridized on two different slides, once with Cy3 and once with Cy5. While this strategy ensures an automatic correction of the gene-specific labelling bias, it also induces dependencies between log-ratio measurements that must be taken into account in the statistical analysis. Results We present two original statistical procedures for the statistical analysis of individually balanced designs. These procedures are compared with the usual ML and REML mixed model procedures proposed in most statistical toolboxes, on both simulated and real data. Conclusion The UP procedure we propose as an alternative to usual mixed model procedures is more efficient and significantly faster to compute. This result provides some useful guidelines for the analysis of complex designs. PMID:18271965

Statistics for People Who (Think They) Hate Statistics. Third Edition

ERIC Educational Resources Information Center

Salkind, Neil J.

2007-01-01

This text teaches an often intimidating and difficult subject in a way that is informative, personable, and clear. The author takes students through various statistical procedures, beginning with correlation and graphical representation of data and ending with inferential techniques and analysis of variance. In addition, the text covers SPSS, and…

Modular reweighting software for statistical mechanical analysis of biased equilibrium data

NASA Astrophysics Data System (ADS)

Sindhikara, Daniel J.

2012-07-01

Here a simple, useful, modular approach and software suite designed for statistical reweighting and analysis of equilibrium ensembles is presented. Statistical reweighting is useful and sometimes necessary for analysis of equilibrium enhanced sampling methods, such as umbrella sampling or replica exchange, and also in experimental cases where biasing factors are explicitly known. Essentially, statistical reweighting allows extrapolation of data from one or more equilibrium ensembles to another. Here, the fundamental separable steps of statistical reweighting are broken up into modules - allowing for application to the general case and avoiding the black-box nature of some “all-inclusive” reweighting programs. Additionally, the programs included are, by-design, written with little dependencies. The compilers required are either pre-installed on most systems, or freely available for download with minimal trouble. Examples of the use of this suite applied to umbrella sampling and replica exchange molecular dynamics simulations will be shown along with advice on how to apply it in the general case. New version program summaryProgram title: Modular reweighting version 2 Catalogue identifier: AEJH_v2_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEJH_v2_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GNU General Public License, version 3 No. of lines in distributed program, including test data, etc.: 179 118 No. of bytes in distributed program, including test data, etc.: 8 518 178 Distribution format: tar.gz Programming language: C++, Python 2.6+, Perl 5+ Computer: Any Operating system: Any RAM: 50-500 MB Supplementary material: An updated version of the original manuscript (Comput. Phys. Commun. 182 (2011) 2227) is available Classification: 4.13 Catalogue identifier of previous version: AEJH_v1_0 Journal reference of previous version: Comput. Phys. Commun. 182 (2011) 2227 Does the new

Re-analysis of survival data of cancer patients utilizing additive homeopathy.

PubMed

Gleiss, Andreas; Frass, Michael; Gaertner, Katharina

2016-08-01

In this short communication we present a re-analysis of homeopathic patient data in comparison to control patient data from the same Outpatient´s Unit "Homeopathy in malignant diseases" of the Medical University of Vienna. In this analysis we took account of a probable immortal time bias. For patients suffering from advanced stages of cancer and surviving the first 6 or 12 months after diagnosis, respectively, the results show that utilizing homeopathy gives a statistically significant (p<0.001) advantage over control patients regarding survival time. In conclusion, bearing in mind all limitations, the results of this retrospective study suggest that patients with advanced stages of cancer might benefit from additional homeopathic treatment until a survival time of up to 12 months after diagnosis. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

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.

Statistical Analysis of the Polarimetric Cloud Analysis and Seeding Test (POLCAST) Field Projects

NASA Astrophysics Data System (ADS)

Ekness, Jamie Lynn

The North Dakota farming industry brings in more than $4.1 billion annually in cash receipts. Unfortunately, agriculture sales vary significantly from year to year, which is due in large part to weather events such as hail storms and droughts. One method to mitigate drought is to use hygroscopic seeding to increase the precipitation efficiency of clouds. The North Dakota Atmospheric Research Board (NDARB) sponsored the Polarimetric Cloud Analysis and Seeding Test (POLCAST) research project to determine the effectiveness of hygroscopic seeding in North Dakota. The POLCAST field projects obtained airborne and radar observations, while conducting randomized cloud seeding. The Thunderstorm Identification Tracking and Nowcasting (TITAN) program is used to analyze radar data (33 usable cases) in determining differences in the duration of the storm, rain rate and total rain amount between seeded and non-seeded clouds. The single ratio of seeded to non-seeded cases is 1.56 (0.28 mm/0.18 mm) or 56% increase for the average hourly rainfall during the first 60 minutes after target selection. A seeding effect is indicated with the lifetime of the storms increasing by 41 % between seeded and non-seeded clouds for the first 60 minutes past seeding decision. A double ratio statistic, a comparison of radar derived rain amount of the last 40 minutes of a case (seed/non-seed), compared to the first 20 minutes (seed/non-seed), is used to account for the natural variability of the cloud system and gives a double ratio of 1.85. The Mann-Whitney test on the double ratio of seeded to non-seeded cases (33 cases) gives a significance (p-value) of 0.063. Bootstrapping analysis of the POLCAST set indicates that 50 cases would provide statistically significant results based on the Mann-Whitney test of the double ratio. All the statistical analysis conducted on the POLCAST data set show that hygroscopic seeding in North Dakota does increase precipitation. While an additional POLCAST field

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

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.

A Genome-Wide Association Analysis Reveals Epistatic Cancellation of Additive Genetic Variance for Root Length in Arabidopsis thaliana.

PubMed

Lachowiec, Jennifer; Shen, Xia; Queitsch, Christine; Carlborg, Örjan

2015-01-01

Efforts to identify loci underlying complex traits generally assume that most genetic variance is additive. Here, we examined the genetics of Arabidopsis thaliana root length and found that the genomic narrow-sense heritability for this trait in the examined population was statistically zero. The low amount of additive genetic variance that could be captured by the genome-wide genotypes likely explains why no associations to root length could be found using standard additive-model-based genome-wide association (GWA) approaches. However, as the broad-sense heritability for root length was significantly larger, and primarily due to epistasis, we also performed an epistatic GWA analysis to map loci contributing to the epistatic genetic variance. Four interacting pairs of loci were revealed, involving seven chromosomal loci that passed a standard multiple-testing corrected significance threshold. The genotype-phenotype maps for these pairs revealed epistasis that cancelled out the additive genetic variance, explaining why these loci were not detected in the additive GWA analysis. Small population sizes, such as in our experiment, increase the risk of identifying false epistatic interactions due to testing for associations with very large numbers of multi-marker genotypes in few phenotyped individuals. Therefore, we estimated the false-positive risk using a new statistical approach that suggested half of the associated pairs to be true positive associations. Our experimental evaluation of candidate genes within the seven associated loci suggests that this estimate is conservative; we identified functional candidate genes that affected root development in four loci that were part of three of the pairs. The statistical epistatic analyses were thus indispensable for confirming known, and identifying new, candidate genes for root length in this population of wild-collected A. thaliana accessions. We also illustrate how epistatic cancellation of the additive genetic variance

Crash analysis, statistics & information notebook 1996-2003

DOT National Transportation Integrated Search

2004-11-01

The Department of Motor Vehicle Safety is proud to present the Crash Analysis, Statistics & : Information (CASI) Notebook 1996-2003. DMVS developed the CASI Notebooks to provide : straightforward, easy to understand crash information. Each page or ta...

Statistical analysis and interpretation of prenatal diagnostic imaging studies, Part 2: descriptive and inferential statistical methods.

PubMed

Tuuli, Methodius G; Odibo, Anthony O

2011-08-01

The objective of this article is to discuss the rationale for common statistical tests used for the analysis and interpretation of prenatal diagnostic imaging studies. Examples from the literature are used to illustrate descriptive and inferential statistics. The uses and limitations of linear and logistic regression analyses are discussed in detail.

Fisher statistics for analysis of diffusion tensor directional information.

PubMed

Hutchinson, Elizabeth B; Rutecki, Paul A; Alexander, Andrew L; Sutula, Thomas P

2012-04-30

A statistical approach is presented for the quantitative analysis of diffusion tensor imaging (DTI) directional information using Fisher statistics, which were originally developed for the analysis of vectors in the field of paleomagnetism. In this framework, descriptive and inferential statistics have been formulated based on the Fisher probability density function, a spherical analogue of the normal distribution. The Fisher approach was evaluated for investigation of rat brain DTI maps to characterize tissue orientation in the corpus callosum, fornix, and hilus of the dorsal hippocampal dentate gyrus, and to compare directional properties in these regions following status epilepticus (SE) or traumatic brain injury (TBI) with values in healthy brains. Direction vectors were determined for each region of interest (ROI) for each brain sample and Fisher statistics were applied to calculate the mean direction vector and variance parameters in the corpus callosum, fornix, and dentate gyrus of normal rats and rats that experienced TBI or SE. Hypothesis testing was performed by calculation of Watson's F-statistic and associated p-value giving the likelihood that grouped observations were from the same directional distribution. In the fornix and midline corpus callosum, no directional differences were detected between groups, however in the hilus, significant (p<0.0005) differences were found that robustly confirmed observations that were suggested by visual inspection of directionally encoded color DTI maps. The Fisher approach is a potentially useful analysis tool that may extend the current capabilities of DTI investigation by providing a means of statistical comparison of tissue structural orientation. Copyright © 2012 Elsevier B.V. All rights reserved.

Compositional differences among Chinese soy sauce types studied by (13)C NMR spectroscopy coupled with multivariate statistical analysis.

PubMed

Kamal, Ghulam Mustafa; Wang, Xiaohua; Bin Yuan; Wang, Jie; Sun, Peng; Zhang, Xu; Liu, Maili

2016-09-01

Soy sauce a well known seasoning all over the world, especially in Asia, is available in global market in a wide range of types based on its purpose and the processing methods. Its composition varies with respect to the fermentation processes and addition of additives, preservatives and flavor enhancers. A comprehensive (1)H NMR based study regarding the metabonomic variations of soy sauce to differentiate among different types of soy sauce available on the global market has been limited due to the complexity of the mixture. In present study, (13)C NMR spectroscopy coupled with multivariate statistical data analysis like principle component analysis (PCA), and orthogonal partial least square-discriminant analysis (OPLS-DA) was applied to investigate metabonomic variations among different types of soy sauce, namely super light, super dark, red cooking and mushroom soy sauce. The main additives in soy sauce like glutamate, sucrose and glucose were easily distinguished and quantified using (13)C NMR spectroscopy which were otherwise difficult to be assigned and quantified due to serious signal overlaps in (1)H NMR spectra. The significantly higher concentration of sucrose in dark, red cooking and mushroom flavored soy sauce can directly be linked to the addition of caramel in soy sauce. Similarly, significantly higher level of glutamate in super light as compared to super dark and mushroom flavored soy sauce may come from the addition of monosodium glutamate. The study highlights the potentiality of (13)C NMR based metabonomics coupled with multivariate statistical data analysis in differentiating between the types of soy sauce on the basis of level of additives, raw materials and fermentation procedures. Copyright © 2016 Elsevier B.V. All rights reserved.

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.

Manipulating measurement scales in medical statistical analysis and data mining: A review of methodologies

PubMed Central

Marateb, Hamid Reza; Mansourian, Marjan; Adibi, Peyman; Farina, Dario

2014-01-01

Background: selecting the correct statistical test and data mining method depends highly on the measurement scale of data, type of variables, and purpose of the analysis. Different measurement scales are studied in details and statistical comparison, modeling, and data mining methods are studied based upon using several medical examples. We have presented two ordinal–variables clustering examples, as more challenging variable in analysis, using Wisconsin Breast Cancer Data (WBCD). Ordinal-to-Interval scale conversion example: a breast cancer database of nine 10-level ordinal variables for 683 patients was analyzed by two ordinal-scale clustering methods. The performance of the clustering methods was assessed by comparison with the gold standard groups of malignant and benign cases that had been identified by clinical tests. Results: the sensitivity and accuracy of the two clustering methods were 98% and 96%, respectively. Their specificity was comparable. Conclusion: by using appropriate clustering algorithm based on the measurement scale of the variables in the study, high performance is granted. Moreover, descriptive and inferential statistics in addition to modeling approach must be selected based on the scale of the variables. PMID:24672565

A Divergence Statistics Extension to VTK for Performance Analysis

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

Pebay, Philippe Pierre; Bennett, Janine Camille

This report follows the series of previous documents ([PT08, BPRT09b, PT09, BPT09, PT10, PB13], where we presented the parallel descriptive, correlative, multi-correlative, principal component analysis, contingency, k -means, order and auto-correlative statistics engines which we developed within the Visualization Tool Kit ( VTK ) as a scalable, parallel and versatile statistics package. We now report on a new engine which we developed for the calculation of divergence statistics, a concept which we hereafter explain and whose main goal is to quantify the discrepancy, in a stasticial manner akin to measuring a distance, between an observed empirical distribution and a theoretical,more » "ideal" one. The ease of use of the new diverence statistics engine is illustrated by the means of C++ code snippets. Although this new engine does not yet have a parallel implementation, it has already been applied to HPC performance analysis, of which we provide an example.« less

Descriptive Statistics and Cluster Analysis for Extreme Rainfall in Java Island

NASA Astrophysics Data System (ADS)

E Komalasari, K.; Pawitan, H.; Faqih, A.

2017-03-01

This study aims to describe regional pattern of extreme rainfall based on maximum daily rainfall for period 1983 to 2012 in Java Island. Descriptive statistics analysis was performed to obtain centralization, variation and distribution of maximum precipitation data. Mean and median are utilized to measure central tendency data while Inter Quartile Range (IQR) and standard deviation are utilized to measure variation of data. In addition, skewness and kurtosis used to obtain shape the distribution of rainfall data. Cluster analysis using squared euclidean distance and ward method is applied to perform regional grouping. Result of this study show that mean (average) of maximum daily rainfall in Java Region during period 1983-2012 is around 80-181mm with median between 75-160mm and standard deviation between 17 to 82. Cluster analysis produces four clusters and show that western area of Java tent to have a higher annual maxima of daily rainfall than northern area, and have more variety of annual maximum value.

APPLICATION OF STATISTICAL ENERGY ANALYSIS TO VIBRATIONS OF MULTI-PANEL STRUCTURES.

DTIC Science & Technology

cylindrical shell are compared with predictions obtained from statistical energy analysis . Generally good agreement is observed. The flow of mechanical...the coefficients of proportionality between power flow and average modal energy difference, which one must know in order to apply statistical energy analysis . No

Statistical Analysis of the Uncertainty in Pre-Flight Aerodynamic Database of a Hypersonic Vehicle

NASA Astrophysics Data System (ADS)

Huh, Lynn

The objective of the present research was to develop a new method to derive the aerodynamic coefficients and the associated uncertainties for flight vehicles via post- flight inertial navigation analysis using data from the inertial measurement unit. Statistical estimates of vehicle state and aerodynamic coefficients are derived using Monte Carlo simulation. Trajectory reconstruction using the inertial navigation system (INS) is a simple and well used method. However, deriving realistic uncertainties in the reconstructed state and any associated parameters is not so straight forward. Extended Kalman filters, batch minimum variance estimation and other approaches have been used. However, these methods generally depend on assumed physical models, assumed statistical distributions (usually Gaussian) or have convergence issues for non-linear problems. The approach here assumes no physical models, is applicable to any statistical distribution, and does not have any convergence issues. The new approach obtains the statistics directly from a sufficient number of Monte Carlo samples using only the generally well known gyro and accelerometer specifications and could be applied to the systems of non-linear form and non-Gaussian distribution. When redundant data are available, the set of Monte Carlo simulations are constrained to satisfy the redundant data within the uncertainties specified for the additional data. The proposed method was applied to validate the uncertainty in the pre-flight aerodynamic database of the X-43A Hyper-X research vehicle. In addition to gyro and acceleration data, the actual flight data include redundant measurements of position and velocity from the global positioning system (GPS). The criteria derived from the blend of the GPS and INS accuracy was used to select valid trajectories for statistical analysis. The aerodynamic coefficients were derived from the selected trajectories by either direct extraction method based on the equations in

Demonstration of a software design and statistical analysis methodology with application to patient outcomes data sets

PubMed Central

Mayo, Charles; Conners, Steve; Warren, Christopher; Miller, Robert; Court, Laurence; Popple, Richard

2013-01-01

Purpose: With emergence of clinical outcomes databases as tools utilized routinely within institutions, comes need for software tools to support automated statistical analysis of these large data sets and intrainstitutional exchange from independent federated databases to support data pooling. In this paper, the authors present a design approach and analysis methodology that addresses both issues. Methods: A software application was constructed to automate analysis of patient outcomes data using a wide range of statistical metrics, by combining use of C#.Net and R code. The accuracy and speed of the code was evaluated using benchmark data sets. Results: The approach provides data needed to evaluate combinations of statistical measurements for ability to identify patterns of interest in the data. Through application of the tools to a benchmark data set for dose-response threshold and to SBRT lung data sets, an algorithm was developed that uses receiver operator characteristic curves to identify a threshold value and combines use of contingency tables, Fisher exact tests, Welch t-tests, and Kolmogorov-Smirnov tests to filter the large data set to identify values demonstrating dose-response. Kullback-Leibler divergences were used to provide additional confirmation. Conclusions: The work demonstrates the viability of the design approach and the software tool for analysis of large data sets. PMID:24320426

Demonstration of a software design and statistical analysis methodology with application to patient outcomes data sets.

PubMed

Mayo, Charles; Conners, Steve; Warren, Christopher; Miller, Robert; Court, Laurence; Popple, Richard

2013-11-01

With emergence of clinical outcomes databases as tools utilized routinely within institutions, comes need for software tools to support automated statistical analysis of these large data sets and intrainstitutional exchange from independent federated databases to support data pooling. In this paper, the authors present a design approach and analysis methodology that addresses both issues. A software application was constructed to automate analysis of patient outcomes data using a wide range of statistical metrics, by combining use of C#.Net and R code. The accuracy and speed of the code was evaluated using benchmark data sets. The approach provides data needed to evaluate combinations of statistical measurements for ability to identify patterns of interest in the data. Through application of the tools to a benchmark data set for dose-response threshold and to SBRT lung data sets, an algorithm was developed that uses receiver operator characteristic curves to identify a threshold value and combines use of contingency tables, Fisher exact tests, Welch t-tests, and Kolmogorov-Smirnov tests to filter the large data set to identify values demonstrating dose-response. Kullback-Leibler divergences were used to provide additional confirmation. The work demonstrates the viability of the design approach and the software tool for analysis of large data sets.

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.

A κ-generalized statistical mechanics approach to income analysis

NASA Astrophysics Data System (ADS)

Clementi, F.; Gallegati, M.; Kaniadakis, G.

2009-02-01

This paper proposes a statistical mechanics approach to the analysis of income distribution and inequality. A new distribution function, having its roots in the framework of κ-generalized statistics, is derived that is particularly suitable for describing the whole spectrum of incomes, from the low-middle income region up to the high income Pareto power-law regime. Analytical expressions for the shape, moments and some other basic statistical properties are given. Furthermore, several well-known econometric tools for measuring inequality, which all exist in a closed form, are considered. A method for parameter estimation is also discussed. The model is shown to fit remarkably well the data on personal income for the United States, and the analysis of inequality performed in terms of its parameters is revealed as very powerful.

Protein Sectors: Statistical Coupling Analysis versus Conservation

PubMed Central

Teşileanu, Tiberiu; Colwell, Lucy J.; Leibler, Stanislas

2015-01-01

Statistical coupling analysis (SCA) is a method for analyzing multiple sequence alignments that was used to identify groups of coevolving residues termed “sectors”. The method applies spectral analysis to a matrix obtained by combining correlation information with sequence conservation. It has been asserted that the protein sectors identified by SCA are functionally significant, with different sectors controlling different biochemical properties of the protein. Here we reconsider the available experimental data and note that it involves almost exclusively proteins with a single sector. We show that in this case sequence conservation is the dominating factor in SCA, and can alone be used to make statistically equivalent functional predictions. Therefore, we suggest shifting the experimental focus to proteins for which SCA identifies several sectors. Correlations in protein alignments, which have been shown to be informative in a number of independent studies, would then be less dominated by sequence conservation. PMID:25723535

Normality Tests for Statistical Analysis: A Guide for Non-Statisticians

PubMed Central

Ghasemi, Asghar; Zahediasl, Saleh

2012-01-01

Statistical errors are common in scientific literature and about 50% of the published articles have at least one error. The assumption of normality needs to be checked for many statistical procedures, namely parametric tests, because their validity depends on it. The aim of this commentary is to overview checking for normality in statistical analysis using SPSS. PMID:23843808

Statistical analysis of Geopotential Height (GH) timeseries based on Tsallis non-extensive statistical mechanics

NASA Astrophysics Data System (ADS)

Karakatsanis, L. P.; Iliopoulos, A. C.; Pavlos, E. G.; Pavlos, G. P.

2018-02-01

In this paper, we perform statistical analysis of time series deriving from Earth's climate. The time series are concerned with Geopotential Height (GH) and correspond to temporal and spatial components of the global distribution of month average values, during the period (1948-2012). The analysis is based on Tsallis non-extensive statistical mechanics and in particular on the estimation of Tsallis' q-triplet, namely {qstat, qsens, qrel}, the reconstructed phase space and the estimation of correlation dimension and the Hurst exponent of rescaled range analysis (R/S). The deviation of Tsallis q-triplet from unity indicates non-Gaussian (Tsallis q-Gaussian) non-extensive character with heavy tails probability density functions (PDFs), multifractal behavior and long range dependences for all timeseries considered. Also noticeable differences of the q-triplet estimation found in the timeseries at distinct local or temporal regions. Moreover, in the reconstructive phase space revealed a lower-dimensional fractal set in the GH dynamical phase space (strong self-organization) and the estimation of Hurst exponent indicated multifractality, non-Gaussianity and persistence. The analysis is giving significant information identifying and characterizing the dynamical characteristics of the earth's climate.

Entropy in statistical energy analysis.

PubMed

Le Bot, Alain

2009-03-01

In this paper, the second principle of thermodynamics is discussed in the framework of statistical energy analysis (SEA). It is shown that the "vibrational entropy" and the "vibrational temperature" of sub-systems only depend on the vibrational energy and the number of resonant modes. A SEA system can be described as a thermodynamic system slightly out of equilibrium. In steady-state condition, the entropy exchanged with exterior by sources and dissipation exactly balances the production of entropy by irreversible processes at interface between SEA sub-systems.

Development of computer-assisted instruction application for statistical data analysis android platform as learning resource

NASA Astrophysics Data System (ADS)

Hendikawati, P.; Arifudin, R.; Zahid, M. Z.

2018-03-01

This study aims to design an android Statistics Data Analysis application that can be accessed through mobile devices to making it easier for users to access. The Statistics Data Analysis application includes various topics of basic statistical along with a parametric statistics data analysis application. The output of this application system is parametric statistics data analysis that can be used for students, lecturers, and users who need the results of statistical calculations quickly and easily understood. Android application development is created using Java programming language. The server programming language uses PHP with the Code Igniter framework, and the database used MySQL. The system development methodology used is the Waterfall methodology with the stages of analysis, design, coding, testing, and implementation and system maintenance. This statistical data analysis application is expected to support statistical lecturing activities and make students easier to understand the statistical analysis of mobile devices.

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

Additive effects in high-voltage layered-oxide cells: A statistics of mixtures approach

DOE PAGES

Sahore, Ritu; Peebles, Cameron; Abraham, Daniel P.; ...

2017-07-20

Li 1.03(Ni 0.5Mn 0.3Co 0.2) 0.97O 2 (NMC)-based coin cells containing the electrolyte additives vinylene carbonate (VC) and tris(trimethylsilyl)phosphite (TMSPi) in the range of 0-2 wt% were cycled between 3.0 and 4.4 V. The changes in capacity at rates of C/10 and C/1 and resistance at 60% state of charge were found to follow linear-with-time kinetic rate laws. Further, the C/10 capacity and resistance data were amenable to modeling by a statistics of mixtures approach. Applying physical meaning to the terms in the empirical models indicated that the interactions between the electrolyte and additives were not simple. For example, theremore » were strong, synergistic interactions between VC and TMSPi affecting C/10 capacity loss, as expected, but there were other, more subtle interactions between the electrolyte components. In conclusion, the interactions between these components controlled the C/10 capacity decline and resistance increase.« less

Bayesian Statistics for Biological Data: Pedigree Analysis

ERIC Educational Resources Information Center

Stanfield, William D.; Carlton, Matthew A.

2004-01-01

The use of Bayes' formula is applied to the biological problem of pedigree analysis to show that the Bayes' formula and non-Bayesian or "classical" methods of probability calculation give different answers. First year college students of biology can be introduced to the Bayesian statistics.

Statistical Analysis of Protein Ensembles

NASA Astrophysics Data System (ADS)

Máté, Gabriell; Heermann, Dieter

2014-04-01

As 3D protein-configuration data is piling up, there is an ever-increasing need for well-defined, mathematically rigorous analysis approaches, especially that the vast majority of the currently available methods rely heavily on heuristics. We propose an analysis framework which stems from topology, the field of mathematics which studies properties preserved under continuous deformations. First, we calculate a barcode representation of the molecules employing computational topology algorithms. Bars in this barcode represent different topological features. Molecules are compared through their barcodes by statistically determining the difference in the set of their topological features. As a proof-of-principle application, we analyze a dataset compiled of ensembles of different proteins, obtained from the Ensemble Protein Database. We demonstrate that our approach correctly detects the different protein groupings.

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

Technical Note: The Initial Stages of Statistical Data Analysis

PubMed Central

Tandy, Richard D.

1998-01-01

Objective: To provide an overview of several important data-related considerations in the design stage of a research project and to review the levels of measurement and their relationship to the statistical technique chosen for the data analysis. Background: When planning a study, the researcher must clearly define the research problem and narrow it down to specific, testable questions. The next steps are to identify the variables in the study, decide how to group and treat subjects, and determine how to measure, and the underlying level of measurement of, the dependent variables. Then the appropriate statistical technique can be selected for data analysis. Description: The four levels of measurement in increasing complexity are nominal, ordinal, interval, and ratio. Nominal data are categorical or “count” data, and the numbers are treated as labels. Ordinal data can be ranked in a meaningful order by magnitude. Interval data possess the characteristics of ordinal data and also have equal distances between levels. Ratio data have a natural zero point. Nominal and ordinal data are analyzed with nonparametric statistical techniques and interval and ratio data with parametric statistical techniques. Advantages: Understanding the four levels of measurement and when it is appropriate to use each is important in determining which statistical technique to use when analyzing data. PMID:16558489

Statistical analysis of RHIC beam position monitors performance

NASA Astrophysics Data System (ADS)

Calaga, R.; Tomás, R.

2004-04-01

A detailed statistical analysis of beam position monitors (BPM) performance at RHIC is a critical factor in improving regular operations and future runs. Robust identification of malfunctioning BPMs plays an important role in any orbit or turn-by-turn analysis. Singular value decomposition and Fourier transform methods, which have evolved as powerful numerical techniques in signal processing, will aid in such identification from BPM data. This is the first attempt at RHIC to use a large set of data to statistically enhance the capability of these two techniques and determine BPM performance. A comparison from run 2003 data shows striking agreement between the two methods and hence can be used to improve BPM functioning at RHIC and possibly other accelerators.

Statistics Education Research in Malaysia and the Philippines: A Comparative Analysis

ERIC Educational Resources Information Center

Reston, Enriqueta; Krishnan, Saras; Idris, Noraini

2014-01-01

This paper presents a comparative analysis of statistics education research in Malaysia and the Philippines by modes of dissemination, research areas, and trends. An electronic search for published research papers in the area of statistics education from 2000-2012 yielded 20 for Malaysia and 19 for the Philippines. Analysis of these papers showed…

IVHS Countermeasures for Rear-End Collisions, Task 1; Vol. II: Statistical Analysis

DOT National Transportation Integrated Search

1994-02-25

This report is from the NHTSA sponsored program, "IVHS Countermeasures for Rear-End Collisions". This Volume, Volume II, Statistical Analysis, presents the statistical analysis of rear-end collision accident data that characterizes the accidents with...

Statistical Learning in Specific Language Impairment: A Meta-Analysis

ERIC Educational Resources Information Center

Lammertink, Imme; Boersma, Paul; Wijnen, Frank; Rispens, Judith

2017-01-01

Purpose: The current meta-analysis provides a quantitative overview of published and unpublished studies on statistical learning in the auditory verbal domain in people with and without specific language impairment (SLI). The database used for the meta-analysis is accessible online and open to updates (Community-Augmented Meta-Analysis), which…

Analysis of statistical misconception in terms of statistical reasoning

NASA Astrophysics Data System (ADS)

Maryati, I.; Priatna, N.

2018-05-01

Reasoning skill is needed for everyone to face globalization era, because every person have to be able to manage and use information from all over the world which can be obtained easily. Statistical reasoning skill is the ability to collect, group, process, interpret, and draw conclusion of information. Developing this skill can be done through various levels of education. However, the skill is low because many people assume that statistics is just the ability to count and using formulas and so do students. Students still have negative attitude toward course which is related to research. The purpose of this research is analyzing students’ misconception in descriptive statistic course toward the statistical reasoning skill. The observation was done by analyzing the misconception test result and statistical reasoning skill test; observing the students’ misconception effect toward statistical reasoning skill. The sample of this research was 32 students of math education department who had taken descriptive statistic course. The mean value of misconception test was 49,7 and standard deviation was 10,6 whereas the mean value of statistical reasoning skill test was 51,8 and standard deviation was 8,5. If the minimal value is 65 to state the standard achievement of a course competence, students’ mean value is lower than the standard competence. The result of students’ misconception study emphasized on which sub discussion that should be considered. Based on the assessment result, it was found that students’ misconception happen on this: 1) writing mathematical sentence and symbol well, 2) understanding basic definitions, 3) determining concept that will be used in solving problem. In statistical reasoning skill, the assessment was done to measure reasoning from: 1) data, 2) representation, 3) statistic format, 4) probability, 5) sample, and 6) association.

Application of the Statistical ICA Technique in the DANCE Data Analysis

NASA Astrophysics Data System (ADS)

Baramsai, Bayarbadrakh; Jandel, M.; Bredeweg, T. A.; Rusev, G.; Walker, C. L.; Couture, A.; Mosby, S.; Ullmann, J. L.; Dance Collaboration

2015-10-01

The Detector for Advanced Neutron Capture Experiments (DANCE) at the Los Alamos Neutron Science Center is used to improve our understanding of the neutron capture reaction. DANCE is a highly efficient 4 π γ-ray detector array consisting of 160 BaF2 crystals which make it an ideal tool for neutron capture experiments. The (n, γ) reaction Q-value equals to the sum energy of all γ-rays emitted in the de-excitation cascades from the excited capture state to the ground state. The total γ-ray energy is used to identify reactions on different isotopes as well as the background. However, it's challenging to identify contribution in the Esum spectra from different isotopes with the similar Q-values. Recently we have tested the applicability of modern statistical methods such as Independent Component Analysis (ICA) to identify and separate different (n, γ) reaction yields on different isotopes that are present in the target material. ICA is a recently developed computational tool for separating multidimensional data into statistically independent additive subcomponents. In this conference talk, we present some results of the application of ICA algorithms and its modification for the DANCE experimental data analysis. This research is supported by the U. S. Department of Energy, Office of Science, Nuclear Physics under the Early Career Award No. LANL20135009.

Statistical analysis of the national crash severity study data

DOT National Transportation Integrated Search

1980-08-01

This is the Final Report on a two-year statistical analysis of the data collected in the National Crash Severity Study (NCSS). The analysis presented is primarily concerned with the relationship between occupant injury severity and the crash conditio...

Visual and Statistical Analysis of Digital Elevation Models Generated Using Idw Interpolator with Varying Powers

NASA Astrophysics Data System (ADS)

Asal, F. F.

2012-07-01

Digital elevation data obtained from different Engineering Surveying techniques is utilized in generating Digital Elevation Model (DEM), which is employed in many Engineering and Environmental applications. This data is usually in discrete point format making it necessary to utilize an interpolation approach for the creation of DEM. Quality assessment of the DEM is a vital issue controlling its use in different applications; however this assessment relies heavily on statistical methods with neglecting the visual methods. The research applies visual analysis investigation on DEMs generated using IDW interpolator of varying powers in order to examine their potential in the assessment of the effects of the variation of the IDW power on the quality of the DEMs. Real elevation data has been collected from field using total station instrument in a corrugated terrain. DEMs have been generated from the data at a unified cell size using IDW interpolator with power values ranging from one to ten. Visual analysis has been undertaken using 2D and 3D views of the DEM; in addition, statistical analysis has been performed for assessment of the validity of the visual techniques in doing such analysis. Visual analysis has shown that smoothing of the DEM decreases with the increase in the power value till the power of four; however, increasing the power more than four does not leave noticeable changes on 2D and 3D views of the DEM. The statistical analysis has supported these results where the value of the Standard Deviation (SD) of the DEM has increased with increasing the power. More specifically, changing the power from one to two has produced 36% of the total increase (the increase in SD due to changing the power from one to ten) in SD and changing to the powers of three and four has given 60% and 75% respectively. This refers to decrease in DEM smoothing with the increase in the power of the IDW. The study also has shown that applying visual methods supported by statistical

Comparative analysis of positive and negative attitudes toward statistics

NASA Astrophysics Data System (ADS)

Ghulami, Hassan Rahnaward; Ab Hamid, Mohd Rashid; Zakaria, Roslinazairimah

2015-02-01

Many statistics lecturers and statistics education researchers are interested to know the perception of their students' attitudes toward statistics during the statistics course. In statistics course, positive attitude toward statistics is a vital because it will be encourage students to get interested in the statistics course and in order to master the core content of the subject matters under study. Although, students who have negative attitudes toward statistics they will feel depressed especially in the given group assignment, at risk for failure, are often highly emotional, and could not move forward. Therefore, this study investigates the students' attitude towards learning statistics. Six latent constructs have been the measurement of students' attitudes toward learning statistic such as affect, cognitive competence, value, difficulty, interest, and effort. The questionnaire was adopted and adapted from the reliable and validate instrument of Survey of Attitudes towards Statistics (SATS). This study is conducted among engineering undergraduate engineering students in the university Malaysia Pahang (UMP). The respondents consist of students who were taking the applied statistics course from different faculties. From the analysis, it is found that the questionnaire is acceptable and the relationships among the constructs has been proposed and investigated. In this case, students show full effort to master the statistics course, feel statistics course enjoyable, have confidence that they have intellectual capacity, and they have more positive attitudes then negative attitudes towards statistics learning. In conclusion in terms of affect, cognitive competence, value, interest and effort construct the positive attitude towards statistics was mostly exhibited. While negative attitudes mostly exhibited by difficulty construct.

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

Imaging mass spectrometry statistical analysis.

PubMed

Jones, Emrys A; Deininger, Sören-Oliver; Hogendoorn, Pancras C W; Deelder, André M; McDonnell, Liam A

2012-08-30

Imaging mass spectrometry is increasingly used to identify new candidate biomarkers. This clinical application of imaging mass spectrometry is highly multidisciplinary: expertise in mass spectrometry is necessary to acquire high quality data, histology is required to accurately label the origin of each pixel's mass spectrum, disease biology is necessary to understand the potential meaning of the imaging mass spectrometry results, and statistics to assess the confidence of any findings. Imaging mass spectrometry data analysis is further complicated because of the unique nature of the data (within the mass spectrometry field); several of the assumptions implicit in the analysis of LC-MS/profiling datasets are not applicable to imaging. The very large size of imaging datasets and the reporting of many data analysis routines, combined with inadequate training and accessible reviews, have exacerbated this problem. In this paper we provide an accessible review of the nature of imaging data and the different strategies by which the data may be analyzed. Particular attention is paid to the assumptions of the data analysis routines to ensure that the reader is apprised of their correct usage in imaging mass spectrometry research. Copyright © 2012 Elsevier B.V. All rights reserved.

Asymptotic modal analysis and statistical energy analysis

NASA Technical Reports Server (NTRS)

Dowell, Earl H.

1988-01-01

Statistical Energy Analysis (SEA) is defined by considering the asymptotic limit of Classical Modal Analysis, an approach called Asymptotic Modal Analysis (AMA). The general approach is described for both structural and acoustical systems. The theoretical foundation is presented for structural systems, and experimental verification is presented for a structural plate responding to a random force. Work accomplished subsequent to the grant initiation focusses on the acoustic response of an interior cavity (i.e., an aircraft or spacecraft fuselage) with a portion of the wall vibrating in a large number of structural modes. First results were presented at the ASME Winter Annual Meeting in December, 1987, and accepted for publication in the Journal of Vibration, Acoustics, Stress and Reliability in Design. It is shown that asymptotically as the number of acoustic modes excited becomes large, the pressure level in the cavity becomes uniform except at the cavity boundaries. However, the mean square pressure at the cavity corner, edge and wall is, respectively, 8, 4, and 2 times the value in the cavity interior. Also it is shown that when the portion of the wall which is vibrating is near a cavity corner or edge, the response is significantly higher.

Rating locomotive crew diesel emission exposure profiles using statistics and Bayesian Decision Analysis.

PubMed

Hewett, Paul; Bullock, William H

2014-01-01

For more than 20 years CSX Transportation (CSXT) has collected exposure measurements from locomotive engineers and conductors who are potentially exposed to diesel emissions. The database included measurements for elemental and total carbon, polycyclic aromatic hydrocarbons, aromatics, aldehydes, carbon monoxide, and nitrogen dioxide. This database was statistically analyzed and summarized, and the resulting statistics and exposure profiles were compared to relevant occupational exposure limits (OELs) using both parametric and non-parametric descriptive and compliance statistics. Exposure ratings, using the American Industrial Health Association (AIHA) exposure categorization scheme, were determined using both the compliance statistics and Bayesian Decision Analysis (BDA). The statistical analysis of the elemental carbon data (a marker for diesel particulate) strongly suggests that the majority of levels in the cabs of the lead locomotives (n = 156) were less than the California guideline of 0.020 mg/m(3). The sample 95th percentile was roughly half the guideline; resulting in an AIHA exposure rating of category 2/3 (determined using BDA). The elemental carbon (EC) levels in the trailing locomotives tended to be greater than those in the lead locomotive; however, locomotive crews rarely ride in the trailing locomotive. Lead locomotive EC levels were similar to those reported by other investigators studying locomotive crew exposures and to levels measured in urban areas. Lastly, both the EC sample mean and 95%UCL were less than the Environmental Protection Agency (EPA) reference concentration of 0.005 mg/m(3). With the exception of nitrogen dioxide, the overwhelming majority of the measurements for total carbon, polycyclic aromatic hydrocarbons, aromatics, aldehydes, and combustion gases in the cabs of CSXT locomotives were either non-detects or considerably less than the working OELs for the years represented in the database. When compared to the previous American

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.

