Introduction To Non Parametric Methods Through R Software
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Introduction To Non Parametric Methods Through R Software
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Author : Editor IJSMI
language : en
Publisher: International Journal of Statistics and Medical Informatics
Release Date : 2022-09-30
Introduction To Non Parametric Methods Through R Software written by Editor IJSMI and has been published by International Journal of Statistics and Medical Informatics this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-30 with Education categories.
Statistical Methods are widely used in Medical, Biological, Clinical, Business and Engineering field. The data which form the basis for the statistical methods helps us to take scientific and informed decisions. Statistical methods deal with the collection, compilation, analysis and making inference from the data. The book mainly focuses on non-parametric aspects of Statistical methods. Non parametric methods or tests are used when the assumption about the distribution of the variables in the data set is not known or does not follow normal distribution assumption. Non parametric methods are useful to deal with ordered categorical data. When the sample size is large, statistical tests are robust due to the central limit theorem property. When sample size is small one need to use non-parametric tests. Compared to parametric tests, non-parametric tests are less powerful i.e. if we fail to reject the null hypothesis even if it is false. When the data set involves ranks or measured in ordinal scale then non-parametric tests are useful and easy to construct than parametric tests. The book uses open source R statistical software to carry out different non-parametric statistical methods with sample datasets.
Introduction To Nonparametric Statistics For The Biological Sciences Using R
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Author : Thomas W. MacFarland
language : en
Publisher: Springer
Release Date : 2016-07-06
Introduction To Nonparametric Statistics For The Biological Sciences Using R written by Thomas W. MacFarland and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-07-06 with Medical categories.
This book contains a rich set of tools for nonparametric analyses, and the purpose of this text is to provide guidance to students and professional researchers on how R is used for nonparametric data analysis in the biological sciences: To introduce when nonparametric approaches to data analysis are appropriate To introduce the leading nonparametric tests commonly used in biostatistics and how R is used to generate appropriate statistics for each test To introduce common figures typically associated with nonparametric data analysis and how R is used to generate appropriate figures in support of each data set The book focuses on how R is used to distinguish between data that could be classified as nonparametric as opposed to data that could be classified as parametric, with both approaches to data classification covered extensively. Following an introductory lesson on nonparametric statistics for the biological sciences, the book is organized into eight self-contained lessons on various analyses and tests using R to broadly compare differences between data sets and statistical approach.
Nonparametric Statistics
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Author : Gregory W. Corder
language : en
Publisher: John Wiley & Sons
Release Date : 2014-05-12
Nonparametric Statistics written by Gregory W. Corder and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-12 with Mathematics categories.
“...a very useful resource for courses in nonparametric statistics in which the emphasis is on applications rather than on theory. It also deserves a place in libraries of all institutions where introductory statistics courses are taught." –CHOICE This Second Edition presents a practical and understandable approach that enhances and expands the statistical toolset for readers. This book includes: New coverage of the sign test and the Kolmogorov-Smirnov two-sample test in an effort to offer a logical and natural progression to statistical power SPSS® (Version 21) software and updated screen captures to demonstrate how to perform and recognize the steps in the various procedures Data sets and odd-numbered solutions provided in an appendix, and tables of critical values Supplementary material to aid in reader comprehension, which includes: narrated videos and screen animations with step-by-step instructions on how to follow the tests using SPSS; online decision trees to help users determine the needed type of statistical test; and additional solutions not found within the book.
Bayesian Nonparametric Data Analysis
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Author : Peter Müller
language : en
Publisher: Springer
Release Date : 2015-06-17
Bayesian Nonparametric Data Analysis written by Peter Müller and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-06-17 with Mathematics categories.
This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones. The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in online software pages.
Practical Nonparametric Statistics
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Author : W. J. Conover
language : en
Publisher:
Release Date : 1980-09-17
Practical Nonparametric Statistics written by W. J. Conover and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1980-09-17 with Mathematics categories.