Applied Behavior Analysis and Statistical Process Control?

ERIC Educational Resources Information Center

Hopkins, B. L.

1995-01-01

Incorporating statistical process control (SPC) methods into applied behavior analysis is discussed. It is claimed that SPC methods would likely reduce applied behavior analysts' intimate contacts with problems and would likely yield poor treatment and research decisions. Cases and data presented by Pfadt and Wheeler (1995) are cited as examples.…

Statistical Analysis Experiment for Freshman Chemistry Lab.

ERIC Educational Resources Information Center

Salzsieder, John C.

1995-01-01

Describes a laboratory experiment dissolving zinc from galvanized nails in which data can be gathered very quickly for statistical analysis. The data have sufficient significant figures and the experiment yields a nice distribution of random errors. Freshman students can gain an appreciation of the relationships between random error, number of…

Assessing Statistically Significant Heavy-Metal Concentrations in Abandoned Mine Areas via Hot Spot Analysis of Portable XRF Data

PubMed Central

Kim, Sung-Min; Choi, Yosoon

2017-01-01

To develop appropriate measures to prevent soil contamination in abandoned mining areas, an understanding of the spatial variation of the potentially toxic trace elements (PTEs) in the soil is necessary. For the purpose of effective soil sampling, this study uses hot spot analysis, which calculates a z-score based on the Getis-Ord Gi* statistic to identify a statistically significant hot spot sample. To constitute a statistically significant hot spot, a feature with a high value should also be surrounded by other features with high values. Using relatively cost- and time-effective portable X-ray fluorescence (PXRF) analysis, sufficient input data are acquired from the Busan abandoned mine and used for hot spot analysis. To calibrate the PXRF data, which have a relatively low accuracy, the PXRF analysis data are transformed using the inductively coupled plasma atomic emission spectrometry (ICP-AES) data. The transformed PXRF data of the Busan abandoned mine are classified into four groups according to their normalized content and z-scores: high content with a high z-score (HH), high content with a low z-score (HL), low content with a high z-score (LH), and low content with a low z-score (LL). The HL and LH cases may be due to measurement errors. Additional or complementary surveys are required for the areas surrounding these suspect samples or for significant hot spot areas. The soil sampling is conducted according to a four-phase procedure in which the hot spot analysis and proposed group classification method are employed to support the development of a sampling plan for the following phase. Overall, 30, 50, 80, and 100 samples are investigated and analyzed in phases 1–4, respectively. The method implemented in this case study may be utilized in the field for the assessment of statistically significant soil contamination and the identification of areas for which an additional survey is required. PMID:28629168

Assessing Statistically Significant Heavy-Metal Concentrations in Abandoned Mine Areas via Hot Spot Analysis of Portable XRF Data.

PubMed

Kim, Sung-Min; Choi, Yosoon

2017-06-18

To develop appropriate measures to prevent soil contamination in abandoned mining areas, an understanding of the spatial variation of the potentially toxic trace elements (PTEs) in the soil is necessary. For the purpose of effective soil sampling, this study uses hot spot analysis, which calculates a z -score based on the Getis-Ord Gi* statistic to identify a statistically significant hot spot sample. To constitute a statistically significant hot spot, a feature with a high value should also be surrounded by other features with high values. Using relatively cost- and time-effective portable X-ray fluorescence (PXRF) analysis, sufficient input data are acquired from the Busan abandoned mine and used for hot spot analysis. To calibrate the PXRF data, which have a relatively low accuracy, the PXRF analysis data are transformed using the inductively coupled plasma atomic emission spectrometry (ICP-AES) data. The transformed PXRF data of the Busan abandoned mine are classified into four groups according to their normalized content and z -scores: high content with a high z -score (HH), high content with a low z -score (HL), low content with a high z -score (LH), and low content with a low z -score (LL). The HL and LH cases may be due to measurement errors. Additional or complementary surveys are required for the areas surrounding these suspect samples or for significant hot spot areas. The soil sampling is conducted according to a four-phase procedure in which the hot spot analysis and proposed group classification method are employed to support the development of a sampling plan for the following phase. Overall, 30, 50, 80, and 100 samples are investigated and analyzed in phases 1-4, respectively. The method implemented in this case study may be utilized in the field for the assessment of statistically significant soil contamination and the identification of areas for which an additional survey is required.

Applied statistical training to strengthen analysis and health research capacity in Rwanda.

PubMed

Thomson, Dana R; Semakula, Muhammed; Hirschhorn, Lisa R; Murray, Megan; Ndahindwa, Vedaste; Manzi, Anatole; Mukabutera, Assumpta; Karema, Corine; Condo, Jeanine; Hedt-Gauthier, Bethany

2016-09-29

To guide efficient investment of limited health resources in sub-Saharan Africa, local researchers need to be involved in, and guide, health system and policy research. While extensive survey and census data are available to health researchers and program officers in resource-limited countries, local involvement and leadership in research is limited due to inadequate experience, lack of dedicated research time and weak interagency connections, among other challenges. Many research-strengthening initiatives host prolonged fellowships out-of-country, yet their approaches have not been evaluated for effectiveness in involvement and development of local leadership in research. We developed, implemented and evaluated a multi-month, deliverable-driven, survey analysis training based in Rwanda to strengthen skills of five local research leaders, 15 statisticians, and a PhD candidate. Research leaders applied with a specific research question relevant to country challenges and committed to leading an analysis to publication. Statisticians with prerequisite statistical training and experience with a statistical software applied to participate in class-based trainings and complete an assigned analysis. Both statisticians and research leaders were provided ongoing in-country mentoring for analysis and manuscript writing. Participants reported a high level of skill, knowledge and collaborator development from class-based trainings and out-of-class mentorship that were sustained 1 year later. Five of six manuscripts were authored by multi-institution teams and submitted to international peer-reviewed scientific journals, and three-quarters of the participants mentored others in survey data analysis or conducted an additional survey analysis in the year following the training. Our model was effective in utilizing existing survey data and strengthening skills among full-time working professionals without disrupting ongoing work commitments and using few resources. Critical to our

MICROARRAY DATA ANALYSIS USING MULTIPLE STATISTICAL MODELS

EPA Science Inventory

Microarray Data Analysis Using Multiple Statistical Models

Wenjun Bao1, Judith E. Schmid1, Amber K. Goetz1, Ming Ouyang2, William J. Welsh2,Andrew I. Brooks3,4, ChiYi Chu3,Mitsunori Ogihara3,4, Yinhe Cheng5, David J. Dix1. 1National Health and Environmental Effects Researc...

TRAPR: R Package for Statistical Analysis and Visualization of RNA-Seq Data.

PubMed

Lim, Jae Hyun; Lee, Soo Youn; Kim, Ju Han

2017-03-01

High-throughput transcriptome sequencing, also known as RNA sequencing (RNA-Seq), is a standard technology for measuring gene expression with unprecedented accuracy. Numerous bioconductor packages have been developed for the statistical analysis of RNA-Seq data. However, these tools focus on specific aspects of the data analysis pipeline, and are difficult to appropriately integrate with one another due to their disparate data structures and processing methods. They also lack visualization methods to confirm the integrity of the data and the process. In this paper, we propose an R-based RNA-Seq analysis pipeline called TRAPR, an integrated tool that facilitates the statistical analysis and visualization of RNA-Seq expression data. TRAPR provides various functions for data management, the filtering of low-quality data, normalization, transformation, statistical analysis, data visualization, and result visualization that allow researchers to build customized analysis pipelines.

Analysis methodology and development of a statistical tool for biodistribution data from internal contamination with actinides.

PubMed

Lamart, Stephanie; Griffiths, Nina M; Tchitchek, Nicolas; Angulo, Jaime F; Van der Meeren, Anne

2017-03-01

The aim of this work was to develop a computational tool that integrates several statistical analysis features for biodistribution data from internal contamination experiments. These data represent actinide levels in biological compartments as a function of time and are derived from activity measurements in tissues and excreta. These experiments aim at assessing the influence of different contamination conditions (e.g. intake route or radioelement) on the biological behavior of the contaminant. The ever increasing number of datasets and diversity of experimental conditions make the handling and analysis of biodistribution data difficult. This work sought to facilitate the statistical analysis of a large number of datasets and the comparison of results from diverse experimental conditions. Functional modules were developed using the open-source programming language R to facilitate specific operations: descriptive statistics, visual comparison, curve fitting, and implementation of biokinetic models. In addition, the structure of the datasets was harmonized using the same table format. Analysis outputs can be written in text files and updated data can be written in the consistent table format. Hence, a data repository is built progressively, which is essential for the optimal use of animal data. Graphical representations can be automatically generated and saved as image files. The resulting computational tool was applied using data derived from wound contamination experiments conducted under different conditions. In facilitating biodistribution data handling and statistical analyses, this computational tool ensures faster analyses and a better reproducibility compared with the use of multiple office software applications. Furthermore, re-analysis of archival data and comparison of data from different sources is made much easier. Hence this tool will help to understand better the influence of contamination characteristics on actinide biokinetics. Our approach can aid

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.

The Importance of Statistical Modeling in Data Analysis and Inference

ERIC Educational Resources Information Center

Rollins, Derrick, Sr.

2017-01-01

Statistical inference simply means to draw a conclusion based on information that comes from data. Error bars are the most commonly used tool for data analysis and inference in chemical engineering data studies. This work demonstrates, using common types of data collection studies, the importance of specifying the statistical model for sound…

Explorations in Statistics: The Analysis of Ratios and Normalized Data

ERIC Educational Resources Information Center

Curran-Everett, Douglas

2013-01-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This ninth installment of "Explorations in Statistics" explores the analysis of ratios and normalized--or standardized--data. As researchers, we compute a ratio--a numerator divided by a denominator--to compute a…

Statistical Energy Analysis (SEA) and Energy Finite Element Analysis (EFEA) Predictions for a Floor-Equipped Composite Cylinder

NASA Technical Reports Server (NTRS)

Grosveld, Ferdinand W.; Schiller, Noah H.; Cabell, Randolph H.

2011-01-01

Comet Enflow is a commercially available, high frequency vibroacoustic analysis software founded on Energy Finite Element Analysis (EFEA) and Energy Boundary Element Analysis (EBEA). Energy Finite Element Analysis (EFEA) was validated on a floor-equipped composite cylinder by comparing EFEA vibroacoustic response predictions with Statistical Energy Analysis (SEA) and experimental results. Statistical Energy Analysis (SEA) predictions were made using the commercial software program VA One 2009 from ESI Group. The frequency region of interest for this study covers the one-third octave bands with center frequencies from 100 Hz to 4000 Hz.

Statistical issues on the analysis of change in follow-up studies in dental research.

PubMed

Blance, Andrew; Tu, Yu-Kang; Baelum, Vibeke; Gilthorpe, Mark S

2007-12-01

To provide an overview to the problems in study design and associated analyses of follow-up studies in dental research, particularly addressing three issues: treatment-baselineinteractions; statistical power; and nonrandomization. Our previous work has shown that many studies purport an interacion between change (from baseline) and baseline values, which is often based on inappropriate statistical analyses. A priori power calculations are essential for randomized controlled trials (RCTs), but in the pre-test/post-test RCT design it is not well known to dental researchers that the choice of statistical method affects power, and that power is affected by treatment-baseline interactions. A common (good) practice in the analysis of RCT data is to adjust for baseline outcome values using ancova, thereby increasing statistical power. However, an important requirement for ancova is there to be no interaction between the groups and baseline outcome (i.e. effective randomization); the patient-selection process should not cause differences in mean baseline values across groups. This assumption is often violated for nonrandomized (observational) studies and the use of ancova is thus problematic, potentially giving biased estimates, invoking Lord's paradox and leading to difficulties in the interpretation of results. Baseline interaction issues can be overcome by use of statistical methods; not widely practiced in dental research: Oldham's method and multilevel modelling; the latter is preferred for its greater flexibility to deal with more than one follow-up occasion as well as additional covariates To illustrate these three key issues, hypothetical examples are considered from the fields of periodontology, orthodontics, and oral implantology. Caution needs to be exercised when considering the design and analysis of follow-up studies. ancova is generally inappropriate for nonrandomized studies and causal inferences from observational data should be avoided.

Coupling strength assumption in statistical energy analysis

PubMed Central

Lafont, T.; Totaro, N.

2017-01-01

This paper is a discussion of the hypothesis of weak coupling in statistical energy analysis (SEA). The examples of coupled oscillators and statistical ensembles of coupled plates excited by broadband random forces are discussed. In each case, a reference calculation is compared with the SEA calculation. First, it is shown that the main SEA relation, the coupling power proportionality, is always valid for two oscillators irrespective of the coupling strength. But the case of three subsystems, consisting of oscillators or ensembles of plates, indicates that the coupling power proportionality fails when the coupling is strong. Strong coupling leads to non-zero indirect coupling loss factors and, sometimes, even to a reversal of the energy flow direction from low to high vibrational temperature. PMID:28484335

[Statistical analysis of German radiologic periodicals: developmental trends in the last 10 years].

PubMed

Golder, W

1999-09-01

To identify which statistical tests are applied in German radiological publications, to what extent their use has changed during the last decade, and which factors might be responsible for this development. The major articles published in "ROFO" and "DER RADIOLOGE" during 1988, 1993 and 1998 were reviewed for statistical content. The contributions were classified by principal focus and radiological subspecialty. The methods used were assigned to descriptive, basal and advanced statistics. Sample size, significance level and power were established. The use of experts' assistance was monitored. Finally, we calculated the so-called cumulative accessibility of the publications. 525 contributions were found to be eligible. In 1988, 87% used descriptive statistics only, 12.5% basal, and 0.5% advanced statistics. The corresponding figures in 1993 and 1998 are 62 and 49%, 32 and 41%, and 6 and 10%, respectively. Statistical techniques were most likely to be used in research on musculoskeletal imaging and articles dedicated to MRI. Six basic categories of statistical methods account for the complete statistical analysis appearing in 90% of the articles. ROC analysis is the single most common advanced technique. Authors make increasingly use of statistical experts' opinion and programs. During the last decade, the use of statistical methods in German radiological journals has fundamentally improved, both quantitatively and qualitatively. Presently, advanced techniques account for 20% of the pertinent statistical tests. This development seems to be promoted by the increasing availability of statistical analysis software.

Analysis of thrips distribution: application of spatial statistics and Kriging

Treesearch

John Aleong; Bruce L. Parker; Margaret Skinner; Diantha Howard

1991-01-01

Kriging is a statistical technique that provides predictions for spatially and temporally correlated data. Observations of thrips distribution and density in Vermont soils are made in both space and time. Traditional statistical analysis of such data assumes that the counts taken over space and time are independent, which is not necessarily true. Therefore, to analyze...

Statistical wind analysis for near-space applications

NASA Astrophysics Data System (ADS)

Roney, Jason A.

2007-09-01

Statistical wind models were developed based on the existing observational wind data for near-space altitudes between 60 000 and 100 000 ft (18 30 km) above ground level (AGL) at two locations, Akon, OH, USA, and White Sands, NM, USA. These two sites are envisioned as playing a crucial role in the first flights of high-altitude airships. The analysis shown in this paper has not been previously applied to this region of the stratosphere for such an application. Standard statistics were compiled for these data such as mean, median, maximum wind speed, and standard deviation, and the data were modeled with Weibull distributions. These statistics indicated, on a yearly average, there is a lull or a “knee” in the wind between 65 000 and 72 000 ft AGL (20 22 km). From the standard statistics, trends at both locations indicated substantial seasonal variation in the mean wind speed at these heights. The yearly and monthly statistical modeling indicated that Weibull distributions were a reasonable model for the data. Forecasts and hindcasts were done by using a Weibull model based on 2004 data and comparing the model with the 2003 and 2005 data. The 2004 distribution was also a reasonable model for these years. Lastly, the Weibull distribution and cumulative function were used to predict the 50%, 95%, and 99% winds, which are directly related to the expected power requirements of a near-space station-keeping airship. These values indicated that using only the standard deviation of the mean may underestimate the operational conditions.

Load Model Verification, Validation and Calibration Framework by Statistical Analysis on Field Data

NASA Astrophysics Data System (ADS)

Jiao, Xiangqing; Liao, Yuan; Nguyen, Thai

2017-11-01

Accurate load models are critical for power system analysis and operation. A large amount of research work has been done on load modeling. Most of the existing research focuses on developing load models, while little has been done on developing formal load model verification and validation (V&V) methodologies or procedures. Most of the existing load model validation is based on qualitative rather than quantitative analysis. In addition, not all aspects of model V&V problem have been addressed by the existing approaches. To complement the existing methods, this paper proposes a novel load model verification and validation framework that can systematically and more comprehensively examine load model's effectiveness and accuracy. Statistical analysis, instead of visual check, quantifies the load model's accuracy, and provides a confidence level of the developed load model for model users. The analysis results can also be used to calibrate load models. The proposed framework can be used as a guidance to systematically examine load models for utility engineers and researchers. The proposed method is demonstrated through analysis of field measurements collected from a utility system.

Propensity Score Analysis: An Alternative Statistical Approach for HRD Researchers

ERIC Educational Resources Information Center

Keiffer, Greggory L.; Lane, Forrest C.

2016-01-01

Purpose: This paper aims to introduce matching in propensity score analysis (PSA) as an alternative statistical approach for researchers looking to make causal inferences using intact groups. Design/methodology/approach: An illustrative example demonstrated the varying results of analysis of variance, analysis of covariance and PSA on a heuristic…

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

Wastewater-Based Epidemiology of Stimulant Drugs: Functional Data Analysis Compared to Traditional Statistical Methods.

PubMed

Salvatore, Stefania; Bramness, Jørgen Gustav; Reid, Malcolm J; Thomas, Kevin Victor; Harman, Christopher; Røislien, Jo

2015-01-01

Wastewater-based epidemiology (WBE) is a new methodology for estimating the drug load in a population. Simple summary statistics and specification tests have typically been used to analyze WBE data, comparing differences between weekday and weekend loads. Such standard statistical methods may, however, overlook important nuanced information in the data. In this study, we apply functional data analysis (FDA) to WBE data and compare the results to those obtained from more traditional summary measures. We analysed temporal WBE data from 42 European cities, using sewage samples collected daily for one week in March 2013. For each city, the main temporal features of two selected drugs were extracted using functional principal component (FPC) analysis, along with simpler measures such as the area under the curve (AUC). The individual cities' scores on each of the temporal FPCs were then used as outcome variables in multiple linear regression analysis with various city and country characteristics as predictors. The results were compared to those of functional analysis of variance (FANOVA). The three first FPCs explained more than 99% of the temporal variation. The first component (FPC1) represented the level of the drug load, while the second and third temporal components represented the level and the timing of a weekend peak. AUC was highly correlated with FPC1, but other temporal characteristic were not captured by the simple summary measures. FANOVA was less flexible than the FPCA-based regression, and even showed concordance results. Geographical location was the main predictor for the general level of the drug load. FDA of WBE data extracts more detailed information about drug load patterns during the week which are not identified by more traditional statistical methods. Results also suggest that regression based on FPC results is a valuable addition to FANOVA for estimating associations between temporal patterns and covariate information.

Statistical Tolerance and Clearance Analysis for Assembly

NASA Technical Reports Server (NTRS)

Lee, S.; Yi, C.

1996-01-01

Tolerance is inevitable because manufacturing exactly equal parts is known to be impossible. Furthermore, the specification of tolerances is an integral part of product design since tolerances directly affect the assemblability, functionality, manufacturability, and cost effectiveness of a product. In this paper, we present statistical tolerance and clearance analysis for the assembly. Our proposed work is expected to make the following contributions: (i) to help the designers to evaluate products for assemblability, (ii) to provide a new perspective to tolerance problems, and (iii) to provide a tolerance analysis tool which can be incorporated into a CAD or solid modeling system.

Regression analysis of mixed recurrent-event and panel-count data with additive rate models.

PubMed

Zhu, Liang; Zhao, Hui; Sun, Jianguo; Leisenring, Wendy; Robison, Leslie L

2015-03-01

Event-history studies of recurrent events are often conducted in fields such as demography, epidemiology, medicine, and social sciences (Cook and Lawless, 2007, The Statistical Analysis of Recurrent Events. New York: Springer-Verlag; Zhao et al., 2011, Test 20, 1-42). For such analysis, two types of data have been extensively investigated: recurrent-event data and panel-count data. However, in practice, one may face a third type of data, mixed recurrent-event and panel-count data or mixed event-history data. Such data occur if some study subjects are monitored or observed continuously and thus provide recurrent-event data, while the others are observed only at discrete times and hence give only panel-count data. A more general situation is that each subject is observed continuously over certain time periods but only at discrete times over other time periods. There exists little literature on the analysis of such mixed data except that published by Zhu et al. (2013, Statistics in Medicine 32, 1954-1963). In this article, we consider the regression analysis of mixed data using the additive rate model and develop some estimating equation-based approaches to estimate the regression parameters of interest. Both finite sample and asymptotic properties of the resulting estimators are established, and the numerical studies suggest that the proposed methodology works well for practical situations. The approach is applied to a Childhood Cancer Survivor Study that motivated this study. © 2014, The International Biometric Society.

Bayesian Sensitivity Analysis of Statistical Models with Missing Data

PubMed Central

ZHU, HONGTU; IBRAHIM, JOSEPH G.; TANG, NIANSHENG

2013-01-01

Methods for handling missing data depend strongly on the mechanism that generated the missing values, such as missing completely at random (MCAR) or missing at random (MAR), as well as other distributional and modeling assumptions at various stages. It is well known that the resulting estimates and tests may be sensitive to these assumptions as well as to outlying observations. In this paper, we introduce various perturbations to modeling assumptions and individual observations, and then develop a formal sensitivity analysis to assess these perturbations in the Bayesian analysis of statistical models with missing data. We develop a geometric framework, called the Bayesian perturbation manifold, to characterize the intrinsic structure of these perturbations. We propose several intrinsic influence measures to perform sensitivity analysis and quantify the effect of various perturbations to statistical models. We use the proposed sensitivity analysis procedure to systematically investigate the tenability of the non-ignorable missing at random (NMAR) assumption. Simulation studies are conducted to evaluate our methods, and a dataset is analyzed to illustrate the use of our diagnostic measures. PMID:24753718

Journal of Transportation and Statistics, Vol. 3, No. 2 : special issue on the statistical analysis and modeling of automotive emissions

DOT National Transportation Integrated Search

2000-09-01

This special issue of the Journal of Transportation and Statistics is devoted to the statistical analysis and modeling of automotive emissions. It contains many of the papers presented in the mini-symposium last August and also includes one additiona...

Statistical Analysis in Dental Research Papers.

DTIC Science & Technology

1983-08-08

AD A136, 019 STATISTICAL ANALYSS IN DENTAL RESEARCH PAPERS(Ul ARMY I INS OF DENTAL NESEARCH WASHINGTON DC L LORTON 0R AUG983 UNCL ASS FED F/S 6/5 IEE...BEFORE COSTL’,..G FORM 2. GOVT ACCESSION NO 3. RECIPIENTS CATALOG NUbER d Ste S. TYPE OF REPORT A PERIOD COVERED ,cistical Analysis in Dental Research ...Papers Submission of papaer Jan- Aue 1983 X!t AUTHOR(&) ". COTACO.RATN Lewis Lorton 9. PERFORMING ORGANIZATION NAME AND ADDRESS 10. PROGRAM ELEMENT

NUMERICAL ANALYSIS TECHNIQUE USING THE STATISTICAL ENERGY ANALYSIS METHOD CONCERNING THE BLASTING NOISE REDUCTION BY THE SOUND INSULATION DOOR USED IN TUNNEL CONSTRUCTIONS

NASA Astrophysics Data System (ADS)

Ishida, Shigeki; Mori, Atsuo; Shinji, Masato

The main method to reduce the blasting charge noise which occurs in a tunnel under construction is to install the sound insulation door in the tunnel. However, the numerical analysis technique to predict the accurate effect of the transmission loss in the sound insulation door is not established. In this study, we measured the blasting charge noise and the vibration of the sound insulation door in the tunnel with the blasting charge, and performed analysis and modified acoustic feature. In addition, we reproduced the noise reduction effect of the sound insulation door by statistical energy analysis method and confirmed that numerical simulation is possible by this procedure.

Linkage analysis of systolic blood pressure: a score statistic and computer implementation

PubMed Central

Wang, Kai; Peng, Yingwei

2003-01-01

A genome-wide linkage analysis was conducted on systolic blood pressure using a score statistic. The randomly selected Replicate 34 of the simulated data was used. The score statistic was applied to the sibships derived from the general pedigrees. An add-on R program to GENEHUNTER was developed for this analysis and is freely available. PMID:14975145

Multi-trait analysis of genome-wide association summary statistics using MTAG.

PubMed

Turley, Patrick; Walters, Raymond K; Maghzian, Omeed; Okbay, Aysu; Lee, James J; Fontana, Mark Alan; Nguyen-Viet, Tuan Anh; Wedow, Robbee; Zacher, Meghan; Furlotte, Nicholas A; Magnusson, Patrik; Oskarsson, Sven; Johannesson, Magnus; Visscher, Peter M; Laibson, David; Cesarini, David; Neale, Benjamin M; Benjamin, Daniel J

2018-02-01

We introduce multi-trait analysis of GWAS (MTAG), a method for joint analysis of summary statistics from genome-wide association studies (GWAS) of different traits, possibly from overlapping samples. We apply MTAG to summary statistics for depressive symptoms (N eff  = 354,862), neuroticism (N = 168,105), and subjective well-being (N = 388,538). As compared to the 32, 9, and 13 genome-wide significant loci identified in the single-trait GWAS (most of which are themselves novel), MTAG increases the number of associated loci to 64, 37, and 49, respectively. Moreover, association statistics from MTAG yield more informative bioinformatics analyses and increase the variance explained by polygenic scores by approximately 25%, matching theoretical expectations.

A critique of the usefulness of inferential statistics in applied behavior analysis

PubMed Central

Hopkins, B. L.; Cole, Brian L.; Mason, Tina L.

1998-01-01

Researchers continue to recommend that applied behavior analysts use inferential statistics in making decisions about effects of independent variables on dependent variables. In many other approaches to behavioral science, inferential statistics are the primary means for deciding the importance of effects. Several possible uses of inferential statistics are considered. Rather than being an objective means for making decisions about effects, as is often claimed, inferential statistics are shown to be subjective. It is argued that the use of inferential statistics adds nothing to the complex and admittedly subjective nonstatistical methods that are often employed in applied behavior analysis. Attacks on inferential statistics that are being made, perhaps with increasing frequency, by those who are not behavior analysts, are discussed. These attackers are calling for banning the use of inferential statistics in research publications and commonly recommend that behavioral scientists should switch to using statistics aimed at interval estimation or the method of confidence intervals. Interval estimation is shown to be contrary to the fundamental assumption of behavior analysis that only individuals behave. It is recommended that authors who wish to publish the results of inferential statistics be asked to justify them as a means for helping us to identify any ways in which they may be useful. PMID:22478304

Analysis of Coastal Dunes: A Remote Sensing and Statistical Approach.

ERIC Educational Resources Information Center

Jones, J. Richard

1985-01-01

Remote sensing analysis and statistical methods were used to analyze the coastal dunes of Plum Island, Massachusetts. The research methodology used provides an example of a student project for remote sensing, geomorphology, or spatial analysis courses at the university level. (RM)

Application of Multivariate Statistical Analysis to Biomarkers in Se-Turkey Crude Oils

NASA Astrophysics Data System (ADS)

Gürgey, K.; Canbolat, S.

2017-11-01

Twenty-four crude oil samples were collected from the 24 oil fields distributed in different districts of SE-Turkey. API and Sulphur content (%), Stable Carbon Isotope, Gas Chromatography (GC), and Gas Chromatography-Mass Spectrometry (GC-MS) data were used to construct a geochemical data matrix. The aim of this study is to examine the genetic grouping or correlations in the crude oil samples, hence the number of source rocks present in the SE-Turkey. To achieve these aims, two of the multivariate statistical analysis techniques (Principle Component Analysis [PCA] and Cluster Analysis were applied to data matrix of 24 samples and 8 source specific biomarker variables/parameters. The results showed that there are 3 genetically different oil groups: Batman-Nusaybin Oils, Adıyaman-Kozluk Oils and Diyarbakir Oils, in addition to a one mixed group. These groupings imply that at least, three different source rocks are present in South-Eastern (SE) Turkey. Grouping of the crude oil samples appears to be consistent with the geographic locations of the oils fields, subsurface stratigraphy as well as geology of the area.

Gene flow analysis method, the D-statistic, is robust in a wide parameter space.

PubMed

Zheng, Yichen; Janke, Axel

2018-01-08

We evaluated the sensitivity of the D-statistic, a parsimony-like method widely used to detect gene flow between closely related species. This method has been applied to a variety of taxa with a wide range of divergence times. However, its parameter space and thus its applicability to a wide taxonomic range has not been systematically studied. Divergence time, population size, time of gene flow, distance of outgroup and number of loci were examined in a sensitivity analysis. The sensitivity study shows that the primary determinant of the D-statistic is the relative population size, i.e. the population size scaled by the number of generations since divergence. This is consistent with the fact that the main confounding factor in gene flow detection is incomplete lineage sorting by diluting the signal. The sensitivity of the D-statistic is also affected by the direction of gene flow, size and number of loci. In addition, we examined the ability of the f-statistics, [Formula: see text] and [Formula: see text], to estimate the fraction of a genome affected by gene flow; while these statistics are difficult to implement to practical questions in biology due to lack of knowledge of when the gene flow happened, they can be used to compare datasets with identical or similar demographic background. The D-statistic, as a method to detect gene flow, is robust against a wide range of genetic distances (divergence times) but it is sensitive to population size. The D-statistic should only be applied with critical reservation to taxa where population sizes are large relative to branch lengths in generations.

Meta-analysis and The Cochrane Collaboration: 20 years of the Cochrane Statistical Methods Group

PubMed Central

2013-01-01

The Statistical Methods Group has played a pivotal role in The Cochrane Collaboration over the past 20 years. The Statistical Methods Group has determined the direction of statistical methods used within Cochrane reviews, developed guidance for these methods, provided training, and continued to discuss and consider new and controversial issues in meta-analysis. The contribution of Statistical Methods Group members to the meta-analysis literature has been extensive and has helped to shape the wider meta-analysis landscape. In this paper, marking the 20th anniversary of The Cochrane Collaboration, we reflect on the history of the Statistical Methods Group, beginning in 1993 with the identification of aspects of statistical synthesis for which consensus was lacking about the best approach. We highlight some landmark methodological developments that Statistical Methods Group members have contributed to in the field of meta-analysis. We discuss how the Group implements and disseminates statistical methods within The Cochrane Collaboration. Finally, we consider the importance of robust statistical methodology for Cochrane systematic reviews, note research gaps, and reflect on the challenges that the Statistical Methods Group faces in its future direction. PMID:24280020

Recent advances in statistical energy analysis

NASA Technical Reports Server (NTRS)

Heron, K. H.

1992-01-01

Statistical Energy Analysis (SEA) has traditionally been developed using modal summation and averaging approach, and has led to the need for many restrictive SEA assumptions. The assumption of 'weak coupling' is particularly unacceptable when attempts are made to apply SEA to structural coupling. It is now believed that this assumption is more a function of the modal formulation rather than a necessary formulation of SEA. The present analysis ignores this restriction and describes a wave approach to the calculation of plate-plate coupling loss factors. Predictions based on this method are compared with results obtained from experiments using point excitation on one side of an irregular six-sided box structure. Conclusions show that the use and calculation of infinite transmission coefficients is the way forward for the development of a purely predictive SEA code.

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

Introductory Statistics and Fish Management.

ERIC Educational Resources Information Center

Jardine, Dick

2002-01-01

Describes how fisheries research and management data (available on a website) have been incorporated into an Introductory Statistics course. In addition to the motivation gained from seeing the practical relevance of the course, some students have participated in the data collection and analysis for the New Hampshire Fish and Game Department. (MM)

Guidelines for the design and statistical analysis of experiments in papers submitted to ATLA.

PubMed

Festing, M F

2001-01-01

In vitro experiments need to be well designed and correctly analysed if they are to achieve their full potential to replace the use of animals in research. An "experiment" is a procedure for collecting scientific data in order to answer a hypothesis, or to provide material for generating new hypotheses, and differs from a survey because the scientist has control over the treatments that can be applied. Most experiments can be classified into one of a few formal designs, the most common being completely randomised, and randomised block designs. These are quite common with in vitro experiments, which are often replicated in time. Some experiments involve a single independent (treatment) variable, while other "factorial" designs simultaneously vary two or more independent variables, such as drug treatment and cell line. Factorial designs often provide additional information at little extra cost. Experiments need to be carefully planned to avoid bias, be powerful yet simple, provide for a valid statistical analysis and, in some cases, have a wide range of applicability. Virtually all experiments need some sort of statistical analysis in order to take account of biological variation among the experimental subjects. Parametric methods using the t test or analysis of variance are usually more powerful than non-parametric methods, provided the underlying assumptions of normality of the residuals and equal variances are approximately valid. The statistical analyses of data from a completely randomised design, and from a randomised-block design are demonstrated in Appendices 1 and 2, and methods of determining sample size are discussed in Appendix 3. Appendix 4 gives a checklist for authors submitting papers to ATLA.

Statistical analysis of aerosol species, trace gasses, and meteorology in Chicago.

PubMed

Binaku, Katrina; O'Brien, Timothy; Schmeling, Martina; Fosco, Tinamarie

2013-09-01

Both canonical correlation analysis (CCA) and principal component analysis (PCA) were applied to atmospheric aerosol and trace gas concentrations and meteorological data collected in Chicago during the summer months of 2002, 2003, and 2004. Concentrations of ammonium, calcium, nitrate, sulfate, and oxalate particulate matter, as well as, meteorological parameters temperature, wind speed, wind direction, and humidity were subjected to CCA and PCA. Ozone and nitrogen oxide mixing ratios were also included in the data set. The purpose of statistical analysis was to determine the extent of existing linear relationship(s), or lack thereof, between meteorological parameters and pollutant concentrations in addition to reducing dimensionality of the original data to determine sources of pollutants. In CCA, the first three canonical variate pairs derived were statistically significant at the 0.05 level. Canonical correlation between the first canonical variate pair was 0.821, while correlations of the second and third canonical variate pairs were 0.562 and 0.461, respectively. The first canonical variate pair indicated that increasing temperatures resulted in high ozone mixing ratios, while the second canonical variate pair showed wind speed and humidity's influence on local ammonium concentrations. No new information was uncovered in the third variate pair. Canonical loadings were also interpreted for information regarding relationships between data sets. Four principal components (PCs), expressing 77.0 % of original data variance, were derived in PCA. Interpretation of PCs suggested significant production and/or transport of secondary aerosols in the region (PC1). Furthermore, photochemical production of ozone and wind speed's influence on pollutants were expressed (PC2) along with overall measure of local meteorology (PC3). In summary, CCA and PCA results combined were successful in uncovering linear relationships between meteorology and air pollutants in Chicago and

Australasian Resuscitation In Sepsis Evaluation trial statistical analysis plan.

PubMed

Delaney, Anthony; Peake, Sandra L; Bellomo, Rinaldo; Cameron, Peter; Holdgate, Anna; Howe, Belinda; Higgins, Alisa; Presneill, Jeffrey; Webb, Steve

2013-10-01

The Australasian Resuscitation In Sepsis Evaluation (ARISE) study is an international, multicentre, randomised, controlled trial designed to evaluate the effectiveness of early goal-directed therapy compared with standard care for patients presenting to the ED with severe sepsis. In keeping with current practice, and taking into considerations aspects of trial design and reporting specific to non-pharmacologic interventions, this document outlines the principles and methods for analysing and reporting the trial results. The document is prepared prior to completion of recruitment into the ARISE study, without knowledge of the results of the interim analysis conducted by the data safety and monitoring committee and prior to completion of the two related international studies. The statistical analysis plan was designed by the ARISE chief investigators, and reviewed and approved by the ARISE steering committee. The data collected by the research team as specified in the study protocol, and detailed in the study case report form were reviewed. Information related to baseline characteristics, characteristics of delivery of the trial interventions, details of resuscitation and other related therapies, and other relevant data are described with appropriate comparisons between groups. The primary, secondary and tertiary outcomes for the study are defined, with description of the planned statistical analyses. A statistical analysis plan was developed, along with a trial profile, mock-up tables and figures. A plan for presenting baseline characteristics, microbiological and antibiotic therapy, details of the interventions, processes of care and concomitant therapies, along with adverse events are described. The primary, secondary and tertiary outcomes are described along with identification of subgroups to be analysed. A statistical analysis plan for the ARISE study has been developed, and is available in the public domain, prior to the completion of recruitment into the

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.

Measuring the Success of an Academic Development Programme: A Statistical Analysis

ERIC Educational Resources Information Center

Smith, L. C.

2009-01-01

This study uses statistical analysis to estimate the impact of first-year academic development courses in microeconomics, statistics, accountancy, and information systems, offered by the University of Cape Town's Commerce Academic Development Programme, on students' graduation performance relative to that achieved by mainstream students. The data…

Analysis of Variance in Statistical Image Processing

NASA Astrophysics Data System (ADS)

Kurz, Ludwik; Hafed Benteftifa, M.

1997-04-01

A key problem in practical image processing is the detection of specific features in a noisy image. Analysis of variance (ANOVA) techniques can be very effective in such situations, and this book gives a detailed account of the use of ANOVA in statistical image processing. The book begins by describing the statistical representation of images in the various ANOVA models. The authors present a number of computationally efficient algorithms and techniques to deal with such problems as line, edge, and object detection, as well as image restoration and enhancement. By describing the basic principles of these techniques, and showing their use in specific situations, the book will facilitate the design of new algorithms for particular applications. It will be of great interest to graduate students and engineers in the field of image processing and pattern recognition.

Applications of statistics to medical science, IV survival analysis.

PubMed

Watanabe, Hiroshi

2012-01-01

The fundamental principles of survival analysis are reviewed. In particular, the Kaplan-Meier method and a proportional hazard model are discussed. This work is the last part of a series in which medical statistics are surveyed.

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...

Docking studies on NSAID/COX-2 isozyme complexes using Contact Statistics analysis

NASA Astrophysics Data System (ADS)

Ermondi, Giuseppe; Caron, Giulia; Lawrence, Raelene; Longo, Dario

2004-11-01

The selective inhibition of COX-2 isozymes should lead to a new generation of NSAIDs with significantly reduced side effects; e.g. celecoxib (Celebrex®) and rofecoxib (Vioxx®). To obtain inhibitors with higher selectivity it has become essential to gain additional insight into the details of the interactions between COX isozymes and NSAIDs. Although X-ray structures of COX-2 complexed with a small number of ligands are available, experimental data are missing for two well-known selective COX-2 inhibitors (rofecoxib and nimesulide) and docking results reported are controversial. We use a combination of a traditional docking procedure with a new computational tool (Contact Statistics analysis) that identifies the best orientation among a number of solutions to shed some light on this topic.

A power comparison of generalized additive models and the spatial scan statistic in a case-control setting

PubMed Central

2010-01-01

Background A common, important problem in spatial epidemiology is measuring and identifying variation in disease risk across a study region. In application of statistical methods, the problem has two parts. First, spatial variation in risk must be detected across the study region and, second, areas of increased or decreased risk must be correctly identified. The location of such areas may give clues to environmental sources of exposure and disease etiology. One statistical method applicable in spatial epidemiologic settings is a generalized additive model (GAM) which can be applied with a bivariate LOESS smoother to account for geographic location as a possible predictor of disease status. A natural hypothesis when applying this method is whether residential location of subjects is associated with the outcome, i.e. is the smoothing term necessary? Permutation tests are a reasonable hypothesis testing method and provide adequate power under a simple alternative hypothesis. These tests have yet to be compared to other spatial statistics. Results This research uses simulated point data generated under three alternative hypotheses to evaluate the properties of the permutation methods and compare them to the popular spatial scan statistic in a case-control setting. Case 1 was a single circular cluster centered in a circular study region. The spatial scan statistic had the highest power though the GAM method estimates did not fall far behind. Case 2 was a single point source located at the center of a circular cluster and Case 3 was a line source at the center of the horizontal axis of a square study region. Each had linearly decreasing logodds with distance from the point. The GAM methods outperformed the scan statistic in Cases 2 and 3. Comparing sensitivity, measured as the proportion of the exposure source correctly identified as high or low risk, the GAM methods outperformed the scan statistic in all three Cases. Conclusions The GAM permutation testing methods

A power comparison of generalized additive models and the spatial scan statistic in a case-control setting.

PubMed

Young, Robin L; Weinberg, Janice; Vieira, Verónica; Ozonoff, Al; Webster, Thomas F

2010-07-19

A common, important problem in spatial epidemiology is measuring and identifying variation in disease risk across a study region. In application of statistical methods, the problem has two parts. First, spatial variation in risk must be detected across the study region and, second, areas of increased or decreased risk must be correctly identified. The location of such areas may give clues to environmental sources of exposure and disease etiology. One statistical method applicable in spatial epidemiologic settings is a generalized additive model (GAM) which can be applied with a bivariate LOESS smoother to account for geographic location as a possible predictor of disease status. A natural hypothesis when applying this method is whether residential location of subjects is associated with the outcome, i.e. is the smoothing term necessary? Permutation tests are a reasonable hypothesis testing method and provide adequate power under a simple alternative hypothesis. These tests have yet to be compared to other spatial statistics. This research uses simulated point data generated under three alternative hypotheses to evaluate the properties of the permutation methods and compare them to the popular spatial scan statistic in a case-control setting. Case 1 was a single circular cluster centered in a circular study region. The spatial scan statistic had the highest power though the GAM method estimates did not fall far behind. Case 2 was a single point source located at the center of a circular cluster and Case 3 was a line source at the center of the horizontal axis of a square study region. Each had linearly decreasing logodds with distance from the point. The GAM methods outperformed the scan statistic in Cases 2 and 3. Comparing sensitivity, measured as the proportion of the exposure source correctly identified as high or low risk, the GAM methods outperformed the scan statistic in all three Cases. The GAM permutation testing methods provide a regression

Spectral signature verification using statistical analysis and text mining

NASA Astrophysics Data System (ADS)

DeCoster, Mallory E.; Firpi, Alexe H.; Jacobs, Samantha K.; Cone, Shelli R.; Tzeng, Nigel H.; Rodriguez, Benjamin M.

2016-05-01

In the spectral science community, numerous spectral signatures are stored in databases representative of many sample materials collected from a variety of spectrometers and spectroscopists. Due to the variety and variability of the spectra that comprise many spectral databases, it is necessary to establish a metric for validating the quality of spectral signatures. This has been an area of great discussion and debate in the spectral science community. This paper discusses a method that independently validates two different aspects of a spectral signature to arrive at a final qualitative assessment; the textual meta-data and numerical spectral data. Results associated with the spectral data stored in the Signature Database1 (SigDB) are proposed. The numerical data comprising a sample material's spectrum is validated based on statistical properties derived from an ideal population set. The quality of the test spectrum is ranked based on a spectral angle mapper (SAM) comparison to the mean spectrum derived from the population set. Additionally, the contextual data of a test spectrum is qualitatively analyzed using lexical analysis text mining. This technique analyzes to understand the syntax of the meta-data to provide local learning patterns and trends within the spectral data, indicative of the test spectrum's quality. Text mining applications have successfully been implemented for security2 (text encryption/decryption), biomedical3 , and marketing4 applications. The text mining lexical analysis algorithm is trained on the meta-data patterns of a subset of high and low quality spectra, in order to have a model to apply to the entire SigDB data set. The statistical and textual methods combine to assess the quality of a test spectrum existing in a database without the need of an expert user. This method has been compared to other validation methods accepted by the spectral science community, and has provided promising results when a baseline spectral signature is

Effects of additional data on Bayesian clustering.

PubMed

Yamazaki, Keisuke

2017-10-01

Hierarchical probabilistic models, such as mixture models, are used for cluster analysis. These models have two types of variables: observable and latent. In cluster analysis, the latent variable is estimated, and it is expected that additional information will improve the accuracy of the estimation of the latent variable. Many proposed learning methods are able to use additional data; these include semi-supervised learning and transfer learning. However, from a statistical point of view, a complex probabilistic model that encompasses both the initial and additional data might be less accurate due to having a higher-dimensional parameter. The present paper presents a theoretical analysis of the accuracy of such a model and clarifies which factor has the greatest effect on its accuracy, the advantages of obtaining additional data, and the disadvantages of increasing the complexity. Copyright © 2017 Elsevier Ltd. All rights reserved.

MORTICIA, a statistical analysis software package for determining optical surveillance system effectiveness.

NASA Astrophysics Data System (ADS)

Ramkilowan, A.; Griffith, D. J.

2017-10-01

Surveillance modelling in terms of the standard Detect, Recognise and Identify (DRI) thresholds remains a key requirement for determining the effectiveness of surveillance sensors. With readily available computational resources it has become feasible to perform statistically representative evaluations of the effectiveness of these sensors. A new capability for performing this Monte-Carlo type analysis is demonstrated in the MORTICIA (Monte- Carlo Optical Rendering for Theatre Investigations of Capability under the Influence of the Atmosphere) software package developed at the Council for Scientific and Industrial Research (CSIR). This first generation, python-based open-source integrated software package, currently in the alpha stage of development aims to provide all the functionality required to perform statistical investigations of the effectiveness of optical surveillance systems in specific or generic deployment theatres. This includes modelling of the mathematical and physical processes that govern amongst other components of a surveillance system; a sensor's detector and optical components, a target and its background as well as the intervening atmospheric influences. In this paper we discuss integral aspects of the bespoke framework that are critical to the longevity of all subsequent modelling efforts. Additionally, some preliminary results are presented.

Collagen morphology and texture analysis: from statistics to classification

PubMed Central

Mostaço-Guidolin, Leila B.; Ko, Alex C.-T.; Wang, Fei; Xiang, Bo; Hewko, Mark; Tian, Ganghong; Major, Arkady; Shiomi, Masashi; Sowa, Michael G.

2013-01-01

In this study we present an image analysis methodology capable of quantifying morphological changes in tissue collagen fibril organization caused by pathological conditions. Texture analysis based on first-order statistics (FOS) and second-order statistics such as gray level co-occurrence matrix (GLCM) was explored to extract second-harmonic generation (SHG) image features that are associated with the structural and biochemical changes of tissue collagen networks. Based on these extracted quantitative parameters, multi-group classification of SHG images was performed. With combined FOS and GLCM texture values, we achieved reliable classification of SHG collagen images acquired from atherosclerosis arteries with >90% accuracy, sensitivity and specificity. The proposed methodology can be applied to a wide range of conditions involving collagen re-modeling, such as in skin disorders, different types of fibrosis and muscular-skeletal diseases affecting ligaments and cartilage. PMID:23846580

FADTTS: functional analysis of diffusion tensor tract statistics.

PubMed

Zhu, Hongtu; Kong, Linglong; Li, Runze; Styner, Martin; Gerig, Guido; Lin, Weili; Gilmore, John H

2011-06-01

The aim of this paper is to present a functional analysis of a diffusion tensor tract statistics (FADTTS) pipeline for delineating the association between multiple diffusion properties along major white matter fiber bundles with a set of covariates of interest, such as age, diagnostic status and gender, and the structure of the variability of these white matter tract properties in various diffusion tensor imaging studies. The FADTTS integrates five statistical tools: (i) a multivariate varying coefficient model for allowing the varying coefficient functions in terms of arc length to characterize the varying associations between fiber bundle diffusion properties and a set of covariates, (ii) a weighted least squares estimation of the varying coefficient functions, (iii) a functional principal component analysis to delineate the structure of the variability in fiber bundle diffusion properties, (iv) a global test statistic to test hypotheses of interest, and (v) a simultaneous confidence band to quantify the uncertainty in the estimated coefficient functions. Simulated data are used to evaluate the finite sample performance of FADTTS. We apply FADTTS to investigate the development of white matter diffusivities along the splenium of the corpus callosum tract and the right internal capsule tract in a clinical study of neurodevelopment. FADTTS can be used to facilitate the understanding of normal brain development, the neural bases of neuropsychiatric disorders, and the joint effects of environmental and genetic factors on white matter fiber bundles. The advantages of FADTTS compared with the other existing approaches are that they are capable of modeling the structured inter-subject variability, testing the joint effects, and constructing their simultaneous confidence bands. However, FADTTS is not crucial for estimation and reduces to the functional analysis method for the single measure. Copyright © 2011 Elsevier Inc. All rights reserved.

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.

Effect of the absolute statistic on gene-sampling gene-set analysis methods.

PubMed

Nam, Dougu

2017-06-01

Gene-set enrichment analysis and its modified versions have commonly been used for identifying altered functions or pathways in disease from microarray data. In particular, the simple gene-sampling gene-set analysis methods have been heavily used for datasets with only a few sample replicates. The biggest problem with this approach is the highly inflated false-positive rate. In this paper, the effect of absolute gene statistic on gene-sampling gene-set analysis methods is systematically investigated. Thus far, the absolute gene statistic has merely been regarded as a supplementary method for capturing the bidirectional changes in each gene set. Here, it is shown that incorporating the absolute gene statistic in gene-sampling gene-set analysis substantially reduces the false-positive rate and improves the overall discriminatory ability. Its effect was investigated by power, false-positive rate, and receiver operating curve for a number of simulated and real datasets. The performances of gene-set analysis methods in one-tailed (genome-wide association study) and two-tailed (gene expression data) tests were also compared and discussed.

Applications of modern statistical methods to analysis of data in physical science

NASA Astrophysics Data System (ADS)

Wicker, James Eric

Modern methods of statistical and computational analysis offer solutions to dilemmas confronting researchers in physical science. Although the ideas behind modern statistical and computational analysis methods were originally introduced in the 1970's, most scientists still rely on methods written during the early era of computing. These researchers, who analyze increasingly voluminous and multivariate data sets, need modern analysis methods to extract the best results from their studies. The first section of this work showcases applications of modern linear regression. Since the 1960's, many researchers in spectroscopy have used classical stepwise regression techniques to derive molecular constants. However, problems with thresholds of entry and exit for model variables plagues this analysis method. Other criticisms of this kind of stepwise procedure include its inefficient searching method, the order in which variables enter or leave the model and problems with overfitting data. We implement an information scoring technique that overcomes the assumptions inherent in the stepwise regression process to calculate molecular model parameters. We believe that this kind of information based model evaluation can be applied to more general analysis situations in physical science. The second section proposes new methods of multivariate cluster analysis. The K-means algorithm and the EM algorithm, introduced in the 1960's and 1970's respectively, formed the basis of multivariate cluster analysis methodology for many years. However, several shortcomings of these methods include strong dependence on initial seed values and inaccurate results when the data seriously depart from hypersphericity. We propose new cluster analysis methods based on genetic algorithms that overcomes the strong dependence on initial seed values. In addition, we propose a generalization of the Genetic K-means algorithm which can accurately identify clusters with complex hyperellipsoidal covariance

A Meta-analysis of Gender Differences in Applied Statistics Achievement.

ERIC Educational Resources Information Center

Schram, Christine M.

1996-01-01

A meta-analysis of gender differences examined statistics achievement in postsecondary level psychology, education, and business courses. Analysis of 13 articles (18 samples) found that undergraduate males had an advantage, outscoring females when the outcome was a series of examinations. Females outscored males when the outcome was total course…

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.

Statistical Analysis of Time-Series from Monitoring of Active Volcanic Vents

NASA Astrophysics Data System (ADS)

Lachowycz, S.; Cosma, I.; Pyle, D. M.; Mather, T. A.; Rodgers, M.; Varley, N. R.

2016-12-01

Despite recent advances in the collection and analysis of time-series from volcano monitoring, and the resulting insights into volcanic processes, challenges remain in forecasting and interpreting activity from near real-time analysis of monitoring data. Statistical methods have potential to characterise the underlying structure and facilitate intercomparison of these time-series, and so inform interpretation of volcanic activity. We explore the utility of multiple statistical techniques that could be widely applicable to monitoring data, including Shannon entropy and detrended fluctuation analysis, by their application to various data streams from volcanic vents during periods of temporally variable activity. Each technique reveals changes through time in the structure of some of the data that were not apparent from conventional analysis. For example, we calculate the Shannon entropy (a measure of the randomness of a signal) of time-series from the recent dome-forming eruptions of Volcán de Colima (Mexico) and Soufrière Hills (Montserrat). The entropy of real-time seismic measurements and the count rate of certain volcano-seismic event types from both volcanoes is found to be temporally variable, with these data generally having higher entropy during periods of lava effusion and/or larger explosions. In some instances, the entropy shifts prior to or coincident with changes in seismic or eruptive activity, some of which were not clearly recognised by real-time monitoring. Comparison with other statistics demonstrates the sensitivity of the entropy to the data distribution, but that it is distinct from conventional statistical measures such as coefficient of variation. We conclude that each analysis technique examined could provide valuable insights for interpretation of diverse monitoring time-series.

Tsallis statistics and neurodegenerative disorders

NASA Astrophysics Data System (ADS)

Iliopoulos, Aggelos C.; Tsolaki, Magdalini; Aifantis, Elias C.

2016-08-01

In this paper, we perform statistical analysis of time series deriving from four neurodegenerative disorders, namely epilepsy, amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), Huntington's disease (HD). The time series are concerned with electroencephalograms (EEGs) of healthy and epileptic states, as well as gait dynamics (in particular stride intervals) of the ALS, PD and HDs. We study data concerning one subject for each neurodegenerative disorder and one healthy control. The analysis is based on Tsallis non-extensive statistical mechanics and in particular on the estimation of Tsallis q-triplet, namely {qstat, qsen, qrel}. The deviation of Tsallis q-triplet from unity indicates non-Gaussian statistics and long-range dependencies for all time series considered. In addition, the results reveal the efficiency of Tsallis statistics in capturing differences in brain dynamics between healthy and epileptic states, as well as differences between ALS, PD, HDs from healthy control subjects. The results indicate that estimations of Tsallis q-indices could be used as possible biomarkers, along with others, for improving classification and prediction of epileptic seizures, as well as for studying the gait complex dynamics of various diseases providing new insights into severity, medications and fall risk, improving therapeutic interventions.

On Conceptual Analysis as the Primary Qualitative Approach to Statistics Education Research in Psychology

ERIC Educational Resources Information Center

Petocz, Agnes; Newbery, Glenn

2010-01-01

Statistics education in psychology often falls disappointingly short of its goals. The increasing use of qualitative approaches in statistics education research has extended and enriched our understanding of statistical cognition processes, and thus facilitated improvements in statistical education and practices. Yet conceptual analysis, a…

Study Designs and Statistical Analyses for Biomarker Research

PubMed Central

Gosho, Masahiko; Nagashima, Kengo; Sato, Yasunori

2012-01-01

Biomarkers are becoming increasingly important for streamlining drug discovery and development. In addition, biomarkers are widely expected to be used as a tool for disease diagnosis, personalized medication, and surrogate endpoints in clinical research. In this paper, we highlight several important aspects related to study design and statistical analysis for clinical research incorporating biomarkers. We describe the typical and current study designs for exploring, detecting, and utilizing biomarkers. Furthermore, we introduce statistical issues such as confounding and multiplicity for statistical tests in biomarker research. PMID:23012528

Analysis and meta-analysis of single-case designs with a standardized mean difference statistic: a primer and applications.

PubMed

Shadish, William R; Hedges, Larry V; Pustejovsky, James E

2014-04-01

This article presents a d-statistic for single-case designs that is in the same metric as the d-statistic used in between-subjects designs such as randomized experiments and offers some reasons why such a statistic would be useful in SCD research. The d has a formal statistical development, is accompanied by appropriate power analyses, and can be estimated using user-friendly SPSS macros. We discuss both advantages and disadvantages of d compared to other approaches such as previous d-statistics, overlap statistics, and multilevel modeling. It requires at least three cases for computation and assumes normally distributed outcomes and stationarity, assumptions that are discussed in some detail. We also show how to test these assumptions. The core of the article then demonstrates in depth how to compute d for one study, including estimation of the autocorrelation and the ratio of between case variance to total variance (between case plus within case variance), how to compute power using a macro, and how to use the d to conduct a meta-analysis of studies using single-case designs in the free program R, including syntax in an appendix. This syntax includes how to read data, compute fixed and random effect average effect sizes, prepare a forest plot and a cumulative meta-analysis, estimate various influence statistics to identify studies contributing to heterogeneity and effect size, and do various kinds of publication bias analyses. This d may prove useful for both the analysis and meta-analysis of data from SCDs. Copyright © 2013 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

Multivariate statistical analysis: Principles and applications to coorbital streams of meteorite falls

NASA Technical Reports Server (NTRS)

Wolf, S. F.; Lipschutz, M. E.

1993-01-01

Multivariate statistical analysis techniques (linear discriminant analysis and logistic regression) can provide powerful discrimination tools which are generally unfamiliar to the planetary science community. Fall parameters were used to identify a group of 17 H chondrites (Cluster 1) that were part of a coorbital stream which intersected Earth's orbit in May, from 1855 - 1895, and can be distinguished from all other H chondrite falls. Using multivariate statistical techniques, it was demonstrated that a totally different criterion, labile trace element contents - hence thermal histories - or 13 Cluster 1 meteorites are distinguishable from those of 45 non-Cluster 1 H chondrites. Here, we focus upon the principles of multivariate statistical techniques and illustrate their application using non-meteoritic and meteoritic examples.

Agriculture, population growth, and statistical analysis of the radiocarbon record.

PubMed

Zahid, H Jabran; Robinson, Erick; Kelly, Robert L

2016-01-26

The human population has grown significantly since the onset of the Holocene about 12,000 y ago. Despite decades of research, the factors determining prehistoric population growth remain uncertain. Here, we examine measurements of the rate of growth of the prehistoric human population based on statistical analysis of the radiocarbon record. We find that, during most of the Holocene, human populations worldwide grew at a long-term annual rate of 0.04%. Statistical analysis of the radiocarbon record shows that transitioning farming societies experienced the same rate of growth as contemporaneous foraging societies. The same rate of growth measured for populations dwelling in a range of environments and practicing a variety of subsistence strategies suggests that the global climate and/or endogenous biological factors, not adaptability to local environment or subsistence practices, regulated the long-term growth of the human population during most of the Holocene. Our results demonstrate that statistical analyses of large ensembles of radiocarbon dates are robust and valuable for quantitatively investigating the demography of prehistoric human populations worldwide.

STATISTICAL ANALYSIS OF SNAP 10A THERMOELECTRIC CONVERTER ELEMENT PROCESS DEVELOPMENT VARIABLES

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

Fitch, S.H.; Morris, J.W.

1962-12-15

Statistical analysis, primarily analysis of variance, was applied to evaluate several factors involved in the development of suitable fabrication and processing techniques for the production of lead telluride thermoelectric elements for the SNAP 10A energy conversion system. The analysis methods are described as to their application for determining the effects of various processing steps, estabIishing the value of individual operations, and evaluating the significance of test results. The elimination of unnecessary or detrimental processing steps was accomplished and the number of required tests was substantially reduced by application of these statistical methods to the SNAP 10A production development effort. (auth)

The Fusion of Financial Analysis and Seismology: Statistical Methods from Financial Market Analysis Applied to Earthquake Data

NASA Astrophysics Data System (ADS)

Ohyanagi, S.; Dileonardo, C.

2013-12-01

As a natural phenomenon earthquake occurrence is difficult to predict. Statistical analysis of earthquake data was performed using candlestick chart and Bollinger Band methods. These statistical methods, commonly used in the financial world to analyze market trends were tested against earthquake data. Earthquakes above Mw 4.0 located on shore of Sanriku (37.75°N ~ 41.00°N, 143.00°E ~ 144.50°E) from February 1973 to May 2013 were selected for analysis. Two specific patterns in earthquake occurrence were recognized through the analysis. One is a spread of candlestick prior to the occurrence of events greater than Mw 6.0. A second pattern shows convergence in the Bollinger Band, which implies a positive or negative change in the trend of earthquakes. Both patterns match general models for the buildup and release of strain through the earthquake cycle, and agree with both the characteristics of the candlestick chart and Bollinger Band analysis. These results show there is a high correlation between patterns in earthquake occurrence and trend analysis by these two statistical methods. The results of this study agree with the appropriateness of the application of these financial analysis methods to the analysis of earthquake occurrence.

Inferring Species Richness and Turnover by Statistical Multiresolution Texture Analysis of Satellite Imagery

PubMed Central

Convertino, Matteo; Mangoubi, Rami S.; Linkov, Igor; Lowry, Nathan C.; Desai, Mukund

2012-01-01

Background The quantification of species-richness and species-turnover is essential to effective monitoring of ecosystems. Wetland ecosystems are particularly in need of such monitoring due to their sensitivity to rainfall, water management and other external factors that affect hydrology, soil, and species patterns. A key challenge for environmental scientists is determining the linkage between natural and human stressors, and the effect of that linkage at the species level in space and time. We propose pixel intensity based Shannon entropy for estimating species-richness, and introduce a method based on statistical wavelet multiresolution texture analysis to quantitatively assess interseasonal and interannual species turnover. Methodology/Principal Findings We model satellite images of regions of interest as textures. We define a texture in an image as a spatial domain where the variations in pixel intensity across the image are both stochastic and multiscale. To compare two textures quantitatively, we first obtain a multiresolution wavelet decomposition of each. Either an appropriate probability density function (pdf) model for the coefficients at each subband is selected, and its parameters estimated, or, a non-parametric approach using histograms is adopted. We choose the former, where the wavelet coefficients of the multiresolution decomposition at each subband are modeled as samples from the generalized Gaussian pdf. We then obtain the joint pdf for the coefficients for all subbands, assuming independence across subbands; an approximation that simplifies the computational burden significantly without sacrificing the ability to statistically distinguish textures. We measure the difference between two textures' representative pdf's via the Kullback-Leibler divergence (KL). Species turnover, or diversity, is estimated using both this KL divergence and the difference in Shannon entropy. Additionally, we predict species richness, or diversity, based on the

Power flow as a complement to statistical energy analysis and finite element analysis

NASA Technical Reports Server (NTRS)

Cuschieri, J. M.

1987-01-01

Present methods of analysis of the structural response and the structure-borne transmission of vibrational energy use either finite element (FE) techniques or statistical energy analysis (SEA) methods. The FE methods are a very useful tool at low frequencies where the number of resonances involved in the analysis is rather small. On the other hand SEA methods can predict with acceptable accuracy the response and energy transmission between coupled structures at relatively high frequencies where the structural modal density is high and a statistical approach is the appropriate solution. In the mid-frequency range, a relatively large number of resonances exist which make finite element method too costly. On the other hand SEA methods can only predict an average level form. In this mid-frequency range a possible alternative is to use power flow techniques, where the input and flow of vibrational energy to excited and coupled structural components can be expressed in terms of input and transfer mobilities. This power flow technique can be extended from low to high frequencies and this can be integrated with established FE models at low frequencies and SEA models at high frequencies to form a verification of the method. This method of structural analysis using power flo and mobility methods, and its integration with SEA and FE analysis is applied to the case of two thin beams joined together at right angles.

Bayesian Statistics and Uncertainty Quantification for Safety Boundary Analysis in Complex Systems

NASA Technical Reports Server (NTRS)

He, Yuning; Davies, Misty Dawn

2014-01-01

The analysis of a safety-critical system often requires detailed knowledge of safe regions and their highdimensional non-linear boundaries. We present a statistical approach to iteratively detect and characterize the boundaries, which are provided as parameterized shape candidates. Using methods from uncertainty quantification and active learning, we incrementally construct a statistical model from only few simulation runs and obtain statistically sound estimates of the shape parameters for safety boundaries.

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

Statistical Analysis of Tsunami Variability

NASA Astrophysics Data System (ADS)

Zolezzi, Francesca; Del Giudice, Tania; Traverso, Chiara; Valfrè, Giulio; Poggi, Pamela; Parker, Eric J.

2010-05-01

similar to that seen in ground motion attenuation correlations used for seismic hazard assessment. The second issue was intra-event variability. This refers to the differences in tsunami wave run-up along a section of coast during a single event. Intra-event variability investigated directly considering field observations. The tsunami events used in the statistical evaluation were selected on the basis of the completeness and reliability of the available data. Tsunami considered for the analysis included the recent and well surveyed tsunami of Boxing Day 2004 (Great Indian Ocean Tsunami), Java 2006, Okushiri 1993, Kocaeli 1999, Messina 1908 and a case study of several historic events in Hawaii. Basic statistical analysis was performed on the field observations from these tsunamis. For events with very wide survey regions, the run-up heights have been grouped in order to maintain a homogeneous distance from the source. Where more than one survey was available for a given event, the original datasets were maintained separately to avoid combination of non-homogeneous data. The observed run-up measurements were used to evaluate the minimum, maximum, average, standard deviation and coefficient of variation for each data set. The minimum coefficient of variation was 0.12 measured for the 2004 Boxing Day tsunami at Nias Island (7 data) while the maximum is 0.98 for the Okushiri 1993 event (93 data). The average coefficient of variation is of the order of 0.45.

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 Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective.

PubMed

Kruschke, John K; Liddell, Torrin M

2018-02-01

In the practice of data analysis, there is a conceptual distinction between hypothesis testing, on the one hand, and estimation with quantified uncertainty on the other. Among frequentists in psychology, a shift of emphasis from hypothesis testing to estimation has been dubbed "the New Statistics" (Cumming 2014). A second conceptual distinction is between frequentist methods and Bayesian methods. Our main goal in this article is to explain how Bayesian methods achieve the goals of the New Statistics better than frequentist methods. The article reviews frequentist and Bayesian approaches to hypothesis testing and to estimation with confidence or credible intervals. The article also describes Bayesian approaches to meta-analysis, randomized controlled trials, and power analysis.

A Statistics-based Platform for Quantitative N-terminome Analysis and Identification of Protease Cleavage Products*

PubMed Central

auf dem Keller, Ulrich; Prudova, Anna; Gioia, Magda; Butler, Georgina S.; Overall, Christopher M.

2010-01-01

Terminal amine isotopic labeling of substrates (TAILS), our recently introduced platform for quantitative N-terminome analysis, enables wide dynamic range identification of original mature protein N-termini and protease cleavage products. Modifying TAILS by use of isobaric tag for relative and absolute quantification (iTRAQ)-like labels for quantification together with a robust statistical classifier derived from experimental protease cleavage data, we report reliable and statistically valid identification of proteolytic events in complex biological systems in MS2 mode. The statistical classifier is supported by a novel parameter evaluating ion intensity-dependent quantification confidences of single peptide quantifications, the quantification confidence factor (QCF). Furthermore, the isoform assignment score (IAS) is introduced, a new scoring system for the evaluation of single peptide-to-protein assignments based on high confidence protein identifications in the same sample prior to negative selection enrichment of N-terminal peptides. By these approaches, we identified and validated, in addition to known substrates, low abundance novel bioactive MMP-2 targets including the plasminogen receptor S100A10 (p11) and the proinflammatory cytokine proEMAP/p43 that were previously undescribed. PMID:20305283

Linearised and non-linearised isotherm models optimization analysis by error functions and statistical means

PubMed Central

2014-01-01

In adsorption study, to describe sorption process and evaluation of best-fitting isotherm model is a key analysis to investigate the theoretical hypothesis. Hence, numerous statistically analysis have been extensively used to estimate validity of the experimental equilibrium adsorption values with the predicted equilibrium values. Several statistical error analysis were carried out. In the present study, the following statistical analysis were carried out to evaluate the adsorption isotherm model fitness, like the Pearson correlation, the coefficient of determination and the Chi-square test, have been used. The ANOVA test was carried out for evaluating significance of various error functions and also coefficient of dispersion were evaluated for linearised and non-linearised models. The adsorption of phenol onto natural soil (Local name Kalathur soil) was carried out, in batch mode at 30 ± 20 C. For estimating the isotherm parameters, to get a holistic view of the analysis the models were compared between linear and non-linear isotherm models. The result reveled that, among above mentioned error functions and statistical functions were designed to determine the best fitting isotherm. PMID:25018878

Gis-Based Spatial Statistical Analysis of College Graduates Employment

NASA Astrophysics Data System (ADS)

Tang, R.

2012-07-01

It is urgently necessary to be aware of the distribution and employment status of college graduates for proper allocation of human resources and overall arrangement of strategic industry. This study provides empirical evidence regarding the use of geocoding and spatial analysis in distribution and employment status of college graduates based on the data from 2004-2008 Wuhan Municipal Human Resources and Social Security Bureau, China. Spatio-temporal distribution of employment unit were analyzed with geocoding using ArcGIS software, and the stepwise multiple linear regression method via SPSS software was used to predict the employment and to identify spatially associated enterprise and professionals demand in the future. The results show that the enterprises in Wuhan east lake high and new technology development zone increased dramatically from 2004 to 2008, and tended to distributed southeastward. Furthermore, the models built by statistical analysis suggest that the specialty of graduates major in has an important impact on the number of the employment and the number of graduates engaging in pillar industries. In conclusion, the combination of GIS and statistical analysis which helps to simulate the spatial distribution of the employment status is a potential tool for human resource development research.

A d-statistic for single-case designs that is equivalent to the usual between-groups d-statistic.

PubMed

Shadish, William R; Hedges, Larry V; Pustejovsky, James E; Boyajian, Jonathan G; Sullivan, Kristynn J; Andrade, Alma; Barrientos, Jeannette L

2014-01-01

We describe a standardised mean difference statistic (d) for single-case designs that is equivalent to the usual d in between-groups experiments. We show how it can be used to summarise treatment effects over cases within a study, to do power analyses in planning new studies and grant proposals, and to meta-analyse effects across studies of the same question. We discuss limitations of this d-statistic, and possible remedies to them. Even so, this d-statistic is better founded statistically than other effect size measures for single-case design, and unlike many general linear model approaches such as multilevel modelling or generalised additive models, it produces a standardised effect size that can be integrated over studies with different outcome measures. SPSS macros for both effect size computation and power analysis are available.

Application of multivariable statistical techniques in plant-wide WWTP control strategies analysis.

PubMed

Flores, X; Comas, J; Roda, I R; Jiménez, L; Gernaey, K V

2007-01-01

The main objective of this paper is to present the application of selected multivariable statistical techniques in plant-wide wastewater treatment plant (WWTP) control strategies analysis. In this study, cluster analysis (CA), principal component analysis/factor analysis (PCA/FA) and discriminant analysis (DA) are applied to the evaluation matrix data set obtained by simulation of several control strategies applied to the plant-wide IWA Benchmark Simulation Model No 2 (BSM2). These techniques allow i) to determine natural groups or clusters of control strategies with a similar behaviour, ii) to find and interpret hidden, complex and casual relation features in the data set and iii) to identify important discriminant variables within the groups found by the cluster analysis. This study illustrates the usefulness of multivariable statistical techniques for both analysis and interpretation of the complex multicriteria data sets and allows an improved use of information for effective evaluation of control strategies.

Statistical analysis of the count and profitability of air conditioners.

PubMed

Rady, El Houssainy A; Mohamed, Salah M; Abd Elmegaly, Alaa A

2018-08-01

This article presents the statistical analysis of the number and profitability of air conditioners in an Egyptian company. Checking the same distribution for each categorical variable has been made using Kruskal-Wallis test.

Statistical analysis of subjective preferences for video enhancement

NASA Astrophysics Data System (ADS)

Woods, Russell L.; Satgunam, PremNandhini; Bronstad, P. Matthew; Peli, Eli

2010-02-01

Measuring preferences for moving video quality is harder than for static images due to the fleeting and variable nature of moving video. Subjective preferences for image quality can be tested by observers indicating their preference for one image over another. Such pairwise comparisons can be analyzed using Thurstone scaling (Farrell, 1999). Thurstone (1927) scaling is widely used in applied psychology, marketing, food tasting and advertising research. Thurstone analysis constructs an arbitrary perceptual scale for the items that are compared (e.g. enhancement levels). However, Thurstone scaling does not determine the statistical significance of the differences between items on that perceptual scale. Recent papers have provided inferential statistical methods that produce an outcome similar to Thurstone scaling (Lipovetsky and Conklin, 2004). Here, we demonstrate that binary logistic regression can analyze preferences for enhanced video.

metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis.

PubMed

Cichonska, Anna; Rousu, Juho; Marttinen, Pekka; Kangas, Antti J; Soininen, Pasi; Lehtimäki, Terho; Raitakari, Olli T; Järvelin, Marjo-Riitta; Salomaa, Veikko; Ala-Korpela, Mika; Ripatti, Samuli; Pirinen, Matti

2016-07-01

A dominant approach to genetic association studies is to perform univariate tests between genotype-phenotype pairs. However, analyzing related traits together increases statistical power, and certain complex associations become detectable only when several variants are tested jointly. Currently, modest sample sizes of individual cohorts, and restricted availability of individual-level genotype-phenotype data across the cohorts limit conducting multivariate tests. We introduce metaCCA, a computational framework for summary statistics-based analysis of a single or multiple studies that allows multivariate representation of both genotype and phenotype. It extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness.Multivariate meta-analysis of two Finnish studies of nuclear magnetic resonance metabolomics by metaCCA, using standard univariate output from the program SNPTEST, shows an excellent agreement with the pooled individual-level analysis of original data. Motivated by strong multivariate signals in the lipid genes tested, we envision that multivariate association testing using metaCCA has a great potential to provide novel insights from already published summary statistics from high-throughput phenotyping technologies. Code is available at https://github.com/aalto-ics-kepaco anna.cichonska@helsinki.fi or matti.pirinen@helsinki.fi Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis

PubMed Central

Cichonska, Anna; Rousu, Juho; Marttinen, Pekka; Kangas, Antti J.; Soininen, Pasi; Lehtimäki, Terho; Raitakari, Olli T.; Järvelin, Marjo-Riitta; Salomaa, Veikko; Ala-Korpela, Mika; Ripatti, Samuli; Pirinen, Matti

2016-01-01

Motivation: A dominant approach to genetic association studies is to perform univariate tests between genotype-phenotype pairs. However, analyzing related traits together increases statistical power, and certain complex associations become detectable only when several variants are tested jointly. Currently, modest sample sizes of individual cohorts, and restricted availability of individual-level genotype-phenotype data across the cohorts limit conducting multivariate tests. Results: We introduce metaCCA, a computational framework for summary statistics-based analysis of a single or multiple studies that allows multivariate representation of both genotype and phenotype. It extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness. Multivariate meta-analysis of two Finnish studies of nuclear magnetic resonance metabolomics by metaCCA, using standard univariate output from the program SNPTEST, shows an excellent agreement with the pooled individual-level analysis of original data. Motivated by strong multivariate signals in the lipid genes tested, we envision that multivariate association testing using metaCCA has a great potential to provide novel insights from already published summary statistics from high-throughput phenotyping technologies. Availability and implementation: Code is available at https://github.com/aalto-ics-kepaco Contacts: anna.cichonska@helsinki.fi or matti.pirinen@helsinki.fi Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27153689

Statistical Signal Models and Algorithms for Image Analysis

DTIC Science & Technology

1984-10-25

In this report, two-dimensional stochastic linear models are used in developing algorithms for image analysis such as classification, segmentation, and object detection in images characterized by textured backgrounds. These models generate two-dimensional random processes as outputs to which statistical inference procedures can naturally be applied. A common thread throughout our algorithms is the interpretation of the inference procedures in terms of linear prediction

Statistical Analysis of the Exchange Rate of Bitcoin.