Probability theory; Statistical inference; Some tests based on the binomial distribution; Contingency tables; Some methods based on ranks; Statistics of the koolmogorov-smirnov type.
Nonparametric Statistics For Non Statisticians
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Author : Gregory W. Corder
language : en
Publisher: John Wiley & Sons
Release Date : 2011-09-20
Nonparametric Statistics For Non Statisticians written by Gregory W. Corder and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-09-20 with Mathematics categories.
A practical and understandable approach to nonparametric statistics for researchers across diverse areas of study As the importance of nonparametric methods in modern statistics continues to grow, these techniques are being increasingly applied to experimental designs across various fields of study. However, researchers are not always properly equipped with the knowledge to correctly apply these methods. Nonparametric Statistics for Non-Statisticians: A Step-by-Step Approach fills a void in the current literature by addressing nonparametric statistics in a manner that is easily accessible for readers with a background in the social, behavioral, biological, and physical sciences. Each chapter follows the same comprehensive format, beginning with a general introduction to the particular topic and a list of main learning objectives. A nonparametric procedure is then presented and accompanied by context-based examples that are outlined in a step-by-step fashion. Next, SPSS® screen captures are used to demonstrate how to perform and recognize the steps in the various procedures. Finally, the authors identify and briefly describe actual examples of corresponding nonparametric tests from diverse fields. Using this organized structure, the book outlines essential skills for the application of nonparametric statistical methods, including how to: Test data for normality and randomness Use the Wilcoxon signed rank test to compare two related samples Apply the Mann-Whitney U test to compare two unrelated samples Compare more than two related samples using the Friedman test Employ the Kruskal-Wallis H test to compare more than two unrelated samples Compare variables of ordinal or dichotomous scales Test for nominal scale data A detailed appendix provides guidance on inputting and analyzing the presented data using SPSS®, and supplemental tables of critical values are provided. In addition, the book's FTP site houses supplemental data sets and solutions for further practice. Extensively classroom tested, Nonparametric Statistics for Non-Statisticians is an ideal book for courses on nonparametric statistics at the upper-undergraduate and graduate levels. It is also an excellent reference for professionals and researchers in the social, behavioral, and health sciences who seek a review of nonparametric methods and relevant applications.
An Introduction To Nonparametric Statistics
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Author : John E. Kolassa
language : en
Publisher: CRC Press
Release Date : 2020-09-28
An Introduction To Nonparametric Statistics written by John E. Kolassa and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-28 with Mathematics categories.
An Introduction to Nonparametric Statistics presents techniques for statistical analysis in the absence of strong assumptions about the distributions generating the data. Rank-based and resampling techniques are heavily represented, but robust techniques are considered as well. These techniques include one-sample testing and estimation, multi-sample testing and estimation, and regression. Attention is paid to the intellectual development of the field, with a thorough review of bibliographical references. Computational tools, in R and SAS, are developed and illustrated via examples. Exercises designed to reinforce examples are included. Features Rank-based techniques including sign, Kruskal-Wallis, Friedman, Mann-Whitney and Wilcoxon tests are presented Tests are inverted to produce estimates and confidence intervals Multivariate tests are explored Techniques reflecting the dependence of a response variable on explanatory variables are presented Density estimation is explored The bootstrap and jackknife are discussed This text is intended for a graduate student in applied statistics. The course is best taken after an introductory course in statistical methodology, elementary probability, and regression. Mathematical prerequisites include calculus through multivariate differentiation and integration, and, ideally, a course in matrix algebra.
Nonparametric Statistics With Applications To Science And Engineering With R
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Author : Paul Kvam
language : en
Publisher: John Wiley & Sons
Release Date : 2022-10-06
Nonparametric Statistics With Applications To Science And Engineering With R written by Paul Kvam and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-06 with Mathematics categories.