PubMed

Chu, Jeffrey; Nadarajah, Saralees; Chan, Stephen

2015-01-01

Bitcoin, the first electronic payment system, is becoming a popular currency. We provide a statistical analysis of the log-returns of the exchange rate of Bitcoin versus the United States Dollar. Fifteen of the most popular parametric distributions in finance are fitted to the log-returns. The generalized hyperbolic distribution is shown to give the best fit. Predictions are given for future values of the exchange rate.

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.

General specifications for the development of a USL NASA PC R and D statistical analysis support package

NASA Technical Reports Server (NTRS)

Dominick, Wayne D. (Editor); Bassari, Jinous; Triantafyllopoulos, Spiros

1984-01-01

The University of Southwestern Louisiana (USL) NASA PC R and D statistical analysis support package is designed to be a three-level package to allow statistical analysis for a variety of applications within the USL Data Base Management System (DBMS) contract work. The design addresses usage of the statistical facilities as a library package, as an interactive statistical analysis system, and as a batch processing package.

Statistical Analysis on the Mechanical Properties of Magnesium Alloys

PubMed Central

Liu, Ruoyu; Jiang, Xianquan; Zhang, Hongju; Zhang, Dingfei; Wang, Jingfeng; Pan, Fusheng

2017-01-01

Knowledge of statistical characteristics of mechanical properties is very important for the practical application of structural materials. Unfortunately, the scatter characteristics of magnesium alloys for mechanical performance remain poorly understood until now. In this study, the mechanical reliability of magnesium alloys is systematically estimated using Weibull statistical analysis. Interestingly, the Weibull modulus, m, of strength for magnesium alloys is as high as that for aluminum and steels, confirming the very high reliability of magnesium alloys. The high predictability in the tensile strength of magnesium alloys represents the capability of preventing catastrophic premature failure during service, which is essential for safety and reliability assessment. PMID:29113116

Instrumental Neutron Activation Analysis and Multivariate Statistics for Pottery Provenance

NASA Astrophysics Data System (ADS)

Glascock, M. D.; Neff, H.; Vaughn, K. J.

2004-06-01

The application of instrumental neutron activation analysis and multivariate statistics to archaeological studies of ceramics and clays is described. A small pottery data set from the Nasca culture in southern Peru is presented for illustration.

Database Creation and Statistical Analysis: Finding Connections Between Two or More Secondary Storage Device

DTIC Science & Technology

2017-09-01

NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS DATABASE CREATION AND STATISTICAL ANALYSIS: FINDING CONNECTIONS BETWEEN TWO OR MORE SECONDARY...BLANK ii Approved for public release. Distribution is unlimited. DATABASE CREATION AND STATISTICAL ANALYSIS: FINDING CONNECTIONS BETWEEN TWO OR MORE...Problem and Motivation . . . . . . . . . . . . . . . . . . . 1 1.2 DOD Applicability . . . . . . . . . . . . . . . . .. . . . . . . 2 1.3 Research

Detailed Analysis of the Interoccurrence Time Statistics in Seismic Activity

NASA Astrophysics Data System (ADS)

Tanaka, Hiroki; Aizawa, Yoji

2017-02-01

The interoccurrence time statistics of seismiciry is studied theoretically as well as numerically by taking into account the conditional probability and the correlations among many earthquakes in different magnitude levels. It is known so far that the interoccurrence time statistics is well approximated by the Weibull distribution, but the more detailed information about the interoccurrence times can be obtained from the analysis of the conditional probability. Firstly, we propose the Embedding Equation Theory (EET), where the conditional probability is described by two kinds of correlation coefficients; one is the magnitude correlation and the other is the inter-event time correlation. Furthermore, the scaling law of each correlation coefficient is clearly determined from the numerical data-analysis carrying out with the Preliminary Determination of Epicenter (PDE) Catalog and the Japan Meteorological Agency (JMA) Catalog. Secondly, the EET is examined to derive the magnitude dependence of the interoccurrence time statistics and the multi-fractal relation is successfully formulated. Theoretically we cannot prove the universality of the multi-fractal relation in seismic activity; nevertheless, the theoretical results well reproduce all numerical data in our analysis, where several common features or the invariant aspects are clearly observed. Especially in the case of stationary ensembles the multi-fractal relation seems to obey an invariant curve, furthermore in the case of non-stationary (moving time) ensembles for the aftershock regime the multi-fractal relation seems to satisfy a certain invariant curve at any moving times. It is emphasized that the multi-fractal relation plays an important role to unify the statistical laws of seismicity: actually the Gutenberg-Richter law and the Weibull distribution are unified in the multi-fractal relation, and some universality conjectures regarding the seismicity are briefly discussed.

Sedimentological analysis and bed thickness statistics from a Carboniferous deep-water channel-levee complex: Myall Trough, SE Australia

NASA Astrophysics Data System (ADS)

Palozzi, Jason; Pantopoulos, George; Maravelis, Angelos G.; Nordsvan, Adam; Zelilidis, Avraam

2018-02-01

This investigation presents an outcrop-based integrated study of internal division analysis and statistical treatment of turbidite bed thickness applied to a Carboniferous deep-water channel-levee complex in the Myall Trough, southeast Australia. Turbidite beds of the studied succession are characterized by a range of sedimentary structures grouped into two main associations, a thick-bedded and a thin-bedded one, that reflect channel-fill and overbank/levee deposits, respectively. Three vertically stacked channel-levee cycles have been identified. Results of statistical analysis of bed thickness, grain-size and internal division patterns applied on the studied channel-levee succession, indicate that turbidite bed thickness data seem to be well characterized by a bimodal lognormal distribution, which is possibly reflecting the difference between deposition from lower-density flows (in a levee/overbank setting) and very high-density flows (in a channel fill setting). Power law and exponential distributions were observed to hold only for the thick-bedded parts of the succession and cannot characterize the whole bed thickness range of the studied sediments. The succession also exhibits non-random clustering of bed thickness and grain-size measurements. The studied sediments are also characterized by the presence of statistically detected fining-upward sandstone packets. A novel quantitative approach (change-point analysis) is proposed for the detection of those packets. Markov permutation statistics also revealed the existence of order in the alternation of internal divisions in the succession expressed by an optimal internal division cycle reflecting two main types of gravity flow events deposited within both thick-bedded conglomeratic and thin-bedded sandstone associations. The analytical methods presented in this study can be used as additional tools for quantitative analysis and recognition of depositional environments in hydrocarbon-bearing research of ancient

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.

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.

Methods of learning in statistical education: Design and analysis of a randomized trial

NASA Astrophysics Data System (ADS)

Boyd, Felicity Turner

Background. Recent psychological and technological advances suggest that active learning may enhance understanding and retention of statistical principles. A randomized trial was designed to evaluate the addition of innovative instructional methods within didactic biostatistics courses for public health professionals. Aims. The primary objectives were to evaluate and compare the addition of two active learning methods (cooperative and internet) on students' performance; assess their impact on performance after adjusting for differences in students' learning style; and examine the influence of learning style on trial participation. Methods. Consenting students enrolled in a graduate introductory biostatistics course were randomized to cooperative learning, internet learning, or control after completing a pretest survey. The cooperative learning group participated in eight small group active learning sessions on key statistical concepts, while the internet learning group accessed interactive mini-applications on the same concepts. Controls received no intervention. Students completed evaluations after each session and a post-test survey. Study outcome was performance quantified by examination scores. Intervention effects were analyzed by generalized linear models using intent-to-treat analysis and marginal structural models accounting for reported participation. Results. Of 376 enrolled students, 265 (70%) consented to randomization; 69, 100, and 96 students were randomized to the cooperative, internet, and control groups, respectively. Intent-to-treat analysis showed no differences between study groups; however, 51% of students in the intervention groups had dropped out after the second session. After accounting for reported participation, expected examination scores were 2.6 points higher (of 100 points) after completing one cooperative learning session (95% CI: 0.3, 4.9) and 2.4 points higher after one internet learning session (95% CI: 0.0, 4.7), versus

Anomalous heat transfer modes of nanofluids: a review based on statistical analysis

NASA Astrophysics Data System (ADS)

Sergis, Antonis; Hardalupas, Yannis

2011-05-01

This paper contains the results of a concise statistical review analysis of a large amount of publications regarding the anomalous heat transfer modes of nanofluids. The application of nanofluids as coolants is a novel practise with no established physical foundations explaining the observed anomalous heat transfer. As a consequence, traditional methods of performing a literature review may not be adequate in presenting objectively the results representing the bulk of the available literature. The current literature review analysis aims to resolve the problems faced by researchers in the past by employing an unbiased statistical analysis to present and reveal the current trends and general belief of the scientific community regarding the anomalous heat transfer modes of nanofluids. The thermal performance analysis indicated that statistically there exists a variable enhancement for conduction, convection/mixed heat transfer, pool boiling heat transfer and critical heat flux modes. The most popular proposed mechanisms in the literature to explain heat transfer in nanofluids are revealed, as well as possible trends between nanofluid properties and thermal performance. The review also suggests future experimentation to provide more conclusive answers to the control mechanisms and influential parameters of heat transfer in nanofluids.

Anomalous heat transfer modes of nanofluids: a review based on statistical analysis.

PubMed

Sergis, Antonis; Hardalupas, Yannis

2011-05-19

This paper contains the results of a concise statistical review analysis of a large amount of publications regarding the anomalous heat transfer modes of nanofluids. The application of nanofluids as coolants is a novel practise with no established physical foundations explaining the observed anomalous heat transfer. As a consequence, traditional methods of performing a literature review may not be adequate in presenting objectively the results representing the bulk of the available literature. The current literature review analysis aims to resolve the problems faced by researchers in the past by employing an unbiased statistical analysis to present and reveal the current trends and general belief of the scientific community regarding the anomalous heat transfer modes of nanofluids. The thermal performance analysis indicated that statistically there exists a variable enhancement for conduction, convection/mixed heat transfer, pool boiling heat transfer and critical heat flux modes. The most popular proposed mechanisms in the literature to explain heat transfer in nanofluids are revealed, as well as possible trends between nanofluid properties and thermal performance. The review also suggests future experimentation to provide more conclusive answers to the control mechanisms and influential parameters of heat transfer in nanofluids.

Anomalous heat transfer modes of nanofluids: a review based on statistical analysis

PubMed Central

2011-01-01

This paper contains the results of a concise statistical review analysis of a large amount of publications regarding the anomalous heat transfer modes of nanofluids. The application of nanofluids as coolants is a novel practise with no established physical foundations explaining the observed anomalous heat transfer. As a consequence, traditional methods of performing a literature review may not be adequate in presenting objectively the results representing the bulk of the available literature. The current literature review analysis aims to resolve the problems faced by researchers in the past by employing an unbiased statistical analysis to present and reveal the current trends and general belief of the scientific community regarding the anomalous heat transfer modes of nanofluids. The thermal performance analysis indicated that statistically there exists a variable enhancement for conduction, convection/mixed heat transfer, pool boiling heat transfer and critical heat flux modes. The most popular proposed mechanisms in the literature to explain heat transfer in nanofluids are revealed, as well as possible trends between nanofluid properties and thermal performance. The review also suggests future experimentation to provide more conclusive answers to the control mechanisms and influential parameters of heat transfer in nanofluids. PMID:21711932

Statistical Analysis of the Exchange Rate of Bitcoin

PubMed Central

Chu, Jeffrey; Nadarajah, Saralees; Chan, Stephen

2015-01-01

Bitcoin, the first electronic payment system, is becoming a popular currency. We provide a statistical analysis of the log-returns of the exchange rate of Bitcoin versus the United States Dollar. Fifteen of the most popular parametric distributions in finance are fitted to the log-returns. The generalized hyperbolic distribution is shown to give the best fit. Predictions are given for future values of the exchange rate. PMID:26222702

Common pitfalls in statistical analysis: Odds versus risk

PubMed Central

Ranganathan, Priya; Aggarwal, Rakesh; Pramesh, C. S.

2015-01-01

In biomedical research, we are often interested in quantifying the relationship between an exposure and an outcome. “Odds” and “Risk” are the most common terms which are used as measures of association between variables. In this article, which is the fourth in the series of common pitfalls in statistical analysis, we explain the meaning of risk and odds and the difference between the two. PMID:26623395

Statistics of Data Fitting: Flaws and Fixes of Polynomial Analysis of Channeled Spectra

NASA Astrophysics Data System (ADS)

Karstens, William; Smith, David

2013-03-01

Starting from general statistical principles, we have critically examined Baumeister's procedure* for determining the refractive index of thin films from channeled spectra. Briefly, the method assumes that the index and interference fringe order may be approximated by polynomials quadratic and cubic in photon energy, respectively. The coefficients of the polynomials are related by differentiation, which is equivalent to comparing energy differences between fringes. However, we find that when the fringe order is calculated from the published IR index for silicon* and then analyzed with Baumeister's procedure, the results do not reproduce the original index. This problem has been traced to 1. Use of unphysical powers in the polynomials (e.g., time-reversal invariance requires that the index is an even function of photon energy), and 2. Use of insufficient terms of the correct parity. Exclusion of unphysical terms and addition of quartic and quintic terms to the index and order polynomials yields significantly better fits with fewer parameters. This represents a specific example of using statistics to determine if the assumed fitting model adequately captures the physics contained in experimental data. The use of analysis of variance (ANOVA) and the Durbin-Watson statistic to test criteria for the validity of least-squares fitting will be discussed. *D.F. Edwards and E. Ochoa, Appl. Opt. 19, 4130 (1980). Supported in part by the US Department of Energy, Office of Nuclear Physics under contract DE-AC02-06CH11357.

Analysis of Variance with Summary Statistics in Microsoft® Excel®

ERIC Educational Resources Information Center

Larson, David A.; Hsu, Ko-Cheng

2010-01-01

Students regularly are asked to solve Single Factor Analysis of Variance problems given only the sample summary statistics (number of observations per category, category means, and corresponding category standard deviations). Most undergraduate students today use Excel for data analysis of this type. However, Excel, like all other statistical…

The Australasian Resuscitation in Sepsis Evaluation (ARISE) trial statistical analysis plan.

PubMed

Delaney, Anthony P; Peake, Sandra L; Bellomo, Rinaldo; Cameron, Peter; Holdgate, Anna; Howe, Belinda; Higgins, Alisa; Presneill, Jeffrey; Webb, Steve

2013-09-01

The Australasian Resuscitation in Sepsis Evaluation (ARISE) study is an international, multicentre, randomised, controlled trial designed to evaluate the effectiveness of early goal-directed therapy compared with standard care for patients presenting to the emergency department with severe sepsis. In keeping with current practice, and considering aspects of trial design and reporting specific to non-pharmacological interventions, our plan outlines the principles and methods for analysing and reporting the trial results. The document is prepared before completion of recruitment into the ARISE study, without knowledge of the results of the interim analysis conducted by the data safety and monitoring committee and before completion of the two related international studies. Our statistical analysis plan was designed by the ARISE chief investigators, and reviewed and approved by the ARISE steering committee. We reviewed the data collected by the research team as specified in the study protocol and detailed in the study case report form. We describe information related to baseline characteristics, characteristics of delivery of the trial interventions, details of resuscitation, other related therapies and other relevant data with appropriate comparisons between groups. We define the primary, secondary and tertiary outcomes for the study, with description of the planned statistical analyses. We have developed a statistical analysis plan with a trial profile, mock-up tables and figures. We describe a plan for presenting baseline characteristics, microbiological and antibiotic therapy, details of the interventions, processes of care and concomitant therapies and adverse events. We describe the primary, secondary and tertiary outcomes with identification of subgroups to be analysed. We have developed a statistical analysis plan for the ARISE study, available in the public domain, before the completion of recruitment into the study. This will minimise analytical bias and

STATISTICAL ANALYSIS OF TANK 19F FLOOR SAMPLE RESULTS

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

Harris, S.

2010-09-02

Representative sampling has been completed for characterization of the residual material on the floor of Tank 19F as per the statistical sampling plan developed by Harris and Shine. Samples from eight locations have been obtained from the tank floor and two of the samples were archived as a contingency. Six samples, referred to in this report as the current scrape samples, have been submitted to and analyzed by SRNL. This report contains the statistical analysis of the floor sample analytical results to determine if further data are needed to reduce uncertainty. Included are comparisons with the prior Mantis samples resultsmore » to determine if they can be pooled with the current scrape samples to estimate the upper 95% confidence limits (UCL95%) for concentration. Statistical analysis revealed that the Mantis and current scrape sample results are not compatible. Therefore, the Mantis sample results were not used to support the quantification of analytes in the residual material. Significant spatial variability among the current scrape sample results was not found. Constituent concentrations were similar between the North and South hemispheres as well as between the inner and outer regions of the tank floor. The current scrape sample results from all six samples fall within their 3-sigma limits. In view of the results from numerous statistical tests, the data were pooled from all six current scrape samples. As such, an adequate sample size was provided for quantification of the residual material on the floor of Tank 19F. The uncertainty is quantified in this report by an UCL95% on each analyte concentration. The uncertainty in analyte concentration was calculated as a function of the number of samples, the average, and the standard deviation of the analytical results. The UCL95% was based entirely on the six current scrape sample results (each averaged across three analytical determinations).« less

[Design and implementation of online statistical analysis function in information system of air pollution and health impact monitoring].

PubMed

Lü, Yiran; Hao, Shuxin; Zhang, Guoqing; Liu, Jie; Liu, Yue; Xu, Dongqun

2018-01-01

To implement the online statistical analysis function in information system of air pollution and health impact monitoring, and obtain the data analysis information real-time. Using the descriptive statistical method as well as time-series analysis and multivariate regression analysis, SQL language and visual tools to implement online statistical analysis based on database software. Generate basic statistical tables and summary tables of air pollution exposure and health impact data online; Generate tendency charts of each data part online and proceed interaction connecting to database; Generate butting sheets which can lead to R, SAS and SPSS directly online. The information system air pollution and health impact monitoring implements the statistical analysis function online, which can provide real-time analysis result to its users.

Multivariate statistical analysis of low-voltage EDS spectrum images

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

Anderson, I.M.

1998-03-01

Whereas energy-dispersive X-ray spectrometry (EDS) has been used for compositional analysis in the scanning electron microscope for 30 years, the benefits of using low operating voltages for such analyses have been explored only during the last few years. This paper couples low-voltage EDS with two other emerging areas of characterization: spectrum imaging and multivariate statistical analysis. The specimen analyzed for this study was a finished Intel Pentium processor, with the polyimide protective coating stripped off to expose the final active layers.

MAI statistics estimation and analysis in a DS-CDMA system

NASA Astrophysics Data System (ADS)

Alami Hassani, A.; Zouak, M.; Mrabti, M.; Abdi, F.

2018-05-01

A primary limitation of Direct Sequence Code Division Multiple Access DS-CDMA link performance and system capacity is multiple access interference (MAI). To examine the performance of CDMA systems in the presence of MAI, i.e., in a multiuser environment, several works assumed that the interference can be approximated by a Gaussian random variable. In this paper, we first develop a new and simple approach to characterize the MAI in a multiuser system. In addition to statistically quantifying the MAI power, the paper also proposes a statistical model for both variance and mean of the MAI for synchronous and asynchronous CDMA transmission. We show that the MAI probability density function (PDF) is Gaussian for the equal-received-energy case and validate it by computer simulations.

Tree-space statistics and approximations for large-scale analysis of anatomical trees.

PubMed

Feragen, Aasa; Owen, Megan; Petersen, Jens; Wille, Mathilde M W; Thomsen, Laura H; Dirksen, Asger; de Bruijne, Marleen

2013-01-01

Statistical analysis of anatomical trees is hard to perform due to differences in the topological structure of the trees. In this paper we define statistical properties of leaf-labeled anatomical trees with geometric edge attributes by considering the anatomical trees as points in the geometric space of leaf-labeled trees. This tree-space is a geodesic metric space where any two trees are connected by a unique shortest path, which corresponds to a tree deformation. However, tree-space is not a manifold, and the usual strategy of performing statistical analysis in a tangent space and projecting onto tree-space is not available. Using tree-space and its shortest paths, a variety of statistical properties, such as mean, principal component, hypothesis testing and linear discriminant analysis can be defined. For some of these properties it is still an open problem how to compute them; others (like the mean) can be computed, but efficient alternatives are helpful in speeding up algorithms that use means iteratively, like hypothesis testing. In this paper, we take advantage of a very large dataset (N = 8016) to obtain computable approximations, under the assumption that the data trees parametrize the relevant parts of tree-space well. Using the developed approximate statistics, we illustrate how the structure and geometry of airway trees vary across a population and show that airway trees with Chronic Obstructive Pulmonary Disease come from a different distribution in tree-space than healthy ones. Software is available from http://image.diku.dk/aasa/software.php.

Investigation of Weibull statistics in fracture analysis of cast aluminum

NASA Technical Reports Server (NTRS)

Holland, Frederic A., Jr.; Zaretsky, Erwin V.

1989-01-01

The fracture strengths of two large batches of A357-T6 cast aluminum coupon specimens were compared by using two-parameter Weibull analysis. The minimum number of these specimens necessary to find the fracture strength of the material was determined. The applicability of three-parameter Weibull analysis was also investigated. A design methodology based on the combination of elementary stress analysis and Weibull statistical analysis is advanced and applied to the design of a spherical pressure vessel shell. The results from this design methodology are compared with results from the applicable ASME pressure vessel code.

Statistical Analysis of 30 Years Rainfall Data: A Case Study

NASA Astrophysics Data System (ADS)

Arvind, G.; Ashok Kumar, P.; Girish Karthi, S.; Suribabu, C. R.

2017-07-01

Rainfall is a prime input for various engineering design such as hydraulic structures, bridges and culverts, canals, storm water sewer and road drainage system. The detailed statistical analysis of each region is essential to estimate the relevant input value for design and analysis of engineering structures and also for crop planning. A rain gauge station located closely in Trichy district is selected for statistical analysis where agriculture is the prime occupation. The daily rainfall data for a period of 30 years is used to understand normal rainfall, deficit rainfall, Excess rainfall and Seasonal rainfall of the selected circle headquarters. Further various plotting position formulae available is used to evaluate return period of monthly, seasonally and annual rainfall. This analysis will provide useful information for water resources planner, farmers and urban engineers to assess the availability of water and create the storage accordingly. The mean, standard deviation and coefficient of variation of monthly and annual rainfall was calculated to check the rainfall variability. From the calculated results, the rainfall pattern is found to be erratic. The best fit probability distribution was identified based on the minimum deviation between actual and estimated values. The scientific results and the analysis paved the way to determine the proper onset and withdrawal of monsoon results which were used for land preparation and sowing.

Statistical analysis of weigh-in-motion data for bridge design in Vermont.

DOT National Transportation Integrated Search

2014-10-01

This study investigates the suitability of the HL-93 live load model recommended by AASHTO LRFD Specifications : for its use in the analysis and design of bridges in Vermont. The method of approach consists in performing a : statistical analysis of w...

The Importance of Medical Students' Attitudes Regarding Cognitive Competence for Teaching Applied Statistics: Multi-Site Study and Meta-Analysis

PubMed Central

Milic, Natasa M.; Masic, Srdjan; Milin-Lazovic, Jelena; Trajkovic, Goran; Bukumiric, Zoran; Savic, Marko; Milic, Nikola V.; Cirkovic, Andja; Gajic, Milan; Kostic, Mirjana; Ilic, Aleksandra; Stanisavljevic, Dejana

2016-01-01

Background The scientific community increasingly is recognizing the need to bolster standards of data analysis given the widespread concern that basic mistakes in data analysis are contributing to the irreproducibility of many published research findings. The aim of this study was to investigate students’ attitudes towards statistics within a multi-site medical educational context, monitor their changes and impact on student achievement. In addition, we performed a systematic review to better support our future pedagogical decisions in teaching applied statistics to medical students. Methods A validated Serbian Survey of Attitudes Towards Statistics (SATS-36) questionnaire was administered to medical students attending obligatory introductory courses in biostatistics from three medical universities in the Western Balkans. A systematic review of peer-reviewed publications was performed through searches of Scopus, Web of Science, Science Direct, Medline, and APA databases through 1994. A meta-analysis was performed for the correlation coefficients between SATS component scores and statistics achievement. Pooled estimates were calculated using random effects models. Results SATS-36 was completed by 461 medical students. Most of the students held positive attitudes towards statistics. Ability in mathematics and grade point average were associated in a multivariate regression model with the Cognitive Competence score, after adjusting for age, gender and computer ability. The results of 90 paired data showed that Affect, Cognitive Competence, and Effort scores demonstrated significant positive changes. The Cognitive Competence score showed the largest increase (M = 0.48, SD = 0.95). The positive correlation found between the Cognitive Competence score and students’ achievement (r = 0.41; p<0.001), was also shown in the meta-analysis (r = 0.37; 95% CI 0.32–0.41). Conclusion Students' subjective attitudes regarding Cognitive Competence at the beginning of the

The Importance of Medical Students' Attitudes Regarding Cognitive Competence for Teaching Applied Statistics: Multi-Site Study and Meta-Analysis.

PubMed

Milic, Natasa M; Masic, Srdjan; Milin-Lazovic, Jelena; Trajkovic, Goran; Bukumiric, Zoran; Savic, Marko; Milic, Nikola V; Cirkovic, Andja; Gajic, Milan; Kostic, Mirjana; Ilic, Aleksandra; Stanisavljevic, Dejana

2016-01-01

The scientific community increasingly is recognizing the need to bolster standards of data analysis given the widespread concern that basic mistakes in data analysis are contributing to the irreproducibility of many published research findings. The aim of this study was to investigate students' attitudes towards statistics within a multi-site medical educational context, monitor their changes and impact on student achievement. In addition, we performed a systematic review to better support our future pedagogical decisions in teaching applied statistics to medical students. A validated Serbian Survey of Attitudes Towards Statistics (SATS-36) questionnaire was administered to medical students attending obligatory introductory courses in biostatistics from three medical universities in the Western Balkans. A systematic review of peer-reviewed publications was performed through searches of Scopus, Web of Science, Science Direct, Medline, and APA databases through 1994. A meta-analysis was performed for the correlation coefficients between SATS component scores and statistics achievement. Pooled estimates were calculated using random effects models. SATS-36 was completed by 461 medical students. Most of the students held positive attitudes towards statistics. Ability in mathematics and grade point average were associated in a multivariate regression model with the Cognitive Competence score, after adjusting for age, gender and computer ability. The results of 90 paired data showed that Affect, Cognitive Competence, and Effort scores demonstrated significant positive changes. The Cognitive Competence score showed the largest increase (M = 0.48, SD = 0.95). The positive correlation found between the Cognitive Competence score and students' achievement (r = 0.41; p<0.001), was also shown in the meta-analysis (r = 0.37; 95% CI 0.32-0.41). Students' subjective attitudes regarding Cognitive Competence at the beginning of the biostatistics course, which were directly linked to

On the Statistical Analysis of the Radar Signature of the MQM-34D

DTIC Science & Technology

1975-01-31

target drone for aspect angles near normal to the roll axis for a vertically polarized measurements system. The radar cross section and glint are... drone . The raw data from RATSCAT are reported in graphical form in an AFSWC three-volume report.. The results reported here are a statistical analysis of...Ta1get Drones , AFSWC-rR.74-0l, January 1974. 2James W. Wright, On the Statistical Analysis of the Radar Signature of the MQM-34D, Interim Report

Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects

NASA Technical Reports Server (NTRS)

Makowski, David; Asseng, Senthold; Ewert, Frank; Bassu, Simona; Durand, Jean-Louis; Martre, Pierre; Adam, Myriam; Aggarwal, Pramod K.; Angulo, Carlos; Baron, Chritian;

Economic and statistical analysis of time limitations for spotting fluids and fishing operations

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

Keller, P.S.; Brinkmann, P.E.; Taneja, P.K.

1984-05-01

This paper reviews the statistics of ''Spotting Fluids'' to free stuck drill pipe as well as the economics and statistics of drill string fishing operations. Data were taken from Mobil Oil Exploration and Producing Southeast Inc.'s (MOEPSI) records from 1970-1981. Only those events which occur after a drill string becomes stuck are discussed. The data collected were categorized as Directional Wells and Straight Wells. Bar diagrams are presented to show the Success Ratio vs. Soaking Time for each of the two categories. An analysis was made to identify the elapsed time limit to place the spotting fluid for maximum probabilitymore » of success. Also determined was the statistical minimum soaking time and the maximum soaking time. For determining the time limit for fishing operations, the following criteria were used: 1. The Risked ''Economic Breakeven Analysis'' concept was developed based on the work of Harrison. 2. Statistical Probability of Success based on MOEPSI's records from 1970-1981.« less

Integrated GIS and multivariate statistical analysis for regional scale assessment of heavy metal soil contamination: A critical review.

PubMed

Hou, Deyi; O'Connor, David; Nathanail, Paul; Tian, Li; Ma, Yan

2017-12-01

Heavy metal soil contamination is associated with potential toxicity to humans or ecotoxicity. Scholars have increasingly used a combination of geographical information science (GIS) with geostatistical and multivariate statistical analysis techniques to examine the spatial distribution of heavy metals in soils at a regional scale. A review of such studies showed that most soil sampling programs were based on grid patterns and composite sampling methodologies. Many programs intended to characterize various soil types and land use types. The most often used sampling depth intervals were 0-0.10 m, or 0-0.20 m, below surface; and the sampling densities used ranged from 0.0004 to 6.1 samples per km 2 , with a median of 0.4 samples per km 2 . The most widely used spatial interpolators were inverse distance weighted interpolation and ordinary kriging; and the most often used multivariate statistical analysis techniques were principal component analysis and cluster analysis. The review also identified several determining and correlating factors in heavy metal distribution in soils, including soil type, soil pH, soil organic matter, land use type, Fe, Al, and heavy metal concentrations. The major natural and anthropogenic sources of heavy metals were found to derive from lithogenic origin, roadway and transportation, atmospheric deposition, wastewater and runoff from industrial and mining facilities, fertilizer application, livestock manure, and sewage sludge. This review argues that the full potential of integrated GIS and multivariate statistical analysis for assessing heavy metal distribution in soils on a regional scale has not yet been fully realized. It is proposed that future research be conducted to map multivariate results in GIS to pinpoint specific anthropogenic sources, to analyze temporal trends in addition to spatial patterns, to optimize modeling parameters, and to expand the use of different multivariate analysis tools beyond principal component analysis

7 CFR 91.38 - Additional fees for appeal of analysis.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 3 2010-01-01 2010-01-01 false Additional fees for appeal of analysis. 91.38 Section... LABORATORY TESTING PROGRAMS SERVICES AND GENERAL INFORMATION Fees and Charges § 91.38 Additional fees for appeal of analysis. (a) The applicant for appeal sample testing will be charged a fee at the hourly rate...

7 CFR 91.38 - Additional fees for appeal of analysis.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 7 Agriculture 3 2013-01-01 2013-01-01 false Additional fees for appeal of analysis. 91.38 Section... LABORATORY TESTING PROGRAMS SERVICES AND GENERAL INFORMATION Fees and Charges § 91.38 Additional fees for appeal of analysis. (a) The applicant for appeal sample testing will be charged a fee at the hourly rate...

A new statistic for the analysis of circular data in gamma-ray astronomy

NASA Technical Reports Server (NTRS)

Protheroe, R. J.

1985-01-01

A new statistic is proposed for the analysis of circular data. The statistic is designed specifically for situations where a test of uniformity is required which is powerful against alternatives in which a small fraction of the observations is grouped in a small range of directions, or phases.

Efficiency Analysis: Enhancing the Statistical and Evaluative Power of the Regression-Discontinuity Design.

ERIC Educational Resources Information Center

Madhere, Serge

An analytic procedure, efficiency analysis, is proposed for improving the utility of quantitative program evaluation for decision making. The three features of the procedure are explained: (1) for statistical control, it adopts and extends the regression-discontinuity design; (2) for statistical inferences, it de-emphasizes hypothesis testing in…

Statistical analysis of the determinations of the Sun's Galactocentric distance

NASA Astrophysics Data System (ADS)

Malkin, Zinovy

2013-02-01

Based on several tens of R0 measurements made during the past two decades, several studies have been performed to derive the best estimate of R0. Some used just simple averaging to derive a result, whereas others provided comprehensive analyses of possible errors in published results. In either case, detailed statistical analyses of data used were not performed. However, a computation of the best estimates of the Galactic rotation constants is not only an astronomical but also a metrological task. Here we perform an analysis of 53 R0 measurements (published in the past 20 years) to assess the consistency of the data. Our analysis shows that they are internally consistent. It is also shown that any trend in the R0 estimates from the last 20 years is statistically negligible, which renders the presence of a bandwagon effect doubtful. On the other hand, the formal errors in the published R0 estimates improve significantly with time.

The Content of Statistical Requirements for Authors in Biomedical Research Journals

PubMed Central

Liu, Tian-Yi; Cai, Si-Yu; Nie, Xiao-Lu; Lyu, Ya-Qi; Peng, Xiao-Xia; Feng, Guo-Shuang

2016-01-01

Background: Robust statistical designing, sound statistical analysis, and standardized presentation are important to enhance the quality and transparency of biomedical research. This systematic review was conducted to summarize the statistical reporting requirements introduced by biomedical research journals with an impact factor of 10 or above so that researchers are able to give statistical issues’ serious considerations not only at the stage of data analysis but also at the stage of methodological design. Methods: Detailed statistical instructions for authors were downloaded from the homepage of each of the included journals or obtained from the editors directly via email. Then, we described the types and numbers of statistical guidelines introduced by different press groups. Items of statistical reporting guideline as well as particular requirements were summarized in frequency, which were grouped into design, method of analysis, and presentation, respectively. Finally, updated statistical guidelines and particular requirements for improvement were summed up. Results: Totally, 21 of 23 press groups introduced at least one statistical guideline. More than half of press groups can update their statistical instruction for authors gradually relative to issues of new statistical reporting guidelines. In addition, 16 press groups, covering 44 journals, address particular statistical requirements. The most of the particular requirements focused on the performance of statistical analysis and transparency in statistical reporting, including “address issues relevant to research design, including participant flow diagram, eligibility criteria, and sample size estimation,” and “statistical methods and the reasons.” Conclusions: Statistical requirements for authors are becoming increasingly perfected. Statistical requirements for authors remind researchers that they should make sufficient consideration not only in regards to statistical methods during the research

The Content of Statistical Requirements for Authors in Biomedical Research Journals.

PubMed

Liu, Tian-Yi; Cai, Si-Yu; Nie, Xiao-Lu; Lyu, Ya-Qi; Peng, Xiao-Xia; Feng, Guo-Shuang

2016-10-20

Robust statistical designing, sound statistical analysis, and standardized presentation are important to enhance the quality and transparency of biomedical research. This systematic review was conducted to summarize the statistical reporting requirements introduced by biomedical research journals with an impact factor of 10 or above so that researchers are able to give statistical issues' serious considerations not only at the stage of data analysis but also at the stage of methodological design. Detailed statistical instructions for authors were downloaded from the homepage of each of the included journals or obtained from the editors directly via email. Then, we described the types and numbers of statistical guidelines introduced by different press groups. Items of statistical reporting guideline as well as particular requirements were summarized in frequency, which were grouped into design, method of analysis, and presentation, respectively. Finally, updated statistical guidelines and particular requirements for improvement were summed up. Totally, 21 of 23 press groups introduced at least one statistical guideline. More than half of press groups can update their statistical instruction for authors gradually relative to issues of new statistical reporting guidelines. In addition, 16 press groups, covering 44 journals, address particular statistical requirements. The most of the particular requirements focused on the performance of statistical analysis and transparency in statistical reporting, including "address issues relevant to research design, including participant flow diagram, eligibility criteria, and sample size estimation," and "statistical methods and the reasons." Statistical requirements for authors are becoming increasingly perfected. Statistical requirements for authors remind researchers that they should make sufficient consideration not only in regards to statistical methods during the research design, but also standardized statistical reporting

Statistical analysis of global horizontal solar irradiation GHI in Fez city, Morocco

NASA Astrophysics Data System (ADS)

Bounoua, Z.; Mechaqrane, A.

2018-05-01

An accurate knowledge of the solar energy reaching the ground is necessary for sizing and optimizing the performances of solar installations. This paper describes a statistical analysis of the global horizontal solar irradiation (GHI) at Fez city, Morocco. For better reliability, we have first applied a set of check procedures to test the quality of hourly GHI measurements. We then eliminate the erroneous values which are generally due to measurement or the cosine effect errors. Statistical analysis show that the annual mean daily values of GHI is of approximately 5 kWh/m²/day. Daily monthly mean values and other parameter are also calculated.

Photon counting statistics analysis of biophotons from hands.

PubMed

Jung, Hyun-Hee; Woo, Won-Myung; Yang, Joon-Mo; Choi, Chunho; Lee, Jonghan; Yoon, Gilwon; Yang, Jong S; Soh, Kwang-Sup

2003-05-01

The photon counting statistics of biophotons emitted from hands is studied with a view to test its agreement with the Poisson distribution. The moments of observed probability up to seventh order have been evaluated. The moments of biophoton emission from hands are in good agreement while those of dark counts of photomultiplier tube show large deviations from the theoretical values of Poisson distribution. The present results are consistent with the conventional delta-value analysis of the second moment of probability.

Meta-analysis of gene-level associations for rare variants based on single-variant statistics.

PubMed

Hu, Yi-Juan; Berndt, Sonja I; Gustafsson, Stefan; Ganna, Andrea; Hirschhorn, Joel; North, Kari E; Ingelsson, Erik; Lin, Dan-Yu

2013-08-08

Meta-analysis of genome-wide association studies (GWASs) has led to the discoveries of many common variants associated with complex human diseases. There is a growing recognition that identifying "causal" rare variants also requires large-scale meta-analysis. The fact that association tests with rare variants are performed at the gene level rather than at the variant level poses unprecedented challenges in the meta-analysis. First, different studies may adopt different gene-level tests, so the results are not compatible. Second, gene-level tests require multivariate statistics (i.e., components of the test statistic and their covariance matrix), which are difficult to obtain. To overcome these challenges, we propose to perform gene-level tests for rare variants by combining the results of single-variant analysis (i.e., p values of association tests and effect estimates) from participating studies. This simple strategy is possible because of an insight that multivariate statistics can be recovered from single-variant statistics, together with the correlation matrix of the single-variant test statistics, which can be estimated from one of the participating studies or from a publicly available database. We show both theoretically and numerically that the proposed meta-analysis approach provides accurate control of the type I error and is as powerful as joint analysis of individual participant data. This approach accommodates any disease phenotype and any study design and produces all commonly used gene-level tests. An application to the GWAS summary results of the Genetic Investigation of ANthropometric Traits (GIANT) consortium reveals rare and low-frequency variants associated with human height. The relevant software is freely available. Copyright © 2013 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

Statistical Learning Analysis in Neuroscience: Aiming for Transparency

PubMed Central

Hanke, Michael; Halchenko, Yaroslav O.; Haxby, James V.; Pollmann, Stefan

2009-01-01

Encouraged by a rise of reciprocal interest between the machine learning and neuroscience communities, several recent studies have demonstrated the explanatory power of statistical learning techniques for the analysis of neural data. In order to facilitate a wider adoption of these methods, neuroscientific research needs to ensure a maximum of transparency to allow for comprehensive evaluation of the employed procedures. We argue that such transparency requires “neuroscience-aware” technology for the performance of multivariate pattern analyses of neural data that can be documented in a comprehensive, yet comprehensible way. Recently, we introduced PyMVPA, a specialized Python framework for machine learning based data analysis that addresses this demand. Here, we review its features and applicability to various neural data modalities. PMID:20582270

STATISTICAL ANALYSIS OF TANK 18F FLOOR SAMPLE RESULTS

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

Harris, S.

2010-09-02

Representative sampling has been completed for characterization of the residual material on the floor of Tank 18F as per the statistical sampling plan developed by Shine [1]. Samples from eight locations have been obtained from the tank floor and two of the samples were archived as a contingency. Six samples, referred to in this report as the current scrape samples, have been submitted to and analyzed by SRNL [2]. This report contains the statistical analysis of the floor sample analytical results to determine if further data are needed to reduce uncertainty. Included are comparisons with the prior Mantis samples resultsmore » [3] to determine if they can be pooled with the current scrape samples to estimate the upper 95% confidence limits (UCL{sub 95%}) for concentration. Statistical analysis revealed that the Mantis and current scrape sample results are not compatible. Therefore, the Mantis sample results were not used to support the quantification of analytes in the residual material. Significant spatial variability among the current sample results was not found. Constituent concentrations were similar between the North and South hemispheres as well as between the inner and outer regions of the tank floor. The current scrape sample results from all six samples fall within their 3-sigma limits. In view of the results from numerous statistical tests, the data were pooled from all six current scrape samples. As such, an adequate sample size was provided for quantification of the residual material on the floor of Tank 18F. The uncertainty is quantified in this report by an upper 95% confidence limit (UCL{sub 95%}) on each analyte concentration. The uncertainty in analyte concentration was calculated as a function of the number of samples, the average, and the standard deviation of the analytical results. The UCL{sub 95%} was based entirely on the six current scrape sample results (each averaged across three analytical determinations).« less

A Third Moment Adjusted Test Statistic for Small Sample Factor Analysis

PubMed Central

Lin, Johnny; Bentler, Peter M.

2012-01-01

Goodness of fit testing in factor analysis is based on the assumption that the test statistic is asymptotically chi-square; but this property may not hold in small samples even when the factors and errors are normally distributed in the population. Robust methods such as Browne’s asymptotically distribution-free method and Satorra Bentler’s mean scaling statistic were developed under the presumption of non-normality in the factors and errors. This paper finds new application to the case where factors and errors are normally distributed in the population but the skewness of the obtained test statistic is still high due to sampling error in the observed indicators. An extension of Satorra Bentler’s statistic is proposed that not only scales the mean but also adjusts the degrees of freedom based on the skewness of the obtained test statistic in order to improve its robustness under small samples. A simple simulation study shows that this third moment adjusted statistic asymptotically performs on par with previously proposed methods, and at a very small sample size offers superior Type I error rates under a properly specified model. Data from Mardia, Kent and Bibby’s study of students tested for their ability in five content areas that were either open or closed book were used to illustrate the real-world performance of this statistic. PMID:23144511

A Third Moment Adjusted Test Statistic for Small Sample Factor Analysis.

PubMed

Lin, Johnny; Bentler, Peter M

2012-01-01

Goodness of fit testing in factor analysis is based on the assumption that the test statistic is asymptotically chi-square; but this property may not hold in small samples even when the factors and errors are normally distributed in the population. Robust methods such as Browne's asymptotically distribution-free method and Satorra Bentler's mean scaling statistic were developed under the presumption of non-normality in the factors and errors. This paper finds new application to the case where factors and errors are normally distributed in the population but the skewness of the obtained test statistic is still high due to sampling error in the observed indicators. An extension of Satorra Bentler's statistic is proposed that not only scales the mean but also adjusts the degrees of freedom based on the skewness of the obtained test statistic in order to improve its robustness under small samples. A simple simulation study shows that this third moment adjusted statistic asymptotically performs on par with previously proposed methods, and at a very small sample size offers superior Type I error rates under a properly specified model. Data from Mardia, Kent and Bibby's study of students tested for their ability in five content areas that were either open or closed book were used to illustrate the real-world performance of this statistic.

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.

OSPAR standard method and software for statistical analysis of beach litter data.

PubMed

Schulz, Marcus; van Loon, Willem; Fleet, David M; Baggelaar, Paul; van der Meulen, Eit

2017-09-15

The aim of this study is to develop standard statistical methods and software for the analysis of beach litter data. The optimal ensemble of statistical methods comprises the Mann-Kendall trend test, the Theil-Sen slope estimation, the Wilcoxon step trend test and basic descriptive statistics. The application of Litter Analyst, a tailor-made software for analysing the results of beach litter surveys, to OSPAR beach litter data from seven beaches bordering on the south-eastern North Sea, revealed 23 significant trends in the abundances of beach litter types for the period 2009-2014. Litter Analyst revealed a large variation in the abundance of litter types between beaches. To reduce the effects of spatial variation, trend analysis of beach litter data can most effectively be performed at the beach or national level. Spatial aggregation of beach litter data within a region is possible, but resulted in a considerable reduction in the number of significant trends. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

Forensic analysis of Salvia divinorum using multivariate statistical procedures. Part I: discrimination from related Salvia species.

PubMed

Willard, Melissa A Bodnar; McGuffin, Victoria L; Smith, Ruth Waddell

2012-01-01

Salvia divinorum is a hallucinogenic herb that is internationally regulated. In this study, salvinorin A, the active compound in S. divinorum, was extracted from S. divinorum plant leaves using a 5-min extraction with dichloromethane. Four additional Salvia species (Salvia officinalis, Salvia guaranitica, Salvia splendens, and Salvia nemorosa) were extracted using this procedure, and all extracts were analyzed by gas chromatography-mass spectrometry. Differentiation of S. divinorum from other Salvia species was successful based on visual assessment of the resulting chromatograms. To provide a more objective comparison, the total ion chromatograms (TICs) were subjected to principal components analysis (PCA). Prior to PCA, the TICs were subjected to a series of data pretreatment procedures to minimize non-chemical sources of variance in the data set. Successful discrimination of S. divinorum from the other four Salvia species was possible based on visual assessment of the PCA scores plot. To provide a numerical assessment of the discrimination, a series of statistical procedures such as Euclidean distance measurement, hierarchical cluster analysis, Student's t tests, Wilcoxon rank-sum tests, and Pearson product moment correlation were also applied to the PCA scores. The statistical procedures were then compared to determine the advantages and disadvantages for forensic applications.

Valid Statistical Analysis for Logistic Regression with Multiple Sources

NASA Astrophysics Data System (ADS)

Fienberg, Stephen E.; Nardi, Yuval; Slavković, Aleksandra B.

Considerable effort has gone into understanding issues of privacy protection of individual information in single databases, and various solutions have been proposed depending on the nature of the data, the ways in which the database will be used and the precise nature of the privacy protection being offered. Once data are merged across sources, however, the nature of the problem becomes far more complex and a number of privacy issues arise for the linked individual files that go well beyond those that are considered with regard to the data within individual sources. In the paper, we propose an approach that gives full statistical analysis on the combined database without actually combining it. We focus mainly on logistic regression, but the method and tools described may be applied essentially to other statistical models as well.

Noise removing in encrypted color images by statistical analysis

NASA Astrophysics Data System (ADS)

Islam, N.; Puech, W.

2012-03-01

Cryptographic techniques are used to secure confidential data from unauthorized access but these techniques are very sensitive to noise. A single bit change in encrypted data can have catastrophic impact over the decrypted data. This paper addresses the problem of removing bit error in visual data which are encrypted using AES algorithm in the CBC mode. In order to remove the noise, a method is proposed which is based on the statistical analysis of each block during the decryption. The proposed method exploits local statistics of the visual data and confusion/diffusion properties of the encryption algorithm to remove the errors. Experimental results show that the proposed method can be used at the receiving end for the possible solution for noise removing in visual data in encrypted domain.

Statistical Analysis of Sport Movement Observations: the Case of Orienteering

NASA Astrophysics Data System (ADS)

Amouzandeh, K.; Karimipour, F.

2017-09-01

Study of movement observations is becoming more popular in several applications. Particularly, analyzing sport movement time series has been considered as a demanding area. However, most of the attempts made on analyzing movement sport data have focused on spatial aspects of movement to extract some movement characteristics, such as spatial patterns and similarities. This paper proposes statistical analysis of sport movement observations, which refers to analyzing changes in the spatial movement attributes (e.g. distance, altitude and slope) and non-spatial movement attributes (e.g. speed and heart rate) of athletes. As the case study, an example dataset of movement observations acquired during the "orienteering" sport is presented and statistically analyzed.

Statistical analysis of flight times for space shuttle ferry flights

NASA Technical Reports Server (NTRS)

Graves, M. E.; Perlmutter, M.

1974-01-01

Markov chain and Monte Carlo analysis techniques are applied to the simulated Space Shuttle Orbiter Ferry flights to obtain statistical distributions of flight time duration between Edwards Air Force Base and Kennedy Space Center. The two methods are compared, and are found to be in excellent agreement. The flights are subjected to certain operational and meteorological requirements, or constraints, which cause eastbound and westbound trips to yield different results. Persistence of events theory is applied to the occurrence of inclement conditions to find their effect upon the statistical flight time distribution. In a sensitivity test, some of the constraints are varied to observe the corresponding changes in the results.

Multivariate meta-analysis: a robust approach based on the theory of U-statistic.

PubMed

Ma, Yan; Mazumdar, Madhu

2011-10-30

Meta-analysis is the methodology for combining findings from similar research studies asking the same question. When the question of interest involves multiple outcomes, multivariate meta-analysis is used to synthesize the outcomes simultaneously taking into account the correlation between the outcomes. Likelihood-based approaches, in particular restricted maximum likelihood (REML) method, are commonly utilized in this context. REML assumes a multivariate normal distribution for the random-effects model. This assumption is difficult to verify, especially for meta-analysis with small number of component studies. The use of REML also requires iterative estimation between parameters, needing moderately high computation time, especially when the dimension of outcomes is large. A multivariate method of moments (MMM) is available and is shown to perform equally well to REML. However, there is a lack of information on the performance of these two methods when the true data distribution is far from normality. In this paper, we propose a new nonparametric and non-iterative method for multivariate meta-analysis on the basis of the theory of U-statistic and compare the properties of these three procedures under both normal and skewed data through simulation studies. It is shown that the effect on estimates from REML because of non-normal data distribution is marginal and that the estimates from MMM and U-statistic-based approaches are very similar. Therefore, we conclude that for performing multivariate meta-analysis, the U-statistic estimation procedure is a viable alternative to REML and MMM. Easy implementation of all three methods are illustrated by their application to data from two published meta-analysis from the fields of hip fracture and periodontal disease. We discuss ideas for future research based on U-statistic for testing significance of between-study heterogeneity and for extending the work to meta-regression setting. Copyright © 2011 John Wiley & Sons, Ltd.

Statistical analysis in MSW collection performance assessment.

PubMed

Teixeira, Carlos Afonso; Avelino, Catarina; Ferreira, Fátima; Bentes, Isabel

2014-09-01

The increase of Municipal Solid Waste (MSW) generated over the last years forces waste managers pursuing more effective collection schemes, technically viable, environmentally effective and economically sustainable. The assessment of MSW services using performance indicators plays a crucial role for improving service quality. In this work, we focus on the relevance of regular system monitoring as a service assessment tool. In particular, we select and test a core-set of MSW collection performance indicators (effective collection distance, effective collection time and effective fuel consumption) that highlights collection system strengths and weaknesses and supports pro-active management decision-making and strategic planning. A statistical analysis was conducted with data collected in mixed collection system of Oporto Municipality, Portugal, during one year, a week per month. This analysis provides collection circuits' operational assessment and supports effective short-term municipality collection strategies at the level of, e.g., collection frequency and timetables, and type of containers. Copyright © 2014 Elsevier Ltd. All rights reserved.

Statistical analysis of the MODIS atmosphere products for the Tomsk region

NASA Astrophysics Data System (ADS)

Afonin, Sergey V.; Belov, Vladimir V.; Engel, Marina V.

2005-10-01

The paper presents the results of using the MODIS Atmosphere Products satellite information to study the atmospheric characteristics (the aerosol and water vapor) in the Tomsk Region (56-61°N, 75-90°E) in 2001-2004. The satellite data were received from the NASA Goddard Distributed Active Archive Center (DAAC) through the INTERNET.To use satellite data for a solution of scientific and applied problems, it is very important to know their accuracy. Despite the results of validation of the MODIS data have already been available in the literature, we decided to carry out additional investigations for the Tomsk Region. The paper presents the results of validation of the aerosol optical thickness (AOT) and total column precipitable water (TCPW), which are in good agreement with the test data. The statistical analysis revealed some interesting facts. Thus, for example, analyzing the data on the spatial distribution of the average seasonal values of AOT or TCPW for 2001-2003 in the Tomsk Region, we established that instead of the expected spatial homogeneity of these distributions, they have similar spatial structures.

Statistical analysis of secondary particle distributions in relativistic nucleus-nucleus collisions

NASA Technical Reports Server (NTRS)

Mcguire, Stephen C.

1987-01-01

The use is described of several statistical techniques to characterize structure in the angular distributions of secondary particles from nucleus-nucleus collisions in the energy range 24 to 61 GeV/nucleon. The objective of this work was to determine whether there are correlations between emitted particle intensity and angle that may be used to support the existence of the quark gluon plasma. The techniques include chi-square null hypothesis tests, the method of discrete Fourier transform analysis, and fluctuation analysis. We have also used the method of composite unit vectors to test for azimuthal asymmetry in a data set of 63 JACEE-3 events. Each method is presented in a manner that provides the reader with some practical detail regarding its application. Of those events with relatively high statistics, Fe approaches 0 at 55 GeV/nucleon was found to possess an azimuthal distribution with a highly non-random structure. No evidence of non-statistical fluctuations was found in the pseudo-rapidity distributions of the events studied. It is seen that the most effective application of these methods relies upon the availability of many events or single events that possess very high multiplicities.

Statistical Analysis of Individual Participant Data Meta-Analyses: A Comparison of Methods and Recommendations for Practice

PubMed Central

Stewart, Gavin B.; Altman, Douglas G.; Askie, Lisa M.; Duley, Lelia; Simmonds, Mark C.; Stewart, Lesley A.

2012-01-01

Background Individual participant data (IPD) meta-analyses that obtain “raw” data from studies rather than summary data typically adopt a “two-stage” approach to analysis whereby IPD within trials generate summary measures, which are combined using standard meta-analytical methods. Recently, a range of “one-stage” approaches which combine all individual participant data in a single meta-analysis have been suggested as providing a more powerful and flexible approach. However, they are more complex to implement and require statistical support. This study uses a dataset to compare “two-stage” and “one-stage” models of varying complexity, to ascertain whether results obtained from the approaches differ in a clinically meaningful way. Methods and Findings We included data from 24 randomised controlled trials, evaluating antiplatelet agents, for the prevention of pre-eclampsia in pregnancy. We performed two-stage and one-stage IPD meta-analyses to estimate overall treatment effect and to explore potential treatment interactions whereby particular types of women and their babies might benefit differentially from receiving antiplatelets. Two-stage and one-stage approaches gave similar results, showing a benefit of using anti-platelets (Relative risk 0.90, 95% CI 0.84 to 0.97). Neither approach suggested that any particular type of women benefited more or less from antiplatelets. There were no material differences in results between different types of one-stage model. Conclusions For these data, two-stage and one-stage approaches to analysis produce similar results. Although one-stage models offer a flexible environment for exploring model structure and are useful where across study patterns relating to types of participant, intervention and outcome mask similar relationships within trials, the additional insights provided by their usage may not outweigh the costs of statistical support for routine application in syntheses of randomised controlled trials

RooStatsCms: A tool for analysis modelling, combination and statistical studies

NASA Astrophysics Data System (ADS)

Piparo, D.; Schott, G.; Quast, G.

2010-04-01

RooStatsCms is an object oriented statistical framework based on the RooFit technology. Its scope is to allow the modelling, statistical analysis and combination of multiple search channels for new phenomena in High Energy Physics. It provides a variety of methods described in literature implemented as classes, whose design is oriented to the execution of multiple CPU intensive jobs on batch systems or on the Grid.

Statistical shape analysis using 3D Poisson equation--A quantitatively validated approach.

PubMed

Gao, Yi; Bouix, Sylvain

2016-05-01

Statistical shape analysis has been an important area of research with applications in biology, anatomy, neuroscience, agriculture, paleontology, etc. Unfortunately, the proposed methods are rarely quantitatively evaluated, and as shown in recent studies, when they are evaluated, significant discrepancies exist in their outputs. In this work, we concentrate on the problem of finding the consistent location of deformation between two population of shapes. We propose a new shape analysis algorithm along with a framework to perform a quantitative evaluation of its performance. Specifically, the algorithm constructs a Signed Poisson Map (SPoM) by solving two Poisson equations on the volumetric shapes of arbitrary topology, and statistical analysis is then carried out on the SPoMs. The method is quantitatively evaluated on synthetic shapes and applied on real shape data sets in brain structures. Copyright © 2016 Elsevier B.V. All rights reserved.

For the Love of Statistics: Appreciating and Learning to Apply Experimental Analysis and Statistics through Computer Programming Activities

ERIC Educational Resources Information Center

Mascaró, Maite; Sacristán, Ana Isabel; Rufino, Marta M.

2016-01-01

For the past 4 years, we have been involved in a project that aims to enhance the teaching and learning of experimental analysis and statistics, of environmental and biological sciences students, through computational programming activities (using R code). In this project, through an iterative design, we have developed sequences of R-code-based…

STAPP: Spatiotemporal analysis of plantar pressure measurements using statistical parametric mapping.

PubMed

Booth, Brian G; Keijsers, Noël L W; Sijbers, Jan; Huysmans, Toon

2018-05-03

Pedobarography produces large sets of plantar pressure samples that are routinely subsampled (e.g. using regions of interest) or aggregated (e.g. center of pressure trajectories, peak pressure images) in order to simplify statistical analysis and provide intuitive clinical measures. We hypothesize that these data reductions discard gait information that can be used to differentiate between groups or conditions. To test the hypothesis of null information loss, we created an implementation of statistical parametric mapping (SPM) for dynamic plantar pressure datasets (i.e. plantar pressure videos). Our SPM software framework brings all plantar pressure videos into anatomical and temporal correspondence, then performs statistical tests at each sampling location in space and time. Novelly, we introduce non-linear temporal registration into the framework in order to normalize for timing differences within the stance phase. We refer to our software framework as STAPP: spatiotemporal analysis of plantar pressure measurements. Using STAPP, we tested our hypothesis on plantar pressure videos from 33 healthy subjects walking at different speeds. As walking speed increased, STAPP was able to identify significant decreases in plantar pressure at mid-stance from the heel through the lateral forefoot. The extent of these plantar pressure decreases has not previously been observed using existing plantar pressure analysis techniques. We therefore conclude that the subsampling of plantar pressure videos - a task which led to the discarding of gait information in our study - can be avoided using STAPP. Copyright © 2018 Elsevier B.V. All rights reserved.

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.

Vibroacoustic optimization using a statistical energy analysis model

NASA Astrophysics Data System (ADS)

Culla, Antonio; D`Ambrogio, Walter; Fregolent, Annalisa; Milana, Silvia

2016-08-01

In this paper, an optimization technique for medium-high frequency dynamic problems based on Statistical Energy Analysis (SEA) method is presented. Using a SEA model, the subsystem energies are controlled by internal loss factors (ILF) and coupling loss factors (CLF), which in turn depend on the physical parameters of the subsystems. A preliminary sensitivity analysis of subsystem energy to CLF's is performed to select CLF's that are most effective on subsystem energies. Since the injected power depends not only on the external loads but on the physical parameters of the subsystems as well, it must be taken into account under certain conditions. This is accomplished in the optimization procedure, where approximate relationships between CLF's, injected power and physical parameters are derived. The approach is applied on a typical aeronautical structure: the cabin of a helicopter.

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.

Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis: Preprint

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

Cheung, WanYin; Zhang, Jie; Florita, Anthony

2015-12-08

Uncertainties associated with solar forecasts present challenges to maintain grid reliability, especially at high solar penetrations. This study aims to quantify the errors associated with the day-ahead solar forecast parameters and the theoretical solar power output for a 51-kW solar power plant in a utility area in the state of Vermont, U.S. Forecasts were generated by three numerical weather prediction (NWP) models, including the Rapid Refresh, the High Resolution Rapid Refresh, and the North American Model, and a machine-learning ensemble model. A photovoltaic (PV) performance model was adopted to calculate theoretical solar power generation using the forecast parameters (e.g., irradiance,more » cell temperature, and wind speed). Errors of the power outputs were quantified using statistical moments and a suite of metrics, such as the normalized root mean squared error (NRMSE). In addition, the PV model's sensitivity to different forecast parameters was quantified and analyzed. Results showed that the ensemble model yielded forecasts in all parameters with the smallest NRMSE. The NRMSE of solar irradiance forecasts of the ensemble NWP model was reduced by 28.10% compared to the best of the three NWP models. Further, the sensitivity analysis indicated that the errors of the forecasted cell temperature attributed only approximately 0.12% to the NRMSE of the power output as opposed to 7.44% from the forecasted solar irradiance.« less

Statistical correlation analysis for comparing vibration data from test and analysis

NASA Technical Reports Server (NTRS)

Butler, T. G.; Strang, R. F.; Purves, L. R.; Hershfeld, D. J.

1986-01-01

A theory was developed to compare vibration modes obtained by NASTRAN analysis with those obtained experimentally. Because many more analytical modes can be obtained than experimental modes, the analytical set was treated as expansion functions for putting both sources in comparative form. The dimensional symmetry was developed for three general cases: nonsymmetric whole model compared with a nonsymmetric whole structural test, symmetric analytical portion compared with a symmetric experimental portion, and analytical symmetric portion with a whole experimental test. The theory was coded and a statistical correlation program was installed as a utility. The theory is established with small classical structures.

A Guideline to Univariate Statistical Analysis for LC/MS-Based Untargeted Metabolomics-Derived Data

PubMed Central

Vinaixa, Maria; Samino, Sara; Saez, Isabel; Duran, Jordi; Guinovart, Joan J.; Yanes, Oscar

2012-01-01

Several metabolomic software programs provide methods for peak picking, retention time alignment and quantification of metabolite features in LC/MS-based metabolomics. Statistical analysis, however, is needed in order to discover those features significantly altered between samples. By comparing the retention time and MS/MS data of a model compound to that from the altered feature of interest in the research sample, metabolites can be then unequivocally identified. This paper reports on a comprehensive overview of a workflow for statistical analysis to rank relevant metabolite features that will be selected for further MS/MS experiments. We focus on univariate data analysis applied in parallel on all detected features. Characteristics and challenges of this analysis are discussed and illustrated using four different real LC/MS untargeted metabolomic datasets. We demonstrate the influence of considering or violating mathematical assumptions on which univariate statistical test rely, using high-dimensional LC/MS datasets. Issues in data analysis such as determination of sample size, analytical variation, assumption of normality and homocedasticity, or correction for multiple testing are discussed and illustrated in the context of our four untargeted LC/MS working examples. PMID:24957762

A Guideline to Univariate Statistical Analysis for LC/MS-Based Untargeted Metabolomics-Derived Data.

PubMed

Vinaixa, Maria; Samino, Sara; Saez, Isabel; Duran, Jordi; Guinovart, Joan J; Yanes, Oscar

2012-10-18

Several metabolomic software programs provide methods for peak picking, retention time alignment and quantification of metabolite features in LC/MS-based metabolomics. Statistical analysis, however, is needed in order to discover those features significantly altered between samples. By comparing the retention time and MS/MS data of a model compound to that from the altered feature of interest in the research sample, metabolites can be then unequivocally identified. This paper reports on a comprehensive overview of a workflow for statistical analysis to rank relevant metabolite features that will be selected for further MS/MS experiments. We focus on univariate data analysis applied in parallel on all detected features. Characteristics and challenges of this analysis are discussed and illustrated using four different real LC/MS untargeted metabolomic datasets. We demonstrate the influence of considering or violating mathematical assumptions on which univariate statistical test rely, using high-dimensional LC/MS datasets. Issues in data analysis such as determination of sample size, analytical variation, assumption of normality and homocedasticity, or correction for multiple testing are discussed and illustrated in the context of our four untargeted LC/MS working examples.

Multi-scale statistical analysis of coronal solar activity

DOE PAGES

Gamborino, Diana; del-Castillo-Negrete, Diego; Martinell, Julio J.

2016-07-08

Multi-filter images from the solar corona are used to obtain temperature maps that are analyzed using techniques based on proper orthogonal decomposition (POD) in order to extract dynamical and structural information at various scales. Exploring active regions before and after a solar flare and comparing them with quiet regions, we show that the multi-scale behavior presents distinct statistical properties for each case that can be used to characterize the level of activity in a region. Information about the nature of heat transport is also to be extracted from the analysis.

Statistical testing and power analysis for brain-wide association study.

PubMed

Gong, Weikang; Wan, Lin; Lu, Wenlian; Ma, Liang; Cheng, Fan; Cheng, Wei; Grünewald, Stefan; Feng, Jianfeng

2018-04-05

The identification of connexel-wise associations, which involves examining functional connectivities between pairwise voxels across the whole brain, is both statistically and computationally challenging. Although such a connexel-wise methodology has recently been adopted by brain-wide association studies (BWAS) to identify connectivity changes in several mental disorders, such as schizophrenia, autism and depression, the multiple correction and power analysis methods designed specifically for connexel-wise analysis are still lacking. Therefore, we herein report the development of a rigorous statistical framework for connexel-wise significance testing based on the Gaussian random field theory. It includes controlling the family-wise error rate (FWER) of multiple hypothesis testings using topological inference methods, and calculating power and sample size for a connexel-wise study. Our theoretical framework can control the false-positive rate accurately, as validated empirically using two resting-state fMRI datasets. Compared with Bonferroni correction and false discovery rate (FDR), it can reduce false-positive rate and increase statistical power by appropriately utilizing the spatial information of fMRI data. Importantly, our method bypasses the need of non-parametric permutation to correct for multiple comparison, thus, it can efficiently tackle large datasets with high resolution fMRI images. The utility of our method is shown in a case-control study. Our approach can identify altered functional connectivities in a major depression disorder dataset, whereas existing methods fail. A software package is available at https://github.com/weikanggong/BWAS. Copyright © 2018 Elsevier B.V. All rights reserved.

Statistical analysis of magnetically soft particles in magnetorheological elastomers

NASA Astrophysics Data System (ADS)

Gundermann, T.; Cremer, P.; Löwen, H.; Menzel, A. M.; Odenbach, S.

2017-04-01

The physical properties of magnetorheological elastomers (MRE) are a complex issue and can be influenced and controlled in many ways, e.g. by applying a magnetic field, by external mechanical stimuli, or by an electric potential. In general, the response of MRE materials to these stimuli is crucially dependent on the distribution of the magnetic particles inside the elastomer. Specific knowledge of the interactions between particles or particle clusters is of high relevance for understanding the macroscopic rheological properties and provides an important input for theoretical calculations. In order to gain a better insight into the correlation between the macroscopic effects and microstructure and to generate a database for theoretical analysis, x-ray micro-computed tomography (X-μCT) investigations as a base for a statistical analysis of the particle configurations were carried out. Different MREs with quantities of 2-15 wt% (0.27-2.3 vol%) of iron powder and different allocations of the particles inside the matrix were prepared. The X-μCT results were edited by an image processing software regarding the geometrical properties of the particles with and without the influence of an external magnetic field. Pair correlation functions for the positions of the particles inside the elastomer were calculated to statistically characterize the distributions of the particles in the samples.

Wavelet analysis in ecology and epidemiology: impact of statistical tests

PubMed Central

Cazelles, Bernard; Cazelles, Kévin; Chavez, Mario

2014-01-01

Wavelet analysis is now frequently used to extract information from ecological and epidemiological time series. Statistical hypothesis tests are conducted on associated wavelet quantities to assess the likelihood that they are due to a random process. Such random processes represent null models and are generally based on synthetic data that share some statistical characteristics with the original time series. This allows the comparison of null statistics with those obtained from original time series. When creating synthetic datasets, different techniques of resampling result in different characteristics shared by the synthetic time series. Therefore, it becomes crucial to consider the impact of the resampling method on the results. We have addressed this point by comparing seven different statistical testing methods applied with different real and simulated data. Our results show that statistical assessment of periodic patterns is strongly affected by the choice of the resampling method, so two different resampling techniques could lead to two different conclusions about the same time series. Moreover, our results clearly show the inadequacy of resampling series generated by white noise and red noise that are nevertheless the methods currently used in the wide majority of wavelets applications. Our results highlight that the characteristics of a time series, namely its Fourier spectrum and autocorrelation, are important to consider when choosing the resampling technique. Results suggest that data-driven resampling methods should be used such as the hidden Markov model algorithm and the ‘beta-surrogate’ method. PMID:24284892

Wavelet analysis in ecology and epidemiology: impact of statistical tests.

PubMed

Cazelles, Bernard; Cazelles, Kévin; Chavez, Mario

2014-02-06

Wavelet analysis is now frequently used to extract information from ecological and epidemiological time series. Statistical hypothesis tests are conducted on associated wavelet quantities to assess the likelihood that they are due to a random process. Such random processes represent null models and are generally based on synthetic data that share some statistical characteristics with the original time series. This allows the comparison of null statistics with those obtained from original time series. When creating synthetic datasets, different techniques of resampling result in different characteristics shared by the synthetic time series. Therefore, it becomes crucial to consider the impact of the resampling method on the results. We have addressed this point by comparing seven different statistical testing methods applied with different real and simulated data. Our results show that statistical assessment of periodic patterns is strongly affected by the choice of the resampling method, so two different resampling techniques could lead to two different conclusions about the same time series. Moreover, our results clearly show the inadequacy of resampling series generated by white noise and red noise that are nevertheless the methods currently used in the wide majority of wavelets applications. Our results highlight that the characteristics of a time series, namely its Fourier spectrum and autocorrelation, are important to consider when choosing the resampling technique. Results suggest that data-driven resampling methods should be used such as the hidden Markov model algorithm and the 'beta-surrogate' method.

How Many Studies Do You Need? A Primer on Statistical Power for Meta-Analysis

ERIC Educational Resources Information Center

Valentine, Jeffrey C.; Pigott, Therese D.; Rothstein, Hannah R.

2010-01-01

In this article, the authors outline methods for using fixed and random effects power analysis in the context of meta-analysis. Like statistical power analysis for primary studies, power analysis for meta-analysis can be done either prospectively or retrospectively and requires assumptions about parameters that are unknown. The authors provide…

Statistical Analysis of CFD Solutions From the Fifth AIAA Drag Prediction Workshop

NASA Technical Reports Server (NTRS)

Morrison, Joseph H.

2013-01-01

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 North America, Europe, Asia, and South America using a common grid sequence and multiple turbulence models for the June 2012 fifth Drag Prediction Workshop sponsored by the AIAA Applied Aerodynamics Technical Committee. The aerodynamic configuration for this workshop was the Common Research Model subsonic transport wing-body previously used for the 4th Drag Prediction Workshop. 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 previous workshops.

University and student segmentation: multilevel latent-class analysis of students' attitudes towards research methods and statistics.

PubMed

Mutz, Rüdiger; Daniel, Hans-Dieter

2013-06-01

It is often claimed that psychology students' attitudes towards research methods and statistics affect course enrollment, persistence, achievement, and course climate. However, the inter-institutional variability has been widely neglected in the research on students' attitudes towards research methods and statistics, but it is important for didactic purposes (heterogeneity of the student population). The paper presents a scale based on findings of the social psychology of attitudes (polar and emotion-based concept) in conjunction with a method for capturing beginning university students' attitudes towards research methods and statistics and identifying the proportion of students having positive attitudes at the institutional level. The study based on a re-analysis of a nationwide survey in Germany in August 2000 of all psychology students that enrolled in fall 1999/2000 (N= 1,490) and N= 44 universities. Using multilevel latent-class analysis (MLLCA), the aim was to group students in different student attitude types and at the same time to obtain university segments based on the incidences of the different student attitude types. Four student latent clusters were found that can be ranked on a bipolar attitude dimension. Membership in a cluster was predicted by age, grade point average (GPA) on school-leaving exam, and personality traits. In addition, two university segments were found: universities with an average proportion of students with positive attitudes and universities with a high proportion of students with positive attitudes (excellent segment). As psychology students make up a very heterogeneous group, the use of multiple learning activities as opposed to the classical lecture course is required. © 2011 The British Psychological Society.

Teaching Statistics from the Operating Table: Minimally Invasive and Maximally Educational

ERIC Educational Resources Information Center

Nowacki, Amy S.

2015-01-01

Statistics courses that focus on data analysis in isolation, discounting the scientific inquiry process, may not motivate students to learn the subject. By involving students in other steps of the inquiry process, such as generating hypotheses and data, students may become more interested and vested in the analysis step. Additionally, such an…

Analysis of spontaneous MEG activity in mild cognitive impairment and Alzheimer's disease using spectral entropies and statistical complexity measures

NASA Astrophysics Data System (ADS)

Bruña, Ricardo; Poza, Jesús; Gómez, Carlos; García, María; Fernández, Alberto; Hornero, Roberto

2012-06-01

Alzheimer's disease (AD) is the most common cause of dementia. Over the last few years, a considerable effort has been devoted to exploring new biomarkers. Nevertheless, a better understanding of brain dynamics is still required to optimize therapeutic strategies. In this regard, the characterization of mild cognitive impairment (MCI) is crucial, due to the high conversion rate from MCI to AD. However, only a few studies have focused on the analysis of magnetoencephalographic (MEG) rhythms to characterize AD and MCI. In this study, we assess the ability of several parameters derived from information theory to describe spontaneous MEG activity from 36 AD patients, 18 MCI subjects and 26 controls. Three entropies (Shannon, Tsallis and Rényi entropies), one disequilibrium measure (based on Euclidean distance ED) and three statistical complexities (based on Lopez Ruiz-Mancini-Calbet complexity LMC) were used to estimate the irregularity and statistical complexity of MEG activity. Statistically significant differences between AD patients and controls were obtained with all parameters (p < 0.01). In addition, statistically significant differences between MCI subjects and controls were achieved by ED and LMC (p < 0.05). In order to assess the diagnostic ability of the parameters, a linear discriminant analysis with a leave-one-out cross-validation procedure was applied. The accuracies reached 83.9% and 65.9% to discriminate AD and MCI subjects from controls, respectively. Our findings suggest that MCI subjects exhibit an intermediate pattern of abnormalities between normal aging and AD. Furthermore, the proposed parameters provide a new description of brain dynamics in AD and MCI.

Metrology Optical Power Budgeting in SIM Using Statistical Analysis Techniques

NASA Technical Reports Server (NTRS)

Kuan, Gary M

2008-01-01

The Space Interferometry Mission (SIM) is a space-based stellar interferometry instrument, consisting of up to three interferometers, which will be capable of micro-arc second resolution. Alignment knowledge of the three interferometer baselines requires a three-dimensional, 14-leg truss with each leg being monitored by an external metrology gauge. In addition, each of the three interferometers requires an internal metrology gauge to monitor the optical path length differences between the two sides. Both external and internal metrology gauges are interferometry based, operating at a wavelength of 1319 nanometers. Each gauge has fiber inputs delivering measurement and local oscillator (LO) power, split into probe-LO and reference-LO beam pairs. These beams experience power loss due to a variety of mechanisms including, but not restricted to, design efficiency, material attenuation, element misalignment, diffraction, and coupling efficiency. Since the attenuation due to these sources may degrade over time, an accounting of the range of expected attenuation is needed so an optical power margin can be book kept. A method of statistical optical power analysis and budgeting, based on a technique developed for deep space RF telecommunications, is described in this paper and provides a numerical confidence level for having sufficient optical power relative to mission metrology performance requirements.

Categorical data processing for real estate objects valuation using statistical analysis

NASA Astrophysics Data System (ADS)

Parygin, D. S.; Malikov, V. P.; Golubev, A. V.; Sadovnikova, N. P.; Petrova, T. M.; Finogeev, A. G.

2018-05-01

Theoretical and practical approaches to the use of statistical methods for studying various properties of infrastructure objects are analyzed in the paper. Methods of forecasting the value of objects are considered. A method for coding categorical variables describing properties of real estate objects is proposed. The analysis of the results of modeling the price of real estate objects using regression analysis and an algorithm based on a comparative approach is carried out.

Statistical assessment on a combined analysis of GRYN-ROMN-UCBN upland vegetation vital signs

USGS Publications Warehouse

Irvine, Kathryn M.; Rodhouse, Thomas J.