NONPARAMETRIC STATISTICS WITH APPLICATIONS TO SCIENCE AND ENGINEERING WITH R Introduction to the methods and techniques of traditional and modern nonparametric statistics, incorporating R code Nonparametric Statistics with Applications to Science and Engineering with R presents modern nonparametric statistics from a practical point of view, with the newly revised edition including custom R functions implementing nonparametric methods to explain how to compute them and make them more comprehensible. Relevant built-in functions and packages on CRAN are also provided with a sample code. R codes in the new edition not only enable readers to perform nonparametric analysis easily, but also to visualize and explore data using R’s powerful graphic systems, such as ggplot2 package and R base graphic system. The new edition includes useful tables at the end of each chapter that help the reader find data sets, files, functions, and packages that are used and relevant to the respective chapter. New examples and exercises that enable readers to gain a deeper insight into nonparametric statistics and increase their comprehension are also included. Some of the sample topics discussed in Nonparametric Statistics with Applications to Science and Engineering with R include: Basics of probability, statistics, Bayesian statistics, order statistics, Kolmogorov–Smirnov test statistics, rank tests, and designed experiments Categorical data, estimating distribution functions, density estimation, least squares regression, curve fitting techniques, wavelets, and bootstrap sampling EM algorithms, statistical learning, nonparametric Bayes, WinBUGS, properties of ranks, and Spearman coefficient of rank correlation Chi-square and goodness-of-fit, contingency tables, Fisher exact test, MC Nemar test, Cochran’s test, Mantel–Haenszel test, and Empirical Likelihood Nonparametric Statistics with Applications to Science and Engineering with R is a highly valuable resource for graduate students in engineering and the physical and mathematical sciences, as well as researchers who need a more comprehensive, but succinct understanding of modern nonparametric statistical methods.
Introduction To Nonparametric Regression
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Author : K. Takezawa
language : en
Publisher: John Wiley & Sons
Release Date : 2005-12-02
Introduction To Nonparametric Regression written by K. Takezawa and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-12-02 with Mathematics categories.
An easy-to-grasp introduction to nonparametric regression This book's straightforward, step-by-step approach provides an excellent introduction to the field for novices of nonparametric regression. Introduction to Nonparametric Regression clearly explains the basic concepts underlying nonparametric regression and features: * Thorough explanations of various techniques, which avoid complex mathematics and excessive abstract theory to help readers intuitively grasp the value of nonparametric regression methods * Statistical techniques accompanied by clear numerical examples that further assist readers in developing and implementing their own solutions * Mathematical equations that are accompanied by a clear explanation of how the equation was derived The first chapter leads with a compelling argument for studying nonparametric regression and sets the stage for more advanced discussions. In addition to covering standard topics, such as kernel and spline methods, the book provides in-depth coverage of the smoothing of histograms, a topic generally not covered in comparable texts. With a learning-by-doing approach, each topical chapter includes thorough S-Plus? examples that allow readers to duplicate the same results described in the chapter. A separate appendix is devoted to the conversion of S-Plus objects to R objects. In addition, each chapter ends with a set of problems that test readers' grasp of key concepts and techniques and also prepares them for more advanced topics. This book is recommended as a textbook for undergraduate and graduate courses in nonparametric regression. Only a basic knowledge of linear algebra and statistics is required. In addition, this is an excellent resource for researchers and engineers in such fields as pattern recognition, speech understanding, and data mining. Practitioners who rely on nonparametric regression for analyzing data in the physical, biological, and social sciences, as well as in finance and economics, will find this an unparalleled resource.
Nonlinear Time Series
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Author : Jianqing Fan
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-09-11
Nonlinear Time Series written by Jianqing Fan and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-09-11 with Mathematics categories.
This is the first book that integrates useful parametric and nonparametric techniques with time series modeling and prediction, the two important goals of time series analysis. Such a book will benefit researchers and practitioners in various fields such as econometricians, meteorologists, biologists, among others who wish to learn useful time series methods within a short period of time. The book also intends to serve as a reference or text book for graduate students in statistics and econometrics.