2014-01-01

As of 2013, Rocky Mountain and Upper Columbia Basin Inventory and Monitoring Networks have multiple years of vegetation data and Greater Yellowstone Network has three years of vegetation data and monitoring is ongoing in all three networks. Our primary objective is to assess whether a combined analysis of these data aimed at exploring correlations with climate and weather data is feasible. We summarize the core survey design elements across protocols and point out the major statistical challenges for a combined analysis at present. The dissimilarity in response designs between ROMN and UCBN-GRYN network protocols presents a statistical challenge that has not been resolved yet. However, the UCBN and GRYN data are compatible as they implement a similar response design; therefore, a combined analysis is feasible and will be pursued in future. When data collected by different networks are combined, the survey design describing the merged dataset is (likely) a complex survey design. A complex survey design is the result of combining datasets from different sampling designs. A complex survey design is characterized by unequal probability sampling, varying stratification, and clustering (see Lohr 2010 Chapter 7 for general overview). Statistical analysis of complex survey data requires modifications to standard methods, one of which is to include survey design weights within a statistical model. We focus on this issue for a combined analysis of upland vegetation from these networks, leaving other topics for future research. We conduct a simulation study on the possible effects of equal versus unequal probability selection of points on parameter estimates of temporal trend using available packages within the R statistical computing package. We find that, as written, using lmer or lm for trend detection in a continuous response and clm and clmm for visually estimated cover classes with “raw” GRTS design weights specified for the weight argument leads to substantially

Statistical model to perform error analysis of curve fits of wind tunnel test data using the techniques of analysis of variance and regression analysis

NASA Technical Reports Server (NTRS)

Alston, D. W.

1981-01-01

The considered research had the objective to design a statistical model that could perform an error analysis of curve fits of wind tunnel test data using analysis of variance and regression analysis techniques. Four related subproblems were defined, and by solving each of these a solution to the general research problem was obtained. The capabilities of the evolved true statistical model are considered. The least squares fit is used to determine the nature of the force, moment, and pressure data. The order of the curve fit is increased in order to delete the quadratic effect in the residuals. The analysis of variance is used to determine the magnitude and effect of the error factor associated with the experimental data.

A statistical analysis of the daily streamflow hydrograph

NASA Astrophysics Data System (ADS)

Kavvas, M. L.; Delleur, J. W.

1984-03-01

In this study a periodic statistical analysis of daily streamflow data in Indiana, U.S.A., was performed to gain some new insight into the stochastic structure which describes the daily streamflow process. This analysis was performed by the periodic mean and covariance functions of the daily streamflows, by the time and peak discharge -dependent recession limb of the daily streamflow hydrograph, by the time and discharge exceedance level (DEL) -dependent probability distribution of the hydrograph peak interarrival time, and by the time-dependent probability distribution of the time to peak discharge. Some new statistical estimators were developed and used in this study. In general features, this study has shown that: (a) the persistence properties of daily flows depend on the storage state of the basin at the specified time origin of the flow process; (b) the daily streamflow process is time irreversible; (c) the probability distribution of the daily hydrograph peak interarrival time depends both on the occurrence time of the peak from which the inter-arrival time originates and on the discharge exceedance level; and (d) if the daily streamflow process is modeled as the release from a linear watershed storage, this release should depend on the state of the storage and on the time of the release as the persistence properties and the recession limb decay rates were observed to change with the state of the watershed storage and time. Therefore, a time-varying reservoir system needs to be considered if the daily streamflow process is to be modeled as the release from a linear watershed storage.

An Analysis Of Additive Manufacturing Production Problems And Solutions

DTIC Science & Technology

2016-12-01

democratization of manufacturing (Hornick, 2015). AM has three distinct advantages over subtractive manufacturing : product customization, design flexibility...58 develops software and other technology solutions for the design , analysis, testing, manufacture , and validation of products (Diane Ryan, personal...NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA MBA PROFESSIONAL REPORT AN ANALYSIS OF ADDITIVE MANUFACTURING PRODUCTION

Wavelet Statistical Analysis of Low-Latitude Geomagnetic Measurements

NASA Astrophysics Data System (ADS)

Papa, A. R.; Akel, A. F.

2009-05-01

Following previous works by our group (Papa et al., JASTP, 2006), where we analyzed a series of records acquired at the Vassouras National Geomagnetic Observatory in Brazil for the month of October 2000, we introduced a wavelet analysis for the same type of data and for other periods. It is well known that wavelets allow a more detailed study in several senses: the time window for analysis can be drastically reduced if compared to other traditional methods (Fourier, for example) and at the same time allow an almost continuous accompaniment of both amplitude and frequency of signals as time goes by. This advantage brings some possibilities for potentially useful forecasting methods of the type also advanced by our group in previous works (see for example, Papa and Sosman, JASTP, 2008). However, the simultaneous statistical analysis of both time series (in our case amplitude and frequency) is a challenging matter and is in this sense that we have found what we consider our main goal. Some possible trends for future works are advanced.

Web-Based Statistical Sampling and Analysis

ERIC Educational Resources Information Center

Quinn, Anne; Larson, Karen

2016-01-01

Consistent with the Common Core State Standards for Mathematics (CCSSI 2010), the authors write that they have asked students to do statistics projects with real data. To obtain real data, their students use the free Web-based app, Census at School, created by the American Statistical Association (ASA) to help promote civic awareness among school…

The sumLINK statistic for genetic linkage analysis in the presence of heterogeneity.

PubMed

Christensen, G B; Knight, S; Camp, N J

2009-11-01

We present the "sumLINK" statistic--the sum of multipoint LOD scores for the subset of pedigrees with nominally significant linkage evidence at a given locus--as an alternative to common methods to identify susceptibility loci in the presence of heterogeneity. We also suggest the "sumLOD" statistic (the sum of positive multipoint LOD scores) as a companion to the sumLINK. sumLINK analysis identifies genetic regions of extreme consistency across pedigrees without regard to negative evidence from unlinked or uninformative pedigrees. Significance is determined by an innovative permutation procedure based on genome shuffling that randomizes linkage information across pedigrees. This procedure for generating the empirical null distribution may be useful for other linkage-based statistics as well. Using 500 genome-wide analyses of simulated null data, we show that the genome shuffling procedure results in the correct type 1 error rates for both the sumLINK and sumLOD. The power of the statistics was tested using 100 sets of simulated genome-wide data from the alternative hypothesis from GAW13. Finally, we illustrate the statistics in an analysis of 190 aggressive prostate cancer pedigrees from the International Consortium for Prostate Cancer Genetics, where we identified a new susceptibility locus. We propose that the sumLINK and sumLOD are ideal for collaborative projects and meta-analyses, as they do not require any sharing of identifiable data between contributing institutions. Further, loci identified with the sumLINK have good potential for gene localization via statistical recombinant mapping, as, by definition, several linked pedigrees contribute to each peak.

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 Analysis for Collision-free Boson Sampling.

PubMed

Huang, He-Liang; Zhong, Han-Sen; Li, Tan; Li, Feng-Guang; Fu, Xiang-Qun; Zhang, Shuo; Wang, Xiang; Bao, Wan-Su

2017-11-10

Boson sampling is strongly believed to be intractable for classical computers but solvable with photons in linear optics, which raises widespread concern as a rapid way to demonstrate the quantum supremacy. However, due to its solution is mathematically unverifiable, how to certify the experimental results becomes a major difficulty in the boson sampling experiment. Here, we develop a statistical analysis scheme to experimentally certify the collision-free boson sampling. Numerical simulations are performed to show the feasibility and practicability of our scheme, and the effects of realistic experimental conditions are also considered, demonstrating that our proposed scheme is experimentally friendly. Moreover, our broad approach is expected to be generally applied to investigate multi-particle coherent dynamics beyond the boson sampling.

Statistical analysis plan of the head position in acute ischemic stroke trial pilot (HEADPOST pilot).

PubMed

Olavarría, Verónica V; Arima, Hisatomi; Anderson, Craig S; Brunser, Alejandro; Muñoz-Venturelli, Paula; Billot, Laurent; Lavados, Pablo M

2017-02-01

Background The HEADPOST Pilot is a proof-of-concept, open, prospective, multicenter, international, cluster randomized, phase IIb controlled trial, with masked outcome assessment. The trial will test if lying flat head position initiated in patients within 12 h of onset of acute ischemic stroke involving the anterior circulation increases cerebral blood flow in the middle cerebral arteries, as measured by transcranial Doppler. The study will also assess the safety and feasibility of patients lying flat for ≥24 h. The trial was conducted in centers in three countries, with ability to perform early transcranial Doppler. A feature of this trial was that patients were randomized to a certain position according to the month of admission to hospital. Objective To outline in detail the predetermined statistical analysis plan for HEADPOST Pilot study. Methods All data collected by participating researchers will be reviewed and formally assessed. Information pertaining to the baseline characteristics of patients, their process of care, and the delivery of treatments will be classified, and for each item, appropriate descriptive statistical analyses are planned with comparisons made between randomized groups. For the outcomes, statistical comparisons to be made between groups are planned and described. Results This statistical analysis plan was developed for the analysis of the results of the HEADPOST Pilot study to be transparent, available, verifiable, and predetermined before data lock. Conclusions We have developed a statistical analysis plan for the HEADPOST Pilot study which is to be followed to avoid analysis bias arising from prior knowledge of the study findings. Trial registration The study is registered under HEADPOST-Pilot, ClinicalTrials.gov Identifier NCT01706094.

A Bifactor Approach to Model Multifaceted Constructs in Statistical Mediation Analysis

ERIC Educational Resources Information Center

Gonzalez, Oscar; MacKinnon, David P.

2018-01-01

Statistical mediation analysis allows researchers to identify the most important mediating constructs in the causal process studied. Identifying specific mediators is especially relevant when the hypothesized mediating construct consists of multiple related facets. The general definition of the construct and its facets might relate differently to…

A wind proxy based on migrating dunes at the Baltic coast: statistical analysis of the link between wind conditions and sand movement

NASA Astrophysics Data System (ADS)

Bierstedt, Svenja E.; Hünicke, Birgit; Zorita, Eduardo; Ludwig, Juliane

2017-07-01

We statistically analyse the relationship between the structure of migrating dunes in the southern Baltic and the driving wind conditions over the past 26 years, with the long-term aim of using migrating dunes as a proxy for past wind conditions at an interannual resolution. The present analysis is based on the dune record derived from geo-radar measurements by Ludwig et al. (2017). The dune system is located at the Baltic Sea coast of Poland and is migrating from west to east along the coast. The dunes present layers with different thicknesses that can be assigned to absolute dates at interannual timescales and put in relation to seasonal wind conditions. To statistically analyse this record and calibrate it as a wind proxy, we used a gridded regional meteorological reanalysis data set (coastDat2) covering recent decades. The identified link between the dune annual layers and wind conditions was additionally supported by the co-variability between dune layers and observed sea level variations in the southern Baltic Sea. We include precipitation and temperature into our analysis, in addition to wind, to learn more about the dependency between these three atmospheric factors and their common influence on the dune system. We set up a statistical linear model based on the correlation between the frequency of days with specific wind conditions in a given season and dune migration velocities derived for that season. To some extent, the dune records can be seen as analogous to tree-ring width records, and hence we use a proxy validation method usually applied in dendrochronology, cross-validation with the leave-one-out method, when the observational record is short. The revealed correlations between the wind record from the reanalysis and the wind record derived from the dune structure is in the range between 0.28 and 0.63, yielding similar statistical validation skill as dendroclimatological records.

mapDIA: Preprocessing and statistical analysis of quantitative proteomics data from data independent acquisition mass spectrometry.

PubMed

Teo, Guoshou; Kim, Sinae; Tsou, Chih-Chiang; Collins, Ben; Gingras, Anne-Claude; Nesvizhskii, Alexey I; Choi, Hyungwon

2015-11-03

Data independent acquisition (DIA) mass spectrometry is an emerging technique that offers more complete detection and quantification of peptides and proteins across multiple samples. DIA allows fragment-level quantification, which can be considered as repeated measurements of the abundance of the corresponding peptides and proteins in the downstream statistical analysis. However, few statistical approaches are available for aggregating these complex fragment-level data into peptide- or protein-level statistical summaries. In this work, we describe a software package, mapDIA, for statistical analysis of differential protein expression using DIA fragment-level intensities. The workflow consists of three major steps: intensity normalization, peptide/fragment selection, and statistical analysis. First, mapDIA offers normalization of fragment-level intensities by total intensity sums as well as a novel alternative normalization by local intensity sums in retention time space. Second, mapDIA removes outlier observations and selects peptides/fragments that preserve the major quantitative patterns across all samples for each protein. Last, using the selected fragments and peptides, mapDIA performs model-based statistical significance analysis of protein-level differential expression between specified groups of samples. Using a comprehensive set of simulation datasets, we show that mapDIA detects differentially expressed proteins with accurate control of the false discovery rates. We also describe the analysis procedure in detail using two recently published DIA datasets generated for 14-3-3β dynamic interaction network and prostate cancer glycoproteome. The software was written in C++ language and the source code is available for free through SourceForge website http://sourceforge.net/projects/mapdia/.This article is part of a Special Issue entitled: Computational Proteomics. Copyright © 2015 Elsevier B.V. All rights reserved.

2017 Annual Disability Statistics Supplement

ERIC Educational Resources Information Center

Lauer, E. A; Houtenville, A. J.

2018-01-01

The "Annual Disability Statistics Supplement" is a companion report to the "Annual Disability Statistics Compendium." The "Supplement" presents statistics on the same topics as the "Compendium," with additional categorizations by demographic characteristics including age, gender and race/ethnicity. In…

Statistics 101 for Radiologists.

PubMed

Anvari, Arash; Halpern, Elkan F; Samir, Anthony E

2015-10-01

Diagnostic tests have wide clinical applications, including screening, diagnosis, measuring treatment effect, and determining prognosis. Interpreting diagnostic test results requires an understanding of key statistical concepts used to evaluate test efficacy. This review explains descriptive statistics and discusses probability, including mutually exclusive and independent events and conditional probability. In the inferential statistics section, a statistical perspective on study design is provided, together with an explanation of how to select appropriate statistical tests. Key concepts in recruiting study samples are discussed, including representativeness and random sampling. Variable types are defined, including predictor, outcome, and covariate variables, and the relationship of these variables to one another. In the hypothesis testing section, we explain how to determine if observed differences between groups are likely to be due to chance. We explain type I and II errors, statistical significance, and study power, followed by an explanation of effect sizes and how confidence intervals can be used to generalize observed effect sizes to the larger population. Statistical tests are explained in four categories: t tests and analysis of variance, proportion analysis tests, nonparametric tests, and regression techniques. We discuss sensitivity, specificity, accuracy, receiver operating characteristic analysis, and likelihood ratios. Measures of reliability and agreement, including κ statistics, intraclass correlation coefficients, and Bland-Altman graphs and analysis, are introduced. © RSNA, 2015.

ROOT — A C++ framework for petabyte data storage, statistical analysis and visualization

NASA Astrophysics Data System (ADS)

Antcheva, I.; Ballintijn, M.; Bellenot, B.; Biskup, M.; Brun, R.; Buncic, N.; Canal, Ph.; Casadei, D.; Couet, O.; Fine, V.; Franco, L.; Ganis, G.; Gheata, A.; Maline, D. Gonzalez; Goto, M.; Iwaszkiewicz, J.; Kreshuk, A.; Segura, D. Marcos; Maunder, R.; Moneta, L.; Naumann, A.; Offermann, E.; Onuchin, V.; Panacek, S.; Rademakers, F.; Russo, P.; Tadel, M.

2009-12-01

ROOT is an object-oriented C++ framework conceived in the high-energy physics (HEP) community, designed for storing and analyzing petabytes of data in an efficient way. Any instance of a C++ class can be stored into a ROOT file in a machine-independent compressed binary format. In ROOT the TTree object container is optimized for statistical data analysis over very large data sets by using vertical data storage techniques. These containers can span a large number of files on local disks, the web, or a number of different shared file systems. In order to analyze this data, the user can chose out of a wide set of mathematical and statistical functions, including linear algebra classes, numerical algorithms such as integration and minimization, and various methods for performing regression analysis (fitting). In particular, the RooFit package allows the user to perform complex data modeling and fitting while the RooStats library provides abstractions and implementations for advanced statistical tools. Multivariate classification methods based on machine learning techniques are available via the TMVA package. A central piece in these analysis tools are the histogram classes which provide binning of one- and multi-dimensional data. Results can be saved in high-quality graphical formats like Postscript and PDF or in bitmap formats like JPG or GIF. The result can also be stored into ROOT macros that allow a full recreation and rework of the graphics. Users typically create their analysis macros step by step, making use of the interactive C++ interpreter CINT, while running over small data samples. Once the development is finished, they can run these macros at full compiled speed over large data sets, using on-the-fly compilation, or by creating a stand-alone batch program. Finally, if processing farms are available, the user can reduce the execution time of intrinsically parallel tasks — e.g. data mining in HEP — by using PROOF, which will take care of optimally

Statistical Analysis of Spectral Properties and Prosodic Parameters of Emotional Speech

NASA Astrophysics Data System (ADS)

Přibil, J.; Přibilová, A.

2009-01-01

The paper addresses reflection of microintonation and spectral properties in male and female acted emotional speech. Microintonation component of speech melody is analyzed regarding its spectral and statistical parameters. According to psychological research of emotional speech, different emotions are accompanied by different spectral noise. We control its amount by spectral flatness according to which the high frequency noise is mixed in voiced frames during cepstral speech synthesis. Our experiments are aimed at statistical analysis of cepstral coefficient values and ranges of spectral flatness in three emotions (joy, sadness, anger), and a neutral state for comparison. Calculated histograms of spectral flatness distribution are visually compared and modelled by Gamma probability distribution. Histograms of cepstral coefficient distribution are evaluated and compared using skewness and kurtosis. Achieved statistical results show good correlation comparing male and female voices for all emotional states portrayed by several Czech and Slovak professional actors.

Multivariate statistical analysis of wildfires in Portugal

NASA Astrophysics Data System (ADS)

Costa, Ricardo; Caramelo, Liliana; Pereira, Mário

2013-04-01

Several studies demonstrate that wildfires in Portugal present high temporal and spatial variability as well as cluster behavior (Pereira et al., 2005, 2011). This study aims to contribute to the characterization of the fire regime in Portugal with the multivariate statistical analysis of the time series of number of fires and area burned in Portugal during the 1980 - 2009 period. The data used in the analysis is an extended version of the Rural Fire Portuguese Database (PRFD) (Pereira et al, 2011), provided by the National Forest Authority (Autoridade Florestal Nacional, AFN), the Portuguese Forest Service, which includes information for more than 500,000 fire records. There are many multiple advanced techniques for examining the relationships among multiple time series at the same time (e.g., canonical correlation analysis, principal components analysis, factor analysis, path analysis, multiple analyses of variance, clustering systems). This study compares and discusses the results obtained with these different techniques. Pereira, M.G., Trigo, R.M., DaCamara, C.C., Pereira, J.M.C., Leite, S.M., 2005: "Synoptic patterns associated with large summer forest fires in Portugal". Agricultural and Forest Meteorology. 129, 11-25. Pereira, M. G., Malamud, B. D., Trigo, R. M., and Alves, P. I.: The history and characteristics of the 1980-2005 Portuguese rural fire database, Nat. Hazards Earth Syst. Sci., 11, 3343-3358, doi:10.5194/nhess-11-3343-2011, 2011 This work is supported by European Union Funds (FEDER/COMPETE - Operational Competitiveness Programme) and by national funds (FCT - Portuguese Foundation for Science and Technology) under the project FCOMP-01-0124-FEDER-022692, the project FLAIR (PTDC/AAC-AMB/104702/2008) and the EU 7th Framework Program through FUME (contract number 243888).

Statistical Literacy: Developing a Youth and Adult Education Statistical Project

ERIC Educational Resources Information Center

Conti, Keli Cristina; Lucchesi de Carvalho, Dione

2014-01-01

This article focuses on the notion of literacy--general and statistical--in the analysis of data from a fieldwork research project carried out as part of a master's degree that investigated the teaching and learning of statistics in adult education mathematics classes. We describe the statistical context of the project that involved the…

[Methods of statistical analysis in differential diagnostics of the degree of brain glioma anaplasia during preoperative stage].

PubMed

Glavatskiĭ, A Ia; Guzhovskaia, N V; Lysenko, S N; Kulik, A V

2005-12-01

The authors proposed a possible preoperative diagnostics of the degree of supratentorial brain gliom anaplasia using statistical analysis methods. It relies on a complex examination of 934 patients with I-IV degree anaplasias, which had been treated in the Institute of Neurosurgery from 1990 to 2004. The use of statistical analysis methods for differential diagnostics of the degree of brain gliom anaplasia may optimize a diagnostic algorithm, increase reliability of obtained data and in some cases avoid carrying out irrational operative intrusions. Clinically important signs for the use of statistical analysis methods directed to preoperative diagnostics of brain gliom anaplasia have been defined

Statistics without Tears: Complex Statistics with Simple Arithmetic

ERIC Educational Resources Information Center

Smith, Brian

2011-01-01

One of the often overlooked aspects of modern statistics is the analysis of time series data. Modern introductory statistics courses tend to rush to probabilistic applications involving risk and confidence. Rarely does the first level course linger on such useful and fascinating topics as time series decomposition, with its practical applications…

[The research protocol VI: How to choose the appropriate statistical test. Inferential statistics].

PubMed

Flores-Ruiz, Eric; Miranda-Novales, María Guadalupe; Villasís-Keever, Miguel Ángel

2017-01-01

The statistical analysis can be divided in two main components: descriptive analysis and inferential analysis. An inference is to elaborate conclusions from the tests performed with the data obtained from a sample of a population. Statistical tests are used in order to establish the probability that a conclusion obtained from a sample is applicable to the population from which it was obtained. However, choosing the appropriate statistical test in general poses a challenge for novice researchers. To choose the statistical test it is necessary to take into account three aspects: the research design, the number of measurements and the scale of measurement of the variables. Statistical tests are divided into two sets, parametric and nonparametric. Parametric tests can only be used if the data show a normal distribution. Choosing the right statistical test will make it easier for readers to understand and apply the results.

DHLAS: A web-based information system for statistical genetic analysis of HLA population data.

PubMed

Thriskos, P; Zintzaras, E; Germenis, A

2007-03-01

DHLAS (database HLA system) is a user-friendly, web-based information system for the analysis of human leukocyte antigens (HLA) data from population studies. DHLAS has been developed using JAVA and the R system, it runs on a Java Virtual Machine and its user-interface is web-based powered by the servlet engine TOMCAT. It utilizes STRUTS, a Model-View-Controller framework and uses several GNU packages to perform several of its tasks. The database engine it relies upon for fast access is MySQL, but others can be used a well. The system estimates metrics, performs statistical testing and produces graphs required for HLA population studies: (i) Hardy-Weinberg equilibrium (calculated using both asymptotic and exact tests), (ii) genetics distances (Euclidian or Nei), (iii) phylogenetic trees using the unweighted pair group method with averages and neigbor-joining method, (iv) linkage disequilibrium (pairwise and overall, including variance estimations), (v) haplotype frequencies (estimate using the expectation-maximization algorithm) and (vi) discriminant analysis. The main merit of DHLAS is the incorporation of a database, thus, the data can be stored and manipulated along with integrated genetic data analysis procedures. In addition, it has an open architecture allowing the inclusion of other functions and procedures.

Statistical mechanics of economics I

NASA Astrophysics Data System (ADS)

Kusmartsev, F. V.

2011-02-01

We show that statistical mechanics is useful in the description of financial crisis and economics. Taking a large amount of instant snapshots of a market over an interval of time we construct their ensembles and study their statistical interference. This results in a probability description of the market and gives capital, money, income, wealth and debt distributions, which in the most cases takes the form of the Bose-Einstein distribution. In addition, statistical mechanics provides the main market equations and laws which govern the correlations between the amount of money, debt, product, prices and number of retailers. We applied the found relations to a study of the evolution of the economics in USA between the years 1996 to 2008 and observe that over that time the income of a major population is well described by the Bose-Einstein distribution which parameters are different for each year. Each financial crisis corresponds to a peak in the absolute activity coefficient. The analysis correctly indicates the past crises and predicts the future one.

[The principal components analysis--method to classify the statistical variables with applications in medicine].

PubMed

Dascălu, Cristina Gena; Antohe, Magda Ecaterina

2009-01-01

Based on the eigenvalues and the eigenvectors analysis, the principal component analysis has the purpose to identify the subspace of the main components from a set of parameters, which are enough to characterize the whole set of parameters. Interpreting the data for analysis as a cloud of points, we find through geometrical transformations the directions where the cloud's dispersion is maximal--the lines that pass through the cloud's center of weight and have a maximal density of points around them (by defining an appropriate criteria function and its minimization. This method can be successfully used in order to simplify the statistical analysis on questionnaires--because it helps us to select from a set of items only the most relevant ones, which cover the variations of the whole set of data. For instance, in the presented sample we started from a questionnaire with 28 items and, applying the principal component analysis we identified 7 principal components--or main items--fact that simplifies significantly the further data statistical analysis.

Re-Evaluation of Event Correlations in Virtual California Using Statistical Analysis

NASA Astrophysics Data System (ADS)

Glasscoe, M. T.; Heflin, M. B.; Granat, R. A.; Yikilmaz, M. B.; Heien, E.; Rundle, J.; Donnellan, A.

2010-12-01

Fusing the results of simulation tools with statistical analysis methods has contributed to our better understanding of the earthquake process. In a previous study, we used a statistical method to investigate emergent phenomena in data produced by the Virtual California earthquake simulator. The analysis indicated that there were some interesting fault interactions and possible triggering and quiescence relationships between events. We have converted the original code from Matlab to python/C++ and are now evaluating data from the most recent version of Virtual California in order to analyze and compare any new behavior exhibited by the model. The Virtual California earthquake simulator can be used to study fault and stress interaction scenarios for realistic California earthquakes. The simulation generates a synthetic earthquake catalog of events with a minimum size of ~M 5.8 that can be evaluated using statistical analysis methods. Virtual California utilizes realistic fault geometries and a simple Amontons - Coulomb stick and slip friction law in order to drive the earthquake process by means of a back-slip model where loading of each segment occurs due to the accumulation of a slip deficit at the prescribed slip rate of the segment. Like any complex system, Virtual California may generate emergent phenomena unexpected even by its designers. In order to investigate this, we have developed a statistical method that analyzes the interaction between Virtual California fault elements and thereby determine whether events on any given fault elements show correlated behavior. Our method examines events on one fault element and then determines whether there is an associated event within a specified time window on a second fault element. Note that an event in our analysis is defined as any time an element slips, rather than any particular “earthquake” along the entire fault length. Results are then tabulated and then differenced with an expected correlation

Statistical analysis of the 70 meter antenna surface distortions

NASA Technical Reports Server (NTRS)

Kiedron, K.; Chian, C. T.; Chuang, K. L.

1987-01-01

Statistical analysis of surface distortions of the 70 meter NASA/JPL antenna, located at Goldstone, was performed. The purpose of this analysis is to verify whether deviations due to gravity loading can be treated as quasi-random variables with normal distribution. Histograms of the RF pathlength error distribution for several antenna elevation positions were generated. The results indicate that the deviations from the ideal antenna surface are not normally distributed. The observed density distribution for all antenna elevation angles is taller and narrower than the normal density, which results in large positive values of kurtosis and a significant amount of skewness. The skewness of the distribution changes from positive to negative as the antenna elevation changes from zenith to horizon.

Processes and subdivisions in diogenites, a multivariate statistical analysis

NASA Technical Reports Server (NTRS)

Harriott, T. A.; Hewins, R. H.

1984-01-01

Multivariate statistical techniques used on diogenite orthopyroxene analyses show the relationships that occur within diogenites and the two orthopyroxenite components (class I and II) in the polymict diogenite Garland. Cluster analysis shows that only Peckelsheim is similar to Garland class I (Fe-rich) and the other diogenites resemble Garland class II. The unique diogenite Y 75032 may be related to type I by fractionation. Factor analysis confirms the subdivision and shows that Fe does not correlate with the weakly incompatible elements across the entire pyroxene composition range, indicating that igneous fractionation is not the process controlling total diogenite composition variation. The occurrence of two groups of diogenites is interpreted as the result of sampling or mixing of two main sequences of orthopyroxene cumulates with slightly different compositions.

Analysis/forecast experiments with a multivariate statistical analysis scheme using FGGE data

NASA Technical Reports Server (NTRS)

Baker, W. E.; Bloom, S. C.; Nestler, M. S.

1985-01-01

A three-dimensional, multivariate, statistical analysis method, optimal interpolation (OI) is described for modeling meteorological data from widely dispersed sites. The model was developed to analyze FGGE data at the NASA-Goddard Laboratory of Atmospherics. The model features a multivariate surface analysis over the oceans, including maintenance of the Ekman balance and a geographically dependent correlation function. Preliminary comparisons are made between the OI model and similar schemes employed at the European Center for Medium Range Weather Forecasts and the National Meteorological Center. The OI scheme is used to provide input to a GCM, and model error correlations are calculated for forecasts of 500 mb vertical water mixing ratios and the wind profiles. Comparisons are made between the predictions and measured data. The model is shown to be as accurate as a successive corrections model out to 4.5 days.

On an additive partial correlation operator and nonparametric estimation of graphical models.

PubMed

Lee, Kuang-Yao; Li, Bing; Zhao, Hongyu

2016-09-01

We introduce an additive partial correlation operator as an extension of partial correlation to the nonlinear setting, and use it to develop a new estimator for nonparametric graphical models. Our graphical models are based on additive conditional independence, a statistical relation that captures the spirit of conditional independence without having to resort to high-dimensional kernels for its estimation. The additive partial correlation operator completely characterizes additive conditional independence, and has the additional advantage of putting marginal variation on appropriate scales when evaluating interdependence, which leads to more accurate statistical inference. We establish the consistency of the proposed estimator. Through simulation experiments and analysis of the DREAM4 Challenge dataset, we demonstrate that our method performs better than existing methods in cases where the Gaussian or copula Gaussian assumption does not hold, and that a more appropriate scaling for our method further enhances its performance.

On an additive partial correlation operator and nonparametric estimation of graphical models

PubMed Central

Li, Bing; Zhao, Hongyu

2016-01-01

Abstract We introduce an additive partial correlation operator as an extension of partial correlation to the nonlinear setting, and use it to develop a new estimator for nonparametric graphical models. Our graphical models are based on additive conditional independence, a statistical relation that captures the spirit of conditional independence without having to resort to high-dimensional kernels for its estimation. The additive partial correlation operator completely characterizes additive conditional independence, and has the additional advantage of putting marginal variation on appropriate scales when evaluating interdependence, which leads to more accurate statistical inference. We establish the consistency of the proposed estimator. Through simulation experiments and analysis of the DREAM4 Challenge dataset, we demonstrate that our method performs better than existing methods in cases where the Gaussian or copula Gaussian assumption does not hold, and that a more appropriate scaling for our method further enhances its performance. PMID:29422689

Statistical analysis of regulatory ecotoxicity tests.

PubMed

Isnard, P; Flammarion, P; Roman, G; Babut, M; Bastien, P; Bintein, S; Esserméant, L; Férard, J F; Gallotti-Schmitt, S; Saouter, E; Saroli, M; Thiébaud, H; Tomassone, R; Vindimian, E

2001-11-01

ANOVA-type data analysis, i.e.. determination of lowest-observed-effect concentrations (LOECs), and no-observed-effect concentrations (NOECs), has been widely used for statistical analysis of chronic ecotoxicity data. However, it is more and more criticised for several reasons, among which the most important is probably the fact that the NOEC depends on the choice of test concentrations and number of replications and rewards poor experiments, i.e., high variability, with high NOEC values. Thus, a recent OECD workshop concluded that the use of the NOEC should be phased out and that a regression-based estimation procedure should be used. Following this workshop, a working group was established at the French level between government, academia and industry representatives. Twenty-seven sets of chronic data (algae, daphnia, fish) were collected and analysed by ANOVA and regression procedures. Several regression models were compared and relations between NOECs and ECx, for different values of x, were established in order to find an alternative summary parameter to the NOEC. Biological arguments are scarce to help in defining a negligible level of effect x for the ECx. With regard to their use in the risk assessment procedures, a convenient methodology would be to choose x so that ECx are on average similar to the present NOEC. This would lead to no major change in the risk assessment procedure. However, experimental data show that the ECx depend on the regression models and that their accuracy decreases in the low effect zone. This disadvantage could probably be reduced by adapting existing experimental protocols but it could mean more experimental effort and higher cost. ECx (derived with existing test guidelines, e.g., regarding the number of replicates) whose lowest bounds of the confidence interval are on average similar to present NOEC would improve this approach by a priori encouraging more precise experiments. However, narrow confidence intervals are not only

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

Statistical analysis of ultrasonic measurements in concrete

NASA Astrophysics Data System (ADS)

Chiang, Chih-Hung; Chen, Po-Chih

2002-05-01

Stress wave techniques such as measurements of ultrasonic pulse velocity are often used to evaluate concrete quality in structures. For proper interpretation of measurement results, the dependence of pulse transit time on the average acoustic impedance and the material homogeneity along the sound path need to be examined. Semi-direct measurement of pulse velocity could be more convenient than through transmission measurement. It is not necessary to assess both sides of concrete floors or walls. A novel measurement scheme is proposed and verified based on statistical analysis. It is shown that Semi-direct measurements are very effective for gathering large amount of pulse velocity data from concrete reference specimens. The variability of measurements is comparable with that reported by American Concrete Institute using either break-off or pullout tests.

Additional EIPC Study Analysis. Final Report

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

Hadley, Stanton W; Gotham, Douglas J.; Luciani, Ralph L.

Between 2010 and 2012 the Eastern Interconnection Planning Collaborative (EIPC) conducted a major long-term resource and transmission study of the Eastern Interconnection (EI). With guidance from a Stakeholder Steering Committee (SSC) that included representatives from the Eastern Interconnection States Planning Council (EISPC) among others, the project was conducted in two phases. Phase 1 involved a long-term capacity expansion analysis that involved creation of eight major futures plus 72 sensitivities. Three scenarios were selected for more extensive transmission- focused evaluation in Phase 2. Five power flow analyses, nine production cost model runs (including six sensitivities), and three capital cost estimations weremore » developed during this second phase. The results from Phase 1 and 2 provided a wealth of data that could be examined further to address energy-related questions. A list of 14 topics was developed for further analysis. This paper brings together the earlier interim reports of the first 13 topics plus one additional topic into a single final report.« less

75 FR 24718 - Guidance for Industry on Documenting Statistical Analysis Programs and Data Files; Availability

Federal Register 2010, 2011, 2012, 2013, 2014

2010-05-05

...] Guidance for Industry on Documenting Statistical Analysis Programs and Data Files; Availability AGENCY... Programs and Data Files.'' This guidance is provided to inform study statisticians of recommendations for documenting statistical analyses and data files submitted to the Center for Veterinary Medicine (CVM) for the...

Automatic Generation of Algorithms for the Statistical Analysis of Planetary Nebulae Images

NASA Technical Reports Server (NTRS)

Fischer, Bernd

2004-01-01

Analyzing data sets collected in experiments or by observations is a Core scientific activity. Typically, experimentd and observational data are &aught with uncertainty, and the analysis is based on a statistical model of the conjectured underlying processes, The large data volumes collected by modern instruments make computer support indispensible for this. Consequently, scientists spend significant amounts of their time with the development and refinement of the data analysis programs. AutoBayes [GF+02, FS03] is a fully automatic synthesis system for generating statistical data analysis programs. Externally, it looks like a compiler: it takes an abstract problem specification and translates it into executable code. Its input is a concise description of a data analysis problem in the form of a statistical model as shown in Figure 1; its output is optimized and fully documented C/C++ code which can be linked dynamically into the Matlab and Octave environments. Internally, however, it is quite different: AutoBayes derives a customized algorithm implementing the given model using a schema-based process, and then further refines and optimizes the algorithm into code. A schema is a parameterized code template with associated semantic constraints which define and restrict the template s applicability. The schema parameters are instantiated in a problem-specific way during synthesis as AutoBayes checks the constraints against the original model or, recursively, against emerging sub-problems. AutoBayes schema library contains problem decomposition operators (which are justified by theorems in a formal logic in the domain of Bayesian networks) as well as machine learning algorithms (e.g., EM, k-Means) and nu- meric optimization methods (e.g., Nelder-Mead simplex, conjugate gradient). AutoBayes augments this schema-based approach by symbolic computation to derive closed-form solutions whenever possible. This is a major advantage over other statistical data analysis systems

Statistical Tutorial | Center for Cancer Research

Cancer.gov

Recent advances in cancer biology have resulted in the need for increased statistical analysis of research data.  ST is designed as a follow up to Statistical Analysis of Research Data (SARD) held in April 2018.  The tutorial will apply the general principles of statistical analysis of research data including descriptive statistics, z- and t-tests of means and mean

Combine bivariate statistics analysis and multivariate statistics analysis to assess landslide susceptibility in Chen-Yu-Lan watershed, Nantou, Taiwan.

NASA Astrophysics Data System (ADS)

Ngan Nguyen, Thi To; Liu, Cheng-Chien

2013-04-01

How landslides occurred and which factors triggered and sped up landslide occurrences were usually asked by researchers in the past decades. Many investigations carried out in many places in the world to finding out methods that predict and prevent damages from landslides phenomena. Chen-Yu-Lan River watershed is reputed as a 'hot pot' of landslide researches in Taiwan by its complicated geological structures with the significant tectonic fault systems and steeply mountainous terrain. Beside annual high precipitation concentration and the abrupt slopes, some natural disaster, as typhoons (Sinlaku-2008, Kalmaegi-2008, and Marakot-2009) and earthquake (Chi-Chi earthquake-1999) are also the triggered factors cause landslides with serious damages in this place. This research expresses the quantitative approaches to generate landslide susceptible map for Chen-Yu-Lan watershed, a mountainous area in the central Taiwan. Landslide inventories data, which were detected from the Formosat-2 imageries for eight years from 2004 to 2011, were applied to carry out landslide susceptibility mapping. Bivariate statistics analysis and multivariate statistics analysis would be applied to calculate susceptible index of landslides. The weights of parameters were computed based on landslide data for eight years from 2004 to 2011. To validate effective levels of factors to landslide occurrences, this method built some multivariate algorithms and compared these results with real landslide occurrences. Besides this method, the historical data of landslides were also used to assess and classify landslide susceptibility levels. From long-term landslide data, relation between landslide susceptibility levels and landslide repetition was assigned. The results demonstrated differently effective levels of potential factors, such as, slope gradient, drainage density, lithology and land use to landslide phenomena. The results also showed logical relationship between weights and characteristics of

Statistical analysis of cascading failures in power grids

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

Chertkov, Michael; Pfitzner, Rene; Turitsyn, Konstantin

2010-12-01

We introduce a new microscopic model of cascading failures in transmission power grids. This model accounts for automatic response of the grid to load fluctuations that take place on the scale of minutes, when optimum power flow adjustments and load shedding controls are unavailable. We describe extreme events, caused by load fluctuations, which cause cascading failures of loads, generators and lines. Our model is quasi-static in the causal, discrete time and sequential resolution of individual failures. The model, in its simplest realization based on the Directed Current description of the power flow problem, is tested on three standard IEEE systemsmore » consisting of 30, 39 and 118 buses. Our statistical analysis suggests a straightforward classification of cascading and islanding phases in terms of the ratios between average number of removed loads, generators and links. The analysis also demonstrates sensitivity to variations in line capacities. Future research challenges in modeling and control of cascading outages over real-world power networks are discussed.« less

A guide to statistical analysis in microbial ecology: a community-focused, living review of multivariate data analyses.

PubMed

Buttigieg, Pier Luigi; Ramette, Alban

2014-12-01

The application of multivariate statistical analyses has become a consistent feature in microbial ecology. However, many microbial ecologists are still in the process of developing a deep understanding of these methods and appreciating their limitations. As a consequence, staying abreast of progress and debate in this arena poses an additional challenge to many microbial ecologists. To address these issues, we present the GUide to STatistical Analysis in Microbial Ecology (GUSTA ME): a dynamic, web-based resource providing accessible descriptions of numerous multivariate techniques relevant to microbial ecologists. A combination of interactive elements allows users to discover and navigate between methods relevant to their needs and examine how they have been used by others in the field. We have designed GUSTA ME to become a community-led and -curated service, which we hope will provide a common reference and forum to discuss and disseminate analytical techniques relevant to the microbial ecology community. © 2014 The Authors. FEMS Microbiology Ecology published by John Wiley & Sons Ltd on behalf of Federation of European Microbiological Societies.

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.

MethVisual - visualization and exploratory statistical analysis of DNA methylation profiles from bisulfite sequencing.

PubMed

Zackay, Arie; Steinhoff, Christine

2010-12-15

Exploration of DNA methylation and its impact on various regulatory mechanisms has become a very active field of research. Simultaneously there is an arising need for tools to process and analyse the data together with statistical investigation and visualisation. MethVisual is a new application that enables exploratory analysis and intuitive visualization of DNA methylation data as is typically generated by bisulfite sequencing. The package allows the import of DNA methylation sequences, aligns them and performs quality control comparison. It comprises basic analysis steps as lollipop visualization, co-occurrence display of methylation of neighbouring and distant CpG sites, summary statistics on methylation status, clustering and correspondence analysis. The package has been developed for methylation data but can be also used for other data types for which binary coding can be inferred. The application of the package, as well as a comparison to existing DNA methylation analysis tools and its workflow based on two datasets is presented in this paper. The R package MethVisual offers various analysis procedures for data that can be binarized, in particular for bisulfite sequenced methylation data. R/Bioconductor has become one of the most important environments for statistical analysis of various types of biological and medical data. Therefore, any data analysis within R that allows the integration of various data types as provided from different technological platforms is convenient. It is the first and so far the only specific package for DNA methylation analysis, in particular for bisulfite sequenced data available in R/Bioconductor enviroment. The package is available for free at http://methvisual.molgen.mpg.de/ and from the Bioconductor Consortium http://www.bioconductor.org.

MethVisual - visualization and exploratory statistical analysis of DNA methylation profiles from bisulfite sequencing

PubMed Central

2010-01-01

Background Exploration of DNA methylation and its impact on various regulatory mechanisms has become a very active field of research. Simultaneously there is an arising need for tools to process and analyse the data together with statistical investigation and visualisation. Findings MethVisual is a new application that enables exploratory analysis and intuitive visualization of DNA methylation data as is typically generated by bisulfite sequencing. The package allows the import of DNA methylation sequences, aligns them and performs quality control comparison. It comprises basic analysis steps as lollipop visualization, co-occurrence display of methylation of neighbouring and distant CpG sites, summary statistics on methylation status, clustering and correspondence analysis. The package has been developed for methylation data but can be also used for other data types for which binary coding can be inferred. The application of the package, as well as a comparison to existing DNA methylation analysis tools and its workflow based on two datasets is presented in this paper. Conclusions The R package MethVisual offers various analysis procedures for data that can be binarized, in particular for bisulfite sequenced methylation data. R/Bioconductor has become one of the most important environments for statistical analysis of various types of biological and medical data. Therefore, any data analysis within R that allows the integration of various data types as provided from different technological platforms is convenient. It is the first and so far the only specific package for DNA methylation analysis, in particular for bisulfite sequenced data available in R/Bioconductor enviroment. The package is available for free at http://methvisual.molgen.mpg.de/ and from the Bioconductor Consortium http://www.bioconductor.org. PMID:21159174

A Statistical Analysis of the Output Signals of an Acousto-Optic Spectrum Analyzer for CW (Continuous-Wave) Signals

DTIC Science & Technology

1988-10-01

A statistical analysis on the output signals of an acousto - optic spectrum analyzer (AOSA) is performed for the case when the input signal is a...processing, Electronic warfare, Radar countermeasures, Acousto - optic , Spectrum analyzer, Statistical analysis, Detection, Estimation, Canada, Modelling.

Extreme value statistics analysis of fracture strengths of a sintered silicon nitride failing from pores

NASA Technical Reports Server (NTRS)

Chao, Luen-Yuan; Shetty, Dinesh K.

1992-01-01

Statistical analysis and correlation between pore-size distribution and fracture strength distribution using the theory of extreme-value statistics is presented for a sintered silicon nitride. The pore-size distribution on a polished surface of this material was characterized, using an automatic optical image analyzer. The distribution measured on the two-dimensional plane surface was transformed to a population (volume) distribution, using the Schwartz-Saltykov diameter method. The population pore-size distribution and the distribution of the pore size at the fracture origin were correllated by extreme-value statistics. Fracture strength distribution was then predicted from the extreme-value pore-size distribution, usin a linear elastic fracture mechanics model of annular crack around pore and the fracture toughness of the ceramic. The predicted strength distribution was in good agreement with strength measurements in bending. In particular, the extreme-value statistics analysis explained the nonlinear trend in the linearized Weibull plot of measured strengths without postulating a lower-bound strength.

Statistical analysis of sparse infection data and its implications for retroviral treatment trials in primates.

PubMed Central

Spouge, J L

1992-01-01

Reports on retroviral primate trials rarely publish any statistical analysis. Present statistical methodology lacks appropriate tests for these trials and effectively discourages quantitative assessment. This paper describes the theory behind VACMAN, a user-friendly computer program that calculates statistics for in vitro and in vivo infectivity data. VACMAN's analysis applies to many retroviral trials using i.v. challenges and is valid whenever the viral dose-response curve has a particular shape. Statistics from actual i.v. retroviral trials illustrate some unappreciated principles of effective animal use: dilutions other than 1:10 can improve titration accuracy; infecting titration animals at the lowest doses possible can lower challenge doses; and finally, challenging test animals in small trials with more virus than controls safeguards against false successes, "reuses" animals, and strengthens experimental conclusions. The theory presented also explains the important concept of viral saturation, a phenomenon that may cause in vitro and in vivo titrations to agree for some retroviral strains and disagree for others. PMID:1323844

Spectral Analysis of B Stars: An Application of Bayesian Statistics

NASA Astrophysics Data System (ADS)

Mugnes, J.-M.; Robert, C.

2012-12-01

To better understand the processes involved in stellar physics, it is necessary to obtain accurate stellar parameters (effective temperature, surface gravity, abundances…). Spectral analysis is a powerful tool for investigating stars, but it is also vital to reduce uncertainties at a decent computational cost. Here we present a spectral analysis method based on a combination of Bayesian statistics and grids of synthetic spectra obtained with TLUSTY. This method simultaneously constrains the stellar parameters by using all the lines accessible in observed spectra and thus greatly reduces uncertainties and improves the overall spectrum fitting. Preliminary results are shown using spectra from the Observatoire du Mont-Mégantic.

Grain-Size Based Additivity Models for Scaling Multi-rate Uranyl Surface Complexation in Subsurface Sediments

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

Zhang, Xiaoying; Liu, Chongxuan; Hu, Bill X.

This study statistically analyzed a grain-size based additivity model that has been proposed to scale reaction rates and parameters from laboratory to field. The additivity model assumed that reaction properties in a sediment including surface area, reactive site concentration, reaction rate, and extent can be predicted from field-scale grain size distribution by linearly adding reaction properties for individual grain size fractions. This study focused on the statistical analysis of the additivity model with respect to reaction rate constants using multi-rate uranyl (U(VI)) surface complexation reactions in a contaminated sediment as an example. Experimental data of rate-limited U(VI) desorption in amore » stirred flow-cell reactor were used to estimate the statistical properties of multi-rate parameters for individual grain size fractions. The statistical properties of the rate constants for the individual grain size fractions were then used to analyze the statistical properties of the additivity model to predict rate-limited U(VI) desorption in the composite sediment, and to evaluate the relative importance of individual grain size fractions to the overall U(VI) desorption. The result indicated that the additivity model provided a good prediction of the U(VI) desorption in the composite sediment. However, the rate constants were not directly scalable using the additivity model, and U(VI) desorption in individual grain size fractions have to be simulated in order to apply the additivity model. An approximate additivity model for directly scaling rate constants was subsequently proposed and evaluated. The result found that the approximate model provided a good prediction of the experimental results within statistical uncertainty. This study also found that a gravel size fraction (2-8mm), which is often ignored in modeling U(VI) sorption and desorption, is statistically significant to the U(VI) desorption in the sediment.« less

Computed Tomography Inspection and Analysis for Additive Manufacturing Components

NASA Technical Reports Server (NTRS)

Beshears, Ronald D.

2016-01-01

Computed tomography (CT) inspection was performed on test articles additively manufactured from metallic materials. Metallic AM and machined wrought alloy test articles with programmed flaws were inspected using a 2MeV linear accelerator based CT system. Performance of CT inspection on identically configured wrought and AM components and programmed flaws was assessed using standard image analysis techniques to determine the impact of additive manufacturing on inspectability of objects with complex geometries.

Use of Statistical Analyses in the Ophthalmic Literature

PubMed Central

Lisboa, Renato; Meira-Freitas, Daniel; Tatham, Andrew J.; Marvasti, Amir H.; Sharpsten, Lucie; Medeiros, Felipe A.

2014-01-01

Purpose To identify the most commonly used statistical analyses in the ophthalmic literature and to determine the likely gain in comprehension of the literature that readers could expect if they were to sequentially add knowledge of more advanced techniques to their statistical repertoire. Design Cross-sectional study Methods All articles published from January 2012 to December 2012 in Ophthalmology, American Journal of Ophthalmology and Archives of Ophthalmology were reviewed. A total of 780 peer-reviewed articles were included. Two reviewers examined each article and assigned categories to each one depending on the type of statistical analyses used. Discrepancies between reviewers were resolved by consensus. Main Outcome Measures Total number and percentage of articles containing each category of statistical analysis were obtained. Additionally we estimated the accumulated number and percentage of articles that a reader would be expected to be able to interpret depending on their statistical repertoire. Results Readers with little or no statistical knowledge would be expected to be able to interpret the statistical methods presented in only 20.8% of articles. In order to understand more than half (51.4%) of the articles published, readers were expected to be familiar with at least 15 different statistical methods. Knowledge of 21 categories of statistical methods was necessary to comprehend 70.9% of articles, while knowledge of more than 29 categories was necessary to comprehend more than 90% of articles. Articles in retina and glaucoma subspecialties showed a tendency for using more complex analysis when compared to cornea. Conclusions Readers of clinical journals in ophthalmology need to have substantial knowledge of statistical methodology to understand the results of published studies in the literature. The frequency of use of complex statistical analyses also indicates that those involved in the editorial peer-review process must have sound statistical

Water quality management using statistical analysis and time-series prediction model

NASA Astrophysics Data System (ADS)

Parmar, Kulwinder Singh; Bhardwaj, Rashmi

2014-12-01

This paper deals with water quality management using statistical analysis and time-series prediction model. The monthly variation of water quality standards has been used to compare statistical mean, median, mode, standard deviation, kurtosis, skewness, coefficient of variation at Yamuna River. Model validated using R-squared, root mean square error, mean absolute percentage error, maximum absolute percentage error, mean absolute error, maximum absolute error, normalized Bayesian information criterion, Ljung-Box analysis, predicted value and confidence limits. Using auto regressive integrated moving average model, future water quality parameters values have been estimated. It is observed that predictive model is useful at 95 % confidence limits and curve is platykurtic for potential of hydrogen (pH), free ammonia, total Kjeldahl nitrogen, dissolved oxygen, water temperature (WT); leptokurtic for chemical oxygen demand, biochemical oxygen demand. Also, it is observed that predicted series is close to the original series which provides a perfect fit. All parameters except pH and WT cross the prescribed limits of the World Health Organization /United States Environmental Protection Agency, and thus water is not fit for drinking, agriculture and industrial use.

Quantifying the Energy Landscape Statistics in Proteins - a Relaxation Mode Analysis

NASA Astrophysics Data System (ADS)

Cai, Zhikun; Zhang, Yang

Energy landscape, the hypersurface in the configurational space, has been a useful concept in describing complex processes that occur over a very long time scale, such as the multistep slow relaxations of supercooled liquids and folding of polypeptide chains into structured proteins. Despite extensive simulation studies, its experimental characterization still remains a challenge. To address this challenge, we developed a relaxation mode analysis (RMA) for liquids under a framework analogous to the normal mode analysis for solids. Using RMA, important statistics of the activation barriers of the energy landscape becomes accessible from experimentally measurable two-point correlation functions, e.g. using quasi-elastic and inelastic scattering experiments. We observed a prominent coarsening effect of the energy landscape. The results were further confirmed by direct sampling of the energy landscape using a metadynamics-like adaptive autonomous basin climbing computation. We first demonstrate RMA in a supercooled liquid when dynamical cooperativity emerges in the landscape-influenced regime. Then we show this framework reveals encouraging energy landscape statistics when applied to proteins.

Statistical analysis of the calibration procedure for personnel radiation measurement instruments

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

Bush, W.J.; Bengston, S.J.; Kalbeitzer, F.L.

1980-11-01

Thermoluminescent analyzer (TLA) calibration procedures were used to estimate personnel radiation exposure levels at the Idaho National Engineering Laboratory (INEL). A statistical analysis is presented herein based on data collected over a six month period in 1979 on four TLA's located in the Department of Energy (DOE) Radiological and Environmental Sciences Laboratory at the INEL. The data were collected according to the day-to-day procedure in effect at that time. Both gamma and beta radiation models are developed. Observed TLA readings of thermoluminescent dosimeters are correlated with known radiation levels. This correlation is then used to predict unknown radiation doses frommore » future analyzer readings of personnel thermoluminescent dosimeters. The statistical techniques applied in this analysis include weighted linear regression, estimation of systematic and random error variances, prediction interval estimation using Scheffe's theory of calibration, the estimation of the ratio of the means of two normal bivariate distributed random variables and their corresponding confidence limits according to Kendall and Stuart, tests of normality, experimental design, a comparison between instruments, and quality control.« less

Batch reporting of forest inventory statistics using the EVALIDator

Treesearch

Patrick D. Miles

2015-01-01

The EVALIDator Web application, developed in 2007, provides estimates and sampling errors of forest statistics (e.g., forest area, number of trees, tree biomass) from data stored in the Forest Inventory and Analysis database. In response to user demand, new features have been added to the EVALIDator. The most recent additions are 1) the ability to generate multiple...

Statistical methods for the analysis of climate extremes

NASA Astrophysics Data System (ADS)

Naveau, Philippe; Nogaj, Marta; Ammann, Caspar; Yiou, Pascal; Cooley, Daniel; Jomelli, Vincent

2005-08-01

Currently there is an increasing research activity in the area of climate extremes because they represent a key manifestation of non-linear systems and an enormous impact on economic and social human activities. Our understanding of the mean behavior of climate and its 'normal' variability has been improving significantly during the last decades. In comparison, climate extreme events have been hard to study and even harder to predict because they are, by definition, rare and obey different statistical laws than averages. In this context, the motivation for this paper is twofold. Firstly, we recall the basic principles of Extreme Value Theory that is used on a regular basis in finance and hydrology, but it still does not have the same success in climate studies. More precisely, the theoretical distributions of maxima and large peaks are recalled. The parameters of such distributions are estimated with the maximum likelihood estimation procedure that offers the flexibility to take into account explanatory variables in our analysis. Secondly, we detail three case-studies to show that this theory can provide a solid statistical foundation, specially when assessing the uncertainty associated with extreme events in a wide range of applications linked to the study of our climate. To cite this article: P. Naveau et al., C. R. Geoscience 337 (2005).

Considerations in the statistical analysis of clinical trials in periodontitis.

PubMed

Imrey, P B

1986-05-01

Adult periodontitis has been described as a chronic infectious process exhibiting sporadic, acute exacerbations which cause quantal, localized losses of dental attachment. Many analytic problems of periodontal trials are similar to those of other chronic diseases. However, the episodic, localized, infrequent, and relatively unpredictable behavior of exacerbations, coupled with measurement error difficulties, cause some specific problems. Considerable controversy exists as to the proper selection and treatment of multiple site data from the same patient for group comparisons for epidemiologic or therapeutic evaluative purposes. This paper comments, with varying degrees of emphasis, on several issues pertinent to the analysis of periodontal trials. Considerable attention is given to the ways in which measurement variability may distort analytic results. Statistical treatments of multiple site data for descriptive summaries are distinguished from treatments for formal statistical inference to validate therapeutic effects. Evidence suggesting that sites behave independently is contested. For inferential analyses directed at therapeutic or preventive effects, analytic models based on site independence are deemed unsatisfactory. Methods of summarization that may yield more powerful analyses than all-site mean scores, while retaining appropriate treatment of inter-site associations, are suggested. Brief comments and opinions on an assortment of other issues in clinical trial analysis are preferred.

Statistical approach to partial equilibrium analysis

NASA Astrophysics Data System (ADS)

Wang, Yougui; Stanley, H. E.

2009-04-01

A statistical approach to market equilibrium and efficiency analysis is proposed in this paper. One factor that governs the exchange decisions of traders in a market, named willingness price, is highlighted and constitutes the whole theory. The supply and demand functions are formulated as the distributions of corresponding willing exchange over the willingness price. The laws of supply and demand can be derived directly from these distributions. The characteristics of excess demand function are analyzed and the necessary conditions for the existence and uniqueness of equilibrium point of the market are specified. The rationing rates of buyers and sellers are introduced to describe the ratio of realized exchange to willing exchange, and their dependence on the market price is studied in the cases of shortage and surplus. The realized market surplus, which is the criterion of market efficiency, can be written as a function of the distributions of willing exchange and the rationing rates. With this approach we can strictly prove that a market is efficient in the state of equilibrium.

Directions for new developments on statistical design and analysis of small population group trials.

PubMed

Hilgers, Ralf-Dieter; Roes, Kit; Stallard, Nigel

2016-06-14

Most statistical design and analysis methods for clinical trials have been developed and evaluated where at least several hundreds of patients could be recruited. These methods may not be suitable to evaluate therapies if the sample size is unavoidably small, which is usually termed by small populations. The specific sample size cut off, where the standard methods fail, needs to be investigated. In this paper, the authors present their view on new developments for design and analysis of clinical trials in small population groups, where conventional statistical methods may be inappropriate, e.g., because of lack of power or poor adherence to asymptotic approximations due to sample size restrictions. Following the EMA/CHMP guideline on clinical trials in small populations, we consider directions for new developments in the area of statistical methodology for design and analysis of small population clinical trials. We relate the findings to the research activities of three projects, Asterix, IDeAl, and InSPiRe, which have received funding since 2013 within the FP7-HEALTH-2013-INNOVATION-1 framework of the EU. As not all aspects of the wide research area of small population clinical trials can be addressed, we focus on areas where we feel advances are needed and feasible. The general framework of the EMA/CHMP guideline on small population clinical trials stimulates a number of research areas. These serve as the basis for the three projects, Asterix, IDeAl, and InSPiRe, which use various approaches to develop new statistical methodology for design and analysis of small population clinical trials. Small population clinical trials refer to trials with a limited number of patients. Small populations may result form rare diseases or specific subtypes of more common diseases. New statistical methodology needs to be tailored to these specific situations. The main results from the three projects will constitute a useful toolbox for improved design and analysis of small

Statistics of high-level scene context

PubMed Central

Greene, Michelle R.

2013-01-01

Context is critical for recognizing environments and for searching for objects within them: contextual associations have been shown to modulate reaction time and object recognition accuracy, as well as influence the distribution of eye movements and patterns of brain activations. However, we have not yet systematically quantified the relationships between objects and their scene environments. Here I seek to fill this gap by providing descriptive statistics of object-scene relationships. A total of 48, 167 objects were hand-labeled in 3499 scenes using the LabelMe tool (Russell et al., 2008). From these data, I computed a variety of descriptive statistics at three different levels of analysis: the ensemble statistics that describe the density and spatial distribution of unnamed “things” in the scene; the bag of words level where scenes are described by the list of objects contained within them; and the structural level where the spatial distribution and relationships between the objects are measured. The utility of each level of description for scene categorization was assessed through the use of linear classifiers, and the plausibility of each level for modeling human scene categorization is discussed. Of the three levels, ensemble statistics were found to be the most informative (per feature), and also best explained human patterns of categorization errors. Although a bag of words classifier had similar performance to human observers, it had a markedly different pattern of errors. However, certain objects are more useful than others, and ceiling classification performance could be achieved using only the 64 most informative objects. As object location tends not to vary as a function of category, structural information provided little additional information. Additionally, these data provide valuable information on natural scene redundancy that can be exploited for machine vision, and can help the visual cognition community to design experiments guided by

Statistics of high-level scene context.

PubMed

Greene, Michelle R

2013-01-01

CONTEXT IS CRITICAL FOR RECOGNIZING ENVIRONMENTS AND FOR SEARCHING FOR OBJECTS WITHIN THEM: contextual associations have been shown to modulate reaction time and object recognition accuracy, as well as influence the distribution of eye movements and patterns of brain activations. However, we have not yet systematically quantified the relationships between objects and their scene environments. Here I seek to fill this gap by providing descriptive statistics of object-scene relationships. A total of 48, 167 objects were hand-labeled in 3499 scenes using the LabelMe tool (Russell et al., 2008). From these data, I computed a variety of descriptive statistics at three different levels of analysis: the ensemble statistics that describe the density and spatial distribution of unnamed "things" in the scene; the bag of words level where scenes are described by the list of objects contained within them; and the structural level where the spatial distribution and relationships between the objects are measured. The utility of each level of description for scene categorization was assessed through the use of linear classifiers, and the plausibility of each level for modeling human scene categorization is discussed. Of the three levels, ensemble statistics were found to be the most informative (per feature), and also best explained human patterns of categorization errors. Although a bag of words classifier had similar performance to human observers, it had a markedly different pattern of errors. However, certain objects are more useful than others, and ceiling classification performance could be achieved using only the 64 most informative objects. As object location tends not to vary as a function of category, structural information provided little additional information. Additionally, these data provide valuable information on natural scene redundancy that can be exploited for machine vision, and can help the visual cognition community to design experiments guided by statistics

Multivariate Statistical Analysis of Water Quality data in Indian River Lagoon, Florida

NASA Astrophysics Data System (ADS)

Sayemuzzaman, M.; Ye, M.

2015-12-01

The Indian River Lagoon, is part of the longest barrier island complex in the United States, is a region of particular concern to the environmental scientist because of the rapid rate of human development throughout the region and the geographical position in between the colder temperate zone and warmer sub-tropical zone. Thus, the surface water quality analysis in this region always brings the newer information. In this present study, multivariate statistical procedures were applied to analyze the spatial and temporal water quality in the Indian River Lagoon over the period 1998-2013. Twelve parameters have been analyzed on twelve key water monitoring stations in and beside the lagoon on monthly datasets (total of 27,648 observations). The dataset was treated using cluster analysis (CA), principle component analysis (PCA) and non-parametric trend analysis. The CA was used to cluster twelve monitoring stations into four groups, with stations on the similar surrounding characteristics being in the same group. The PCA was then applied to the similar groups to find the important water quality parameters. The principal components (PCs), PC1 to PC5 was considered based on the explained cumulative variances 75% to 85% in each cluster groups. Nutrient species (phosphorus and nitrogen), salinity, specific conductivity and erosion factors (TSS, Turbidity) were major variables involved in the construction of the PCs. Statistical significant positive or negative trends and the abrupt trend shift were detected applying Mann-Kendall trend test and Sequential Mann-Kendall (SQMK), for each individual stations for the important water quality parameters. Land use land cover change pattern, local anthropogenic activities and extreme climate such as drought might be associated with these trends. This study presents the multivariate statistical assessment in order to get better information about the quality of surface water. Thus, effective pollution control/management of the surface

Statistical process management: An essential element of quality improvement

NASA Astrophysics Data System (ADS)

Buckner, M. R.

Successful quality improvement requires a balanced program involving the three elements that control quality: organization, people and technology. The focus of the SPC/SPM User's Group is to advance the technology component of Total Quality by networking within the Group and by providing an outreach within Westinghouse to foster the appropriate use of statistic techniques to achieve Total Quality. SPM encompasses the disciplines by which a process is measured against its intrinsic design capability, in the face of measurement noise and other obscuring variability. SPM tools facilitate decisions about the process that generated the data. SPM deals typically with manufacturing processes, but with some flexibility of definition and technique it accommodates many administrative processes as well. The techniques of SPM are those of Statistical Process Control, Statistical Quality Control, Measurement Control, and Experimental Design. In addition, techniques such as job and task analysis, and concurrent engineering are important elements of systematic planning and analysis that are needed early in the design process to ensure success. The SPC/SPM User's Group is endeavoring to achieve its objectives by sharing successes that have occurred within the member's own Westinghouse department as well as within other US and foreign industry. In addition, failures are reviewed to establish lessons learned in order to improve future applications. In broader terms, the Group is interested in making SPM the accepted way of doing business within Westinghouse.

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

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 Analysis of Human Body Movement and Group Interactions in Response to Music

NASA Astrophysics Data System (ADS)

Desmet, Frank; Leman, Marc; Lesaffre, Micheline; de Bruyn, Leen

Quantification of time series that relate to physiological data is challenging for empirical music research. Up to now, most studies have focused on time-dependent responses of individual subjects in controlled environments. However, little is known about time-dependent responses of between-subject interactions in an ecological context. This paper provides new findings on the statistical analysis of group synchronicity in response to musical stimuli. Different statistical techniques were applied to time-dependent data obtained from an experiment on embodied listening in individual and group settings. Analysis of inter group synchronicity are described. Dynamic Time Warping (DTW) and Cross Correlation Function (CCF) were found to be valid methods to estimate group coherence of the resulting movements. It was found that synchronicity of movements between individuals (human-human interactions) increases significantly in the social context. Moreover, Analysis of Variance (ANOVA) revealed that the type of music is the predominant factor in both the individual and the social context.

Primarily Statistics: Developing an Introductory Statistics Course for Pre-Service Elementary Teachers

ERIC Educational Resources Information Center

Green, Jennifer L.; Blankenship, Erin E.

2013-01-01

We developed an introductory statistics course for pre-service elementary teachers. In this paper, we describe the goals and structure of the course, as well as the assessments we implemented. Additionally, we use example course work to demonstrate pre-service teachers' progress both in learning statistics and as novice teachers. Overall, the…

Statistical Diversions

ERIC Educational Resources Information Center

Petocz, Peter; Sowey, Eric

2012-01-01

The term "data snooping" refers to the practice of choosing which statistical analyses to apply to a set of data after having first looked at those data. Data snooping contradicts a fundamental precept of applied statistics, that the scheme of analysis is to be planned in advance. In this column, the authors shall elucidate the…

Compilation and Analysis of 20 and 30 GHz Rain Fade Events at the ACTS NASA Ground Station: Statistics and Model Assessment

NASA Technical Reports Server (NTRS)

Manning, Robert M.

1996-01-01

The purpose of the propagation studies within the ACTS Project Office is to acquire 20 and 30 GHz rain fade statistics using the ACTS beacon links received at the NGS (NASA Ground Station) in Cleveland. Other than the raw, statistically unprocessed rain fade events that occur in real time, relevant rain fade statistics derived from such events are the cumulative rain fade statistics as well as fade duration statistics (beyond given fade thresholds) over monthly and yearly time intervals. Concurrent with the data logging exercise, monthly maximum rainfall levels recorded at the US Weather Service at Hopkins Airport are appended to the database to facilitate comparison of observed fade statistics with those predicted by the ACTS Rain Attenuation Model. Also, the raw fade data will be in a format, complete with documentation, for use by other investigators who require realistic fade event evolution in time for simulation purposes or further analysis for comparisons with other rain fade prediction models, etc. The raw time series data from the 20 and 30 GHz beacon signals is purged of non relevant data intervals where no rain fading has occurred. All other data intervals which contain rain fade events are archived with the accompanying time stamps. The definition of just what constitutes a rain fade event will be discussed later. The archived data serves two purposes. First, all rain fade event data is recombined into a contiguous data series every month and every year; this will represent an uninterrupted record of the actual (i.e., not statistically processed) temporal evolution of rain fade at 20 and 30 GHz at the location of the NGS. The second purpose of the data in such a format is to enable a statistical analysis of prevailing propagation parameters such as cumulative distributions of attenuation on a monthly and yearly basis as well as fade duration probabilities below given fade thresholds, also on a monthly and yearly basis. In addition, various subsidiary

Progress of statistical analysis in biomedical research through the historical review of the development of the Framingham score.

PubMed

Ignjatović, Aleksandra; Stojanović, Miodrag; Milošević, Zoran; Anđelković Apostolović, Marija

2017-12-02

The interest in developing risk models in medicine not only is appealing, but also associated with many obstacles in different aspects of predictive model development. Initially, the association of biomarkers or the association of more markers with the specific outcome was proven by statistical significance, but novel and demanding questions required the development of new and more complex statistical techniques. Progress of statistical analysis in biomedical research can be observed the best through the history of the Framingham study and development of the Framingham score. Evaluation of predictive models comes from a combination of the facts which are results of several metrics. Using logistic regression and Cox proportional hazards regression analysis, the calibration test, and the ROC curve analysis should be mandatory and eliminatory, and the central place should be taken by some new statistical techniques. In order to obtain complete information related to the new marker in the model, recently, there is a recommendation to use the reclassification tables by calculating the net reclassification index and the integrated discrimination improvement. Decision curve analysis is a novel method for evaluating the clinical usefulness of a predictive model. It may be noted that customizing and fine-tuning of the Framingham risk score initiated the development of statistical analysis. Clinically applicable predictive model should be a trade-off between all abovementioned statistical metrics, a trade-off between calibration and discrimination, accuracy and decision-making, costs and benefits, and quality and quantity of patient's life.

A new statistical analysis of rare earth element diffusion data in garnet

NASA Astrophysics Data System (ADS)

Chu, X.; Ague, J. J.

2015-12-01

The incorporation of rare earth elements (REE) in garnet, Sm and Lu in particular, links garnet chemical zoning to absolute age determinations. The application of REE-based geochronology depends critically on the diffusion behaviors of the parent and daughter isotopes. Previous experimental studies on REE diffusion in garnet, however, exhibit significant discrepancies that impact interpretations of garnet Sm/Nd and Lu/Hf ages.We present a new statistical framework to analyze diffusion data for REE using an Arrhenius relationship that accounts for oxygen fugacity, cation radius and garnet unit-cell dimensions [1]. Our approach is based on Bayesian statistics and is implemented by the Markov chain Monte Carlo method. A similar approach has been recently applied to model diffusion of divalent cations in garnet [2]. The analysis incorporates recent data [3] in addition to the data compilation in ref. [1]. We also include the inter-run bias that helps reconcile the discrepancies among data sets. This additional term estimates the reproducibility and other experimental variabilities not explicitly incorporated in the Arrhenius relationship [2] (e.g., compositional dependence [3] and water content).The fitted Arrhenius relationships are consistent with the models in ref. [3], as well as refs. [1]&[4] at high temperatures. Down-temperature extrapolation leads to >0.5 order of magnitude faster diffusion coefficients than in refs. [1]&[4] at <750 °C. The predicted diffusion coefficients are significantly slower than ref. [5]. The fast diffusion [5] was supported by a field test of the Pikwitonei Granulite—the garnet Sm/Nd age postdates the metamorphic peak (750 °C) by ~30 Myr [6], suggesting considerable resetting of the Sm/Nd system during cooling. However, the Pikwitonei Granulite is a recently recognized UHT terrane with peak temperature exceeding 900 °C [7]. The revised closure temperature (~730 °C) is consistent with our new diffusion model.[1] Carlson (2012) Am

Comparative analysis of ferroelectric domain statistics via nonlinear diffraction in random nonlinear materials.

PubMed

Wang, B; Switowski, K; Cojocaru, C; Roppo, V; Sheng, Y; Scalora, M; Kisielewski, J; Pawlak, D; Vilaseca, R; Akhouayri, H; Krolikowski, W; Trull, J

2018-01-22

We present an indirect, non-destructive optical method for domain statistic characterization in disordered nonlinear crystals having homogeneous refractive index and spatially random distribution of ferroelectric domains. This method relies on the analysis of the wave-dependent spatial distribution of the second harmonic, in the plane perpendicular to the optical axis in combination with numerical simulations. We apply this technique to the characterization of two different media, Calcium Barium Niobate and Strontium Barium Niobate, with drastically different statistical distributions of ferroelectric domains.

Statistical Tutorial | Center for Cancer Research

Cancer.gov

Recent advances in cancer biology have resulted in the need for increased statistical analysis of research data.  ST is designed as a follow up to Statistical Analysis of Research Data (SARD) held in April 2018.  The tutorial will apply the general principles of statistical analysis of research data including descriptive statistics, z- and t-tests of means and mean differences, simple and multiple linear regression, ANOVA tests, and Chi-Squared distribution.

Tsallis non-additive entropy and natural time analysis of seismicity

NASA Astrophysics Data System (ADS)

Sarlis, N. V.; Skordas, E. S.; Varotsos, P.

2017-12-01

Within the context of Tsallis non-additive entropy [1] statistical mechanics -in the frame of which kappa distributions arise [2,3]- a derivation of the Gutenberg-Richter (GR) law of seismicity has been proposed [4,5]. Such an analysis leads to a generalized GR law [6,7] which is applied here to the earthquakes in Japan and California. These seismic data are also studied in natural time [6] revealing that although some properties of seismicity may be recovered by the non-additive entropy approach, temporal correlations between successive earthquake magnitudes should be also taken into account [6,8]. The importance of such correlations is strengthened by the observation of periods of long range correlated earthquake magnitude time series [9] a few months before all earthquakes of magnitude 7.6 or larger in the entire Japanese area from 1 January 1984 to 11 March 2011 (the day of the magnitude 9.0 Tohoku-Oki earthquake) almost simultaneously with characteristic order parameter variations of seismicity [10]. These variations appear approximately when low frequency abnormal changes of the electric and magnetic field of the Earth (less than around 1Hz) are recorded [11] before strong earthquakes as the magnitude 9.0 Tohoku-Oki earthquake in Japan in 2011 [12]. 1. C Tsallis, J Stat Phys 52 (1988) 479 2. G Livadiotis, and D J McComas, J Geophys Res 114 (2009) A11105 3. G Livadiotis, Kappa Distributions. (Elsevier, Amsterdam) 2017. doi: 10.1016/B978-0-12-804638-8.01001-9 4. O Sotolongo-Costa, A Posadas, Phys Rev Lett 92 (2004) 048501 5. R Silva, G França, C Vilar, J Alcaniz, Phys Rev E 73 (2006) 026102 6. N Sarlis, E Skordas, P Varotsos, Phys Rev E 82 (2010) 021110 7. L Telesca, Bull Seismol Soc Am 102 (2012) 886-891 8. P Varotsos, N Sarlis, E Skordas, Natural Time Analysis: The new view of time. (Springer, Berlin) 2011. doi: 10.1007/978-3-642-16449-1 9. P Varotsos, N Sarlis, E Skordas, J Geophys Res Space Physics 119 (2014) 9192. 10. N Sarlis, E Skordas, P Varotsos, T

How Much Math Do Students Need to Succeed in Business and Economics Statistics? An Ordered Probit Analysis

ERIC Educational Resources Information Center

Green, Jeffrey J.; Stone, Courtenay C.; Zegeye, Abera; Charles, Thomas A.

2009-01-01

Because statistical analysis requires the ability to use mathematics, students typically are required to take one or more prerequisite math courses prior to enrolling in the business statistics course. Despite these math prerequisites, however, many students find it difficult to learn business statistics. In this study, we use an ordered probit…

Statistical analysis of effective singular values in matrix rank determination

NASA Technical Reports Server (NTRS)

Konstantinides, Konstantinos; Yao, Kung

1988-01-01

A major problem in using SVD (singular-value decomposition) as a tool in determining the effective rank of a perturbed matrix is that of distinguishing between significantly small and significantly large singular values to the end, conference regions are derived for the perturbed singular values of matrices with noisy observation data. The analysis is based on the theories of perturbations of singular values and statistical significance test. Threshold bounds for perturbation due to finite-precision and i.i.d. random models are evaluated. In random models, the threshold bounds depend on the dimension of the matrix, the noisy variance, and predefined statistical level of significance. Results applied to the problem of determining the effective order of a linear autoregressive system from the approximate rank of a sample autocorrelation matrix are considered. Various numerical examples illustrating the usefulness of these bounds and comparisons to other previously known approaches are given.

Can Percentiles Replace Raw Scores in the Statistical Analysis of Test Data?

ERIC Educational Resources Information Center

Zimmerman, Donald W.; Zumbo, Bruno D.

2005-01-01

Educational and psychological testing textbooks typically warn of the inappropriateness of performing arithmetic operations and statistical analysis on percentiles instead of raw scores. This seems inconsistent with the well-established finding that transforming scores to ranks and using nonparametric methods often improves the validity and power…

IUTAM Symposium on Statistical Energy Analysis, 8-11 July 1997, Programme

DTIC Science & Technology

1997-01-01

distribution is unlimited 12b. DISTRIBUTION CODE 13. ABSTRACT (Maximum200 words) This was the first international scientific gathering devoted...energy flow, continuum dynamics, vibrational energy, statistical energy analysis (SEA) 15. NUMBER OF PAGES 16. PRICE CODE INSECURITY... correlation v=V(ɘ ’• • determination of the correlation n^, =11^, (<?). When harmonic motion and time-average are considered, the following I

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

Modern Empirical Statistical Spectral Analysis.

DTIC Science & Technology

1980-05-01

716-723. Akaike, H. (1977). On entropy maximization principle, Applications of Statistics, P.R. Krishnaiah , ed., North-Holland, Amsterdam, 27-41...by P. Krishnaiah , North Holland: Amsterdam, 283-295. Parzen, E. (1979). Forecasting and whitening filter estimation, TIMS Studies in the Management

Visualization of time series statistical data by shape analysis (GDP ratio changes among Asia countries)

NASA Astrophysics Data System (ADS)

Shirota, Yukari; Hashimoto, Takako; Fitri Sari, Riri

2018-03-01

It has been very significant to visualize time series big data. In the paper we shall discuss a new analysis method called “statistical shape analysis” or “geometry driven statistics” on time series statistical data in economics. In the paper, we analyse the agriculture, value added and industry, value added (percentage of GDP) changes from 2000 to 2010 in Asia. We handle the data as a set of landmarks on a two-dimensional image to see the deformation using the principal components. The point of the analysis method is the principal components of the given formation which are eigenvectors of its bending energy matrix. The local deformation can be expressed as the set of non-Affine transformations. The transformations give us information about the local differences between in 2000 and in 2010. Because the non-Affine transformation can be decomposed into a set of partial warps, we present the partial warps visually. The statistical shape analysis is widely used in biology but, in economics, no application can be found. In the paper, we investigate its potential to analyse the economic data.

Statistical Analysis of Bending Rigidity Coefficient Determined Using Fluorescence-Based Flicker-Noise Spectroscopy.

PubMed

Doskocz, Joanna; Drabik, Dominik; Chodaczek, Grzegorz; Przybyło, Magdalena; Langner, Marek

2018-06-01

Bending rigidity coefficient describes propensity of a lipid bilayer to deform. In order to measure the parameter experimentally using flickering noise spectroscopy, the microscopic imaging is required, which necessitates the application of giant unilamellar vesicles (GUV) lipid bilayer model. The major difficulty associated with the application of the model is the statistical character of GUV population with respect to their size and the homogeneity of lipid bilayer composition, if a mixture of lipids is used. In the paper, the bending rigidity coefficient was measured using the fluorescence-enhanced flicker-noise spectroscopy. In the paper, the bending rigidity coefficient was determined for large populations of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine and 1,2-dioleoyl-sn-glycero-3-phosphocholine vesicles. The quantity of obtained experimental data allows to perform statistical analysis aiming at the identification of the distribution, which is the most appropriate for the calculation of the value of the membrane bending rigidity coefficient. It has been demonstrated that the bending rigidity coefficient is characterized by an asymmetrical distribution, which is well approximated with the gamma distribution. Since there are no biophysical reasons for that we propose to use the difference between normal and gamma fits as a measure of the homogeneity of vesicle population. In addition, the effect of a fluorescent label and types of instrumental setups on determined values has been tested. Obtained results show that the value of the bending rigidity coefficient does not depend on the type of a fluorescent label nor on the type of microscope used.

Characterizing microstructural features of biomedical samples by statistical analysis of Mueller matrix images

NASA Astrophysics Data System (ADS)

He, Honghui; Dong, Yang; Zhou, Jialing; Ma, Hui

2017-03-01

As one of the salient features of light, polarization contains abundant structural and optical information of media. Recently, as a comprehensive description of polarization property, the Mueller matrix polarimetry has been applied to various biomedical studies such as cancerous tissues detections. In previous works, it has been found that the structural information encoded in the 2D Mueller matrix images can be presented by other transformed parameters with more explicit relationship to certain microstructural features. In this paper, we present a statistical analyzing method to transform the 2D Mueller matrix images into frequency distribution histograms (FDHs) and their central moments to reveal the dominant structural features of samples quantitatively. The experimental results of porcine heart, intestine, stomach, and liver tissues demonstrate that the transformation parameters and central moments based on the statistical analysis of Mueller matrix elements have simple relationships to the dominant microstructural properties of biomedical samples, including the density and orientation of fibrous structures, the depolarization power, diattenuation and absorption abilities. It is shown in this paper that the statistical analysis of 2D images of Mueller matrix elements may provide quantitative or semi-quantitative criteria for biomedical diagnosis.

On intracluster Faraday rotation. II - Statistical analysis

NASA Technical Reports Server (NTRS)

Lawler, J. M.; Dennison, B.

1982-01-01

The comparison of a reliable sample of radio source Faraday rotation measurements seen through rich clusters of galaxies, with sources seen through the outer parts of clusters and therefore having little intracluster Faraday rotation, indicates that the distribution of rotation in the former population is broadened, but only at the 80% level of statistical confidence. Employing a physical model for the intracluster medium in which the square root of magnetic field strength/turbulent cell per gas core radius number ratio equals approximately 0.07 microgauss, a Monte Carlo simulation is able to reproduce the observed broadening. An upper-limit analysis figure of less than 0.20 microgauss for the field strength/turbulent cell ratio, combined with lower limits on field strength imposed by limitations on the Compton-scattered flux, shows that intracluster magnetic fields must be tangled on scales greater than about 20 kpc.

Data Analysis & Statistical Methods for Command File Errors

NASA Technical Reports Server (NTRS)

Meshkat, Leila; Waggoner, Bruce; Bryant, Larry

2014-01-01

This paper explains current work on modeling for managing the risk of command file errors. It is focused on analyzing actual data from a JPL spaceflight mission to build models for evaluating and predicting error rates as a function of several key variables. We constructed a rich dataset by considering the number of errors, the number of files radiated, including the number commands and blocks in each file, as well as subjective estimates of workload and operational novelty. We have assessed these data using different curve fitting and distribution fitting techniques, such as multiple regression analysis, and maximum likelihood estimation to see how much of the variability in the error rates can be explained with these. We have also used goodness of fit testing strategies and principal component analysis to further assess our data. Finally, we constructed a model of expected error rates based on the what these statistics bore out as critical drivers to the error rate. This model allows project management to evaluate the error rate against a theoretically expected rate as well as anticipate future error rates.

A functional U-statistic method for association analysis of sequencing data.

PubMed

Jadhav, Sneha; Tong, Xiaoran; Lu, Qing

2017-11-01

Although sequencing studies hold great promise for uncovering novel variants predisposing to human diseases, the high dimensionality of the sequencing data brings tremendous challenges to data analysis. Moreover, for many complex diseases (e.g., psychiatric disorders) multiple related phenotypes are collected. These phenotypes can be different measurements of an underlying disease, or measurements characterizing multiple related diseases for studying common genetic mechanism. Although jointly analyzing these phenotypes could potentially increase the power of identifying disease-associated genes, the different types of phenotypes pose challenges for association analysis. To address these challenges, we propose a nonparametric method, functional U-statistic method (FU), for multivariate analysis of sequencing data. It first constructs smooth functions from individuals' sequencing data, and then tests the association of these functions with multiple phenotypes by using a U-statistic. The method provides a general framework for analyzing various types of phenotypes (e.g., binary and continuous phenotypes) with unknown distributions. Fitting the genetic variants within a gene using a smoothing function also allows us to capture complexities of gene structure (e.g., linkage disequilibrium, LD), which could potentially increase the power of association analysis. Through simulations, we compared our method to the multivariate outcome score test (MOST), and found that our test attained better performance than MOST. In a real data application, we apply our method to the sequencing data from Minnesota Twin Study (MTS) and found potential associations of several nicotine receptor subunit (CHRN) genes, including CHRNB3, associated with nicotine dependence and/or alcohol dependence. © 2017 WILEY PERIODICALS, INC.

Audience Diversion Due to Cable Television: A Statistical Analysis of New Data.

ERIC Educational Resources Information Center

Park, Rolla Edward

A statistical analysis of new data suggests that television broadcasting will continue to prosper, despite increasing competition from cable television carrying distant signals. Data on cable and non-cable audiences in 121 counties with well defined signal choice support generalized least squares estimates of two models: total audience and…

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…

Data exploration, quality control and statistical analysis of ChIP-exo/nexus experiments

PubMed Central

Welch, Rene; Chung, Dongjun; Grass, Jeffrey; Landick, Robert

2017-01-01

Abstract ChIP-exo/nexus experiments rely on innovative modifications of the commonly used ChIP-seq protocol for high resolution mapping of transcription factor binding sites. Although many aspects of the ChIP-exo data analysis are similar to those of ChIP-seq, these high throughput experiments pose a number of unique quality control and analysis challenges. We develop a novel statistical quality control pipeline and accompanying R/Bioconductor package, ChIPexoQual, to enable exploration and analysis of ChIP-exo and related experiments. ChIPexoQual evaluates a number of key issues including strand imbalance, library complexity, and signal enrichment of data. Assessment of these features are facilitated through diagnostic plots and summary statistics computed over regions of the genome with varying levels of coverage. We evaluated our QC pipeline with both large collections of public ChIP-exo/nexus data and multiple, new ChIP-exo datasets from Escherichia coli. ChIPexoQual analysis of these datasets resulted in guidelines for using these QC metrics across a wide range of sequencing depths and provided further insights for modelling ChIP-exo data. PMID:28911122

Monitoring Method of Cow Anthrax Based on Gis and Spatial Statistical Analysis

NASA Astrophysics Data System (ADS)

Li, Lin; Yang, Yong; Wang, Hongbin; Dong, Jing; Zhao, Yujun; He, Jianbin; Fan, Honggang

Geographic information system (GIS) is a computer application system, which possesses the ability of manipulating spatial information and has been used in many fields related with the spatial information management. Many methods and models have been established for analyzing animal diseases distribution models and temporal-spatial transmission models. Great benefits have been gained from the application of GIS in animal disease epidemiology. GIS is now a very important tool in animal disease epidemiological research. Spatial analysis function of GIS can be widened and strengthened by using spatial statistical analysis, allowing for the deeper exploration, analysis, manipulation and interpretation of spatial pattern and spatial correlation of the animal disease. In this paper, we analyzed the cow anthrax spatial distribution characteristics in the target district A (due to the secret of epidemic data we call it district A) based on the established GIS of the cow anthrax in this district in combination of spatial statistical analysis and GIS. The Cow anthrax is biogeochemical disease, and its geographical distribution is related closely to the environmental factors of habitats and has some spatial characteristics, and therefore the correct analysis of the spatial distribution of anthrax cow for monitoring and the prevention and control of anthrax has a very important role. However, the application of classic statistical methods in some areas is very difficult because of the pastoral nomadic context. The high mobility of livestock and the lack of enough suitable sampling for the some of the difficulties in monitoring currently make it nearly impossible to apply rigorous random sampling methods. It is thus necessary to develop an alternative sampling method, which could overcome the lack of sampling and meet the requirements for randomness. The GIS computer application software ArcGIS9.1 was used to overcome the lack of data of sampling sites.Using ArcGIS 9.1 and GEODA

Statistical analysis of solid waste composition data: Arithmetic mean, standard deviation and correlation coefficients.

PubMed

Edjabou, Maklawe Essonanawe; Martín-Fernández, Josep Antoni; Scheutz, Charlotte; Astrup, Thomas Fruergaard

2017-11-01

Data for fractional solid waste composition provide relative magnitudes of individual waste fractions, the percentages of which always sum to 100, thereby connecting them intrinsically. Due to this sum constraint, waste composition data represent closed data, and their interpretation and analysis require statistical methods, other than classical statistics that are suitable only for non-constrained data such as absolute values. However, the closed characteristics of waste composition data are often ignored when analysed. The results of this study showed, for example, that unavoidable animal-derived food waste amounted to 2.21±3.12% with a confidence interval of (-4.03; 8.45), which highlights the problem of the biased negative proportions. A Pearson's correlation test, applied to waste fraction generation (kg mass), indicated a positive correlation between avoidable vegetable food waste and plastic packaging. However, correlation tests applied to waste fraction compositions (percentage values) showed a negative association in this regard, thus demonstrating that statistical analyses applied to compositional waste fraction data, without addressing the closed characteristics of these data, have the potential to generate spurious or misleading results. Therefore, ¨compositional data should be transformed adequately prior to any statistical analysis, such as computing mean, standard deviation and correlation coefficients. Copyright © 2017 Elsevier Ltd. All rights reserved.

Analysis Code - Data Analysis in 'Leveraging Multiple Statistical Methods for Inverse Prediction in Nuclear Forensics Applications' (LMSMIPNFA) v. 1.0

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

Lewis, John R

R code that performs the analysis of a data set presented in the paper ‘Leveraging Multiple Statistical Methods for Inverse Prediction in Nuclear Forensics Applications’ by Lewis, J., Zhang, A., Anderson-Cook, C. It provides functions for doing inverse predictions in this setting using several different statistical methods. The data set is a publicly available data set from a historical Plutonium production experiment.

Voxel-based statistical analysis of cerebral glucose metabolism in patients with permanent vegetative state after acquired brain injury.

PubMed

Kim, Yong Wook; Kim, Hyoung Seop; An, Young-Sil; Im, Sang Hee

2010-10-01

Permanent vegetative state is defined as the impaired level of consciousness longer than 12 months after traumatic causes and 3 months after non-traumatic causes of brain injury. Although many studies assessed the cerebral metabolism in patients with acute and persistent vegetative state after brain injury, few studies investigated the cerebral metabolism in patients with permanent vegetative state. In this study, we performed the voxel-based analysis of cerebral glucose metabolism and investigated the relationship between regional cerebral glucose metabolism and the severity of impaired consciousness in patients with permanent vegetative state after acquired brain injury. We compared the regional cerebral glucose metabolism as demonstrated by F-18 fluorodeoxyglucose positron emission tomography from 12 patients with permanent vegetative state after acquired brain injury with those from 12 control subjects. Additionally, covariance analysis was performed to identify regions where decreased changes in regional cerebral glucose metabolism significantly correlated with a decrease of level of consciousness measured by JFK-coma recovery scale. Statistical analysis was performed using statistical parametric mapping. Compared with controls, patients with permanent vegetative state demonstrated decreased cerebral glucose metabolism in the left precuneus, both posterior cingulate cortices, the left superior parietal lobule (P(corrected) < 0.001), and increased cerebral glucose metabolism in the both cerebellum and the right supramarginal cortices (P(corrected) < 0.001). In the covariance analysis, a decrease in the level of consciousness was significantly correlated with decreased cerebral glucose metabolism in the both posterior cingulate cortices (P(uncorrected) < 0.005). Our findings suggest that the posteromedial parietal cortex, which are part of neural network for consciousness, may be relevant structure for pathophysiological mechanism in patients with permanent

Statistical analysis on experimental calibration data for flowmeters in pressure pipes

NASA Astrophysics Data System (ADS)

Lazzarin, Alessandro; Orsi, Enrico; Sanfilippo, Umberto

2017-08-01

This paper shows a statistical analysis on experimental calibration data for flowmeters (i.e.: electromagnetic, ultrasonic, turbine flowmeters) in pressure pipes. The experimental calibration data set consists of the whole archive of the calibration tests carried out on 246 flowmeters from January 2001 to October 2015 at Settore Portate of Laboratorio di Idraulica “G. Fantoli” of Politecnico di Milano, that is accredited as LAT 104 for a flow range between 3 l/s and 80 l/s, with a certified Calibration and Measurement Capability (CMC) - formerly known as Best Measurement Capability (BMC) - equal to 0.2%. The data set is split into three subsets, respectively consisting in: 94 electromagnetic, 83 ultrasonic and 69 turbine flowmeters; each subset is analysed separately from the others, but then a final comparison is carried out. In particular, the main focus of the statistical analysis is the correction C, that is the difference between the flow rate Q measured by the calibration facility (through the accredited procedures and the certified reference specimen) minus the flow rate QM contemporarily recorded by the flowmeter under calibration, expressed as a percentage of the same QM .

The volume-mortality relation for radical cystectomy in England: retrospective analysis of hospital episode statistics

PubMed Central

Bottle, Alex; Darzi, Ara W; Athanasiou, Thanos; Vale, Justin A

2010-01-01

Objectives To investigate the relation between volume and mortality after adjustment for case mix for radical cystectomy in the English healthcare setting using improved statistical methodology, taking into account the institutional and surgeon volume effects and institutional structural and process of care factors. Design Retrospective analysis of hospital episode statistics using multilevel modelling. Setting English hospitals carrying out radical cystectomy in the seven financial years 2000/1 to 2006/7. Participants Patients with a primary diagnosis of cancer undergoing an inpatient elective cystectomy. Main outcome measure Mortality within 30 days of cystectomy. Results Compared with low volume institutions, medium volume ones had a significantly higher odds of in-hospital and total mortality: odds ratio 1.72 (95% confidence interval 1.00 to 2.98, P=0.05) and 1.82 (1.08 to 3.06, P=0.02). This was only seen in the final model, which included adjustment for structural and processes of care factors. The surgeon volume-mortality relation showed weak evidence of reduced odds of in-hospital mortality (by 35%) for the high volume surgeons, although this did not reach statistical significance at the 5% level. Conclusions The relation between case volume and mortality after radical cystectomy for bladder cancer became evident only after adjustment for structural and process of care factors, including staffing levels of nurses and junior doctors, in addition to case mix. At least for this relatively uncommon procedure, adjusting for these confounders when examining the volume-outcome relation is critical before considering centralisation of care to a few specialist institutions. Outcomes other than mortality, such as functional morbidity and disease recurrence may ultimately influence towards centralising care. PMID:20305302

Treated cabin acoustic prediction using statistical energy analysis

NASA Technical Reports Server (NTRS)

Yoerkie, Charles A.; Ingraham, Steven T.; Moore, James A.

1987-01-01

The application of statistical energy analysis (SEA) to the modeling and design of helicopter cabin interior noise control treatment is demonstrated. The information presented here is obtained from work sponsored at NASA Langley for the development of analytic modeling techniques and the basic understanding of cabin noise. Utility and executive interior models are developed directly from existing S-76 aircraft designs. The relative importance of panel transmission loss (TL), acoustic leakage, and absorption to the control of cabin noise is shown using the SEA modeling parameters. It is shown that the major cabin noise improvement below 1000 Hz comes from increased panel TL, while above 1000 Hz it comes from reduced acoustic leakage and increased absorption in the cabin and overhead cavities.

Spatial statistical analysis of tree deaths using airborne digital imagery

NASA Astrophysics Data System (ADS)

Chang, Ya-Mei; Baddeley, Adrian; Wallace, Jeremy; Canci, Michael

2013-04-01

High resolution digital airborne imagery offers unprecedented opportunities for observation and monitoring of vegetation, providing the potential to identify, locate and track individual vegetation objects over time. Analytical tools are required to quantify relevant information. In this paper, locations of trees over a large area of native woodland vegetation were identified using morphological image analysis techniques. Methods of spatial point process statistics were then applied to estimate the spatially-varying tree death risk, and to show that it is significantly non-uniform. [Tree deaths over the area were detected in our previous work (Wallace et al., 2008).] The study area is a major source of ground water for the city of Perth, and the work was motivated by the need to understand and quantify vegetation changes in the context of water extraction and drying climate. The influence of hydrological variables on tree death risk was investigated using spatial statistics (graphical exploratory methods, spatial point pattern modelling and diagnostics).

Regional Exploratory Analysis Between Atomospheric Aerosols and Precipitable Water in the Lower Troposhere via Inferential Statistics

NASA Astrophysics Data System (ADS)

Martinez, B. S.; Ye, H.; Levy, R. C.; Fetzer, E. J.; Remer, L.

2017-12-01

Atmospheric aerosols expose high levels of uncertainty in regard to Earth's changing atmospheric energy budget. Continued exploration and analysis is necessary to obtain more complete understanding in which, and to what degree, aerosols contribute within climate feedbacks and global climate change. With the advent of global satellite retrievals, along with specific aerosol optical depth (AOD) Dark Target and Deep Blue algorithms, aerosols can now be better measured and analyzed. Aerosol effect on climate depends primarily on altitude, the reflectance albedo of the underlying surface, along with the presence of clouds and the dynamics thereof. As currently known, the majority of aerosol distribution and mixing occur in the lower troposphere from the surface upwards to around 2km. Additionally, being a primary greenhouse gas contributor, water vapor is significant to climate feedbacks and Earth's radiation budget. Feedbacks are generally reported from the top of atmosphere (TOA). Therefore, little is known of the relationship between water vapor and aerosols; specifically, in regional areas of the globe known for aerosol loading such as anthropogenic biomass burning in South America and naturally occurring dust blowing off the deserts in the African and Arabian peninsulas. Statistical regression and timeseries analysis are used in determining significant probabilities suggesting trends of both regional precipitable water (PW) and AOD increase and decrease over a 13-year time period from 2003-2015. Regions with statistically significant positive or negative trends of AOD and PW are analyzed in determining correlations, or lack thereof. This initial examination helps to deduce and better understand how aerosols contribute to the radiation budget and assessing climate change.

Optimal Multicomponent Analysis Using the Generalized Standard Addition Method.

ERIC Educational Resources Information Center

Raymond, Margaret; And Others

1983-01-01

Describes an experiment on the simultaneous determination of chromium and magnesium by spectophotometry modified to include the Generalized Standard Addition Method computer program, a multivariate calibration method that provides optimal multicomponent analysis in the presence of interference and matrix effects. Provides instructions for…

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

Additive interaction between heterogeneous environmental ...

EPA Pesticide Factsheets

BACKGROUND Environmental exposures often occur in tandem; however, epidemiological research often focuses on singular exposures. Statistical interactions among broad, well-characterized environmental domains have not yet been evaluated in association with health. We address this gap by conducting a county-level cross-sectional analysis of interactions between Environmental Quality Index (EQI) domain indices on preterm birth in the Unites States from 2000-2005.METHODS: The EQI, a county-level index constructed for the 2000-2005 time period, was constructed from five domain-specific indices (air, water, land, built and sociodemographic) using principal component analyses. County-level preterm birth rates (n=3141) were estimated using live births from the National Center for Health Statistics. Linear regression was used to estimate prevalence differences (PD) and 95% confidence intervals (CI) comparing worse environmental quality to the better quality for each model for a) each individual domain main effect b) the interaction contrast and c) the two main effects plus interaction effect (i.e. the “net effect”) to show departure from additive interaction for the all U.S counties. Analyses were also performed for subgroupings by four urban/rural strata. RESULTS: We found the suggestion of antagonistic interactions but no synergism, along with several purely additive (i.e., no interaction) associations. In the non-stratified model, we observed antagonistic interac

Assessment of trace elements levels in patients with Type 2 diabetes using multivariate statistical analysis.

PubMed

Badran, M; Morsy, R; Soliman, H; Elnimr, T

2016-01-01

The trace elements metabolism has been reported to possess specific roles in the pathogenesis and progress of diabetes mellitus. Due to the continuous increase in the population of patients with Type 2 diabetes (T2D), this study aims to assess the levels and inter-relationships of fast blood glucose (FBG) and serum trace elements in Type 2 diabetic patients. This study was conducted on 40 Egyptian Type 2 diabetic patients and 36 healthy volunteers (Hospital of Tanta University, Tanta, Egypt). The blood serum was digested and then used to determine the levels of 24 trace elements using an inductive coupled plasma mass spectroscopy (ICP-MS). Multivariate statistical analysis depended on correlation coefficient, cluster analysis (CA) and principal component analysis (PCA), were used to analysis the data. The results exhibited significant changes in FBG and eight of trace elements, Zn, Cu, Se, Fe, Mn, Cr, Mg, and As, levels in the blood serum of Type 2 diabetic patients relative to those of healthy controls. The statistical analyses using multivariate statistical techniques were obvious in the reduction of the experimental variables, and grouping the trace elements in patients into three clusters. The application of PCA revealed a distinct difference in associations of trace elements and their clustering patterns in control and patients group in particular for Mg, Fe, Cu, and Zn that appeared to be the most crucial factors which related with Type 2 diabetes. Therefore, on the basis of this study, the contributors of trace elements content in Type 2 diabetic patients can be determine and specify with correlation relationship and multivariate statistical analysis, which confirm that the alteration of some essential trace metals may play a role in the development of diabetes mellitus. Copyright © 2015 Elsevier GmbH. All rights reserved.

Study design and statistical analysis of data in human population studies with the micronucleus assay.

PubMed

Ceppi, Marcello; Gallo, Fabio; Bonassi, Stefano

2011-01-01

The most common study design performed in population studies based on the micronucleus (MN) assay, is the cross-sectional study, which is largely performed to evaluate the DNA damaging effects of exposure to genotoxic agents in the workplace, in the environment, as well as from diet or lifestyle factors. Sample size is still a critical issue in the design of MN studies since most recent studies considering gene-environment interaction, often require a sample size of several hundred subjects, which is in many cases difficult to achieve. The control of confounding is another major threat to the validity of causal inference. The most popular confounders considered in population studies using MN are age, gender and smoking habit. Extensive attention is given to the assessment of effect modification, given the increasing inclusion of biomarkers of genetic susceptibility in the study design. Selected issues concerning the statistical treatment of data have been addressed in this mini-review, starting from data description, which is a critical step of statistical analysis, since it allows to detect possible errors in the dataset to be analysed and to check the validity of assumptions required for more complex analyses. Basic issues dealing with statistical analysis of biomarkers are extensively evaluated, including methods to explore the dose-response relationship among two continuous variables and inferential analysis. A critical approach to the use of parametric and non-parametric methods is presented, before addressing the issue of most suitable multivariate models to fit MN data. In the last decade, the quality of statistical analysis of MN data has certainly evolved, although even nowadays only a small number of studies apply the Poisson model, which is the most suitable method for the analysis of MN data.

Log-Normality and Multifractal Analysis of Flame Surface Statistics

NASA Astrophysics Data System (ADS)

Saha, Abhishek; Chaudhuri, Swetaprovo; Law, Chung K.

2013-11-01

The turbulent flame surface is typically highly wrinkled and folded at a multitude of scales controlled by various flame properties. It is useful if the information contained in this complex geometry can be projected onto a simpler regular geometry for the use of spectral, wavelet or multifractal analyses. Here we investigate local flame surface statistics of turbulent flame expanding under constant pressure. First the statistics of local length ratio is experimentally obtained from high-speed Mie scattering images. For spherically expanding flame, length ratio on the measurement plane, at predefined equiangular sectors is defined as the ratio of the actual flame length to the length of a circular-arc of radius equal to the average radius of the flame. Assuming isotropic distribution of such flame segments we convolute suitable forms of the length-ratio probability distribution functions (pdfs) to arrive at corresponding area-ratio pdfs. Both the pdfs are found to be near log-normally distributed and shows self-similar behavior with increasing radius. Near log-normality and rather intermittent behavior of the flame-length ratio suggests similarity with dissipation rate quantities which stimulates multifractal analysis. Currently at Indian Institute of Science, India.

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

On the blind use of statistical tools in the analysis of globular cluster stars

NASA Astrophysics Data System (ADS)

D'Antona, Francesca; Caloi, Vittoria; Tailo, Marco

2018-04-01

As with most data analysis methods, the Bayesian method must be handled with care. We show that its application to determine stellar evolution parameters within globular clusters can lead to paradoxical results if used without the necessary precautions. This is a cautionary tale on the use of statistical tools for big data analysis.

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.

Statistical principle and methodology in the NISAN system.

PubMed Central

Asano, C

1979-01-01

The NISAN system is a new interactive statistical analysis program package constructed by an organization of Japanese statisticans. The package is widely available for both statistical situations, confirmatory analysis and exploratory analysis, and is planned to obtain statistical wisdom and to choose optimal process of statistical analysis for senior statisticians. PMID:540594

Statistical Literacy as a Function of Online versus Hybrid Course Delivery Format for an Introductory Graduate Statistics Course

ERIC Educational Resources Information Center

Hahs-Vaughn, Debbie L.; Acquaye, Hannah; Griffith, Matthew D.; Jo, Hang; Matthews, Ken; Acharya, Parul

2017-01-01

Statistical literacy refers to understanding fundamental statistical concepts. Assessment of statistical literacy can take the forms of tasks that require students to identify, translate, compute, read, and interpret data. In addition, statistical instruction can take many forms encompassing course delivery format such as face-to-face, hybrid,…

Statistical analysis of tiny SXR flares observed by SphinX

NASA Astrophysics Data System (ADS)

Gryciuk, Magdalena; Siarkowski, Marek; Sylwester, Janusz; Kepa, Anna; Gburek, Szymon; Mrozek, Tomasz; Podgórski, Piotr

2015-08-01

The Solar Photometer in X-rays (SphinX) was designed to observe soft X-ray solar emission in the energy range between ~1 keV and 15 keV with the resolution better than 0.5 keV. The instrument operated from February until November 2009 aboard CORONAS-Photon satellite, during the phase of exceptionally low minimum of solar activity. Here we use SphinX data for analysis of micro-flares and brightenings. Despite a very low activity more than a thousand small X-ray events have been recognized by semi-automatic inspection of SphinX light curves. A catalogue of temporal and physical characteristics of these events is shown and discussed and results of the statistical analysis of the catalogue data are presented.

Architecture of a spatial data service system for statistical analysis and visualization of regional climate changes

NASA Astrophysics Data System (ADS)

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

2017-11-01

The use of large geospatial datasets in climate change studies requires the development of a set of Spatial Data Infrastructure (SDI) elements, including geoprocessing and cartographical visualization web services. This paper presents the architecture of a geospatial OGC web service system as an integral part of a virtual research environment (VRE) general architecture for statistical processing and visualization of meteorological and climatic data. The architecture is a set of interconnected standalone SDI nodes with corresponding data storage systems. Each node runs a specialized software, such as a geoportal, cartographical web services (WMS/WFS), a metadata catalog, and a MySQL database of technical metadata describing geospatial datasets available for the node. It also contains geospatial data processing services (WPS) based on a modular computing backend realizing statistical processing functionality and, thus, providing analysis of large datasets with the results of visualization and export into files of standard formats (XML, binary, etc.). Some cartographical web services have been developed in a system’s prototype to provide capabilities to work with raster and vector geospatial data based on OGC web services. The distributed architecture presented allows easy addition of new nodes, computing and data storage systems, and provides a solid computational infrastructure for regional climate change studies based on modern Web and GIS technologies.

Statistical Frequency-Dependent Analysis of Trial-to-Trial Variability in Single Time Series by Recurrence Plots.

PubMed

Tošić, Tamara; Sellers, Kristin K; Fröhlich, Flavio; Fedotenkova, Mariia; Beim Graben, Peter; Hutt, Axel

2015-01-01

For decades, research in neuroscience has supported the hypothesis that brain dynamics exhibits recurrent metastable states connected by transients, which together encode fundamental neural information processing. To understand the system's dynamics it is important to detect such recurrence domains, but it is challenging to extract them from experimental neuroscience datasets due to the large trial-to-trial variability. The proposed methodology extracts recurrent metastable states in univariate time series by transforming datasets into their time-frequency representations and computing recurrence plots based on instantaneous spectral power values in various frequency bands. Additionally, a new statistical inference analysis compares different trial recurrence plots with corresponding surrogates to obtain statistically significant recurrent structures. This combination of methods is validated by applying it to two artificial datasets. In a final study of visually-evoked Local Field Potentials in partially anesthetized ferrets, the methodology is able to reveal recurrence structures of neural responses with trial-to-trial variability. Focusing on different frequency bands, the δ-band activity is much less recurrent than α-band activity. Moreover, α-activity is susceptible to pre-stimuli, while δ-activity is much less sensitive to pre-stimuli. This difference in recurrence structures in different frequency bands indicates diverse underlying information processing steps in the brain.

Statistical Frequency-Dependent Analysis of Trial-to-Trial Variability in Single Time Series by Recurrence Plots

PubMed Central

Tošić, Tamara; Sellers, Kristin K.; Fröhlich, Flavio; Fedotenkova, Mariia; beim Graben, Peter; Hutt, Axel

2016-01-01

For decades, research in neuroscience has supported the hypothesis that brain dynamics exhibits recurrent metastable states connected by transients, which together encode fundamental neural information processing. To understand the system's dynamics it is important to detect such recurrence domains, but it is challenging to extract them from experimental neuroscience datasets due to the large trial-to-trial variability. The proposed methodology extracts recurrent metastable states in univariate time series by transforming datasets into their time-frequency representations and computing recurrence plots based on instantaneous spectral power values in various frequency bands. Additionally, a new statistical inference analysis compares different trial recurrence plots with corresponding surrogates to obtain statistically significant recurrent structures. This combination of methods is validated by applying it to two artificial datasets. In a final study of visually-evoked Local Field Potentials in partially anesthetized ferrets, the methodology is able to reveal recurrence structures of neural responses with trial-to-trial variability. Focusing on different frequency bands, the δ-band activity is much less recurrent than α-band activity. Moreover, α-activity is susceptible to pre-stimuli, while δ-activity is much less sensitive to pre-stimuli. This difference in recurrence structures in different frequency bands indicates diverse underlying information processing steps in the brain. PMID:26834580

How to interpret the results of medical time series data analysis: Classical statistical approaches versus dynamic Bayesian network modeling.

PubMed

Onisko, Agnieszka; Druzdzel, Marek J; Austin, R Marshall

2016-01-01

Classical statistics is a well-established approach in the analysis of medical data. While the medical community seems to be familiar with the concept of a statistical analysis and its interpretation, the Bayesian approach, argued by many of its proponents to be superior to the classical frequentist approach, is still not well-recognized in the analysis of medical data. The goal of this study is to encourage data analysts to use the Bayesian approach, such as modeling with graphical probabilistic networks, as an insightful alternative to classical statistical analysis of medical data. This paper offers a comparison of two approaches to analysis of medical time series data: (1) classical statistical approach, such as the Kaplan-Meier estimator and the Cox proportional hazards regression model, and (2) dynamic Bayesian network modeling. Our comparison is based on time series cervical cancer screening data collected at Magee-Womens Hospital, University of Pittsburgh Medical Center over 10 years. The main outcomes of our comparison are cervical cancer risk assessments produced by the three approaches. However, our analysis discusses also several aspects of the comparison, such as modeling assumptions, model building, dealing with incomplete data, individualized risk assessment, results interpretation, and model validation. Our study shows that the Bayesian approach is (1) much more flexible in terms of modeling effort, and (2) it offers an individualized risk assessment, which is more cumbersome for classical statistical approaches.

Additional weekend therapy may reduce length of rehabilitation stay after stroke: a meta-analysis of individual patient data.

PubMed

English, Coralie; Shields, Nora; Brusco, Natasha K; Taylor, Nicholas F; Watts, Jennifer J; Peiris, Casey; Bernhardt, Julie; Crotty, Maria; Esterman, Adrian; Segal, Leonie; Hillier, Susan

2016-07-01

Among people receiving inpatient rehabilitation after stroke, does additional weekend physiotherapy and/or occupational therapy reduce the length of rehabilitation hospital stay compared to those who receive a weekday-only service, and does this change after controlling for individual factors? Does additional weekend therapy improve the ability to walk and perform activities of daily living, measured at discharge? Does additional weekend therapy improve health-related quality of life, measured 6 months after discharge from rehabilitation? Which individual, clinical and hospital characteristics are associated with shorter length of rehabilitation hospital stay? This study pooled individual data from two randomised, controlled trials (n=350) using an individual patient data meta-analysis and multivariate regression. People with stroke admitted to inpatient rehabilitation facilities. Additional weekend therapy (physiotherapy and/or occupational therapy) compared to usual care (5 days/week therapy). Length of rehabilitation hospital stay, independence in activities of daily living measured with the Functional Independence Measure, walking speed and health-related quality of life. Participants who received weekend therapy had a shorter length of rehabilitation hospital stay. In the un-adjusted analysis, this was not statistically significant (MD -5.7 days, 95% CI -13.0 to 1.5). Controlling for hospital site, age, walking speed and Functional Independence Measure score on admission, receiving weekend therapy was significantly associated with a shorter length of rehabilitation hospital stay (β=7.5, 95% CI 1.7 to 13.4, p=0.001). There were no significant between-group differences in Functional Independence Measure scores (MD 1.9 points, 95% CI -2.8 to 6.6), walking speed (MD 0.06 m/second, 95% CI -0.15 to 0.04) or health-related quality of life (SMD -0.04, 95% CI -0.26 to 0.19) at discharge. Modest evidence indicates that additional weekend therapy might reduce

Failure Analysis by Statistical Techniques (FAST). Volume 1. User’s Manual

DTIC Science & Technology

1974-10-31

REPORT NUMBER DNA 3336F-1 2. OOVT ACCESSION NO 4. TITLE Cand Sublllle) • FAILURE ANALYSIS BY STATISTICAL TECHNIQUES (FAST) Volume I, User’s...SS2), and t’ a facility ( SS7 ). The other three diagrams break down the three critical subsystems. T le median probability of survival of the

Validation analysis of probabilistic models of dietary exposure to food additives.