## Using Multivariate Statistics

Author: Barbara G. Tabachnick,Linda S. Fidell

Publisher: Allyn & Bacon

ISBN: N.A

Category: Mathematics

Page: 966

View: 4421

Practical textbook explaining multivariate statistical techniques.
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## Multivariate Statistical Methods

A Primer, Fourth Edition

Author: Bryan F.J. Manly,Jorge A. Navarro Alberto

Publisher: CRC Press

ISBN: 1498728995

Category: Mathematics

Page: 269

View: 3901

Multivariate Statistical Methods: A Primer provides an introductory overview of multivariate methods without getting too deep into the mathematical details. This fourth edition is a revised and updated version of this bestselling introductory textbook. It retains the clear and concise style of the previous editions of the book and focuses on examples from biological and environmental sciences. The major update with this edition is that R code has been included for each of the analyses described, although in practice any standard statistical package can be used. The original idea with this book still applies. This was to make it as short as possible and enable readers to begin using multivariate methods in an intelligent manner. With updated information on multivariate analyses, new references, and R code included, this book continues to provide a timely introduction to useful tools for multivariate statistical analysis.
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## Using Multivariate Statistics: Pearson New International Edition

Author: Barbara G. Tabachnick,Linda S. Fidell

Publisher: Pearson Higher Ed

ISBN: 1292034548

Category: Social Science

Page: 1072

View: 4963

A Practical Approach to using Multivariate Analyses Using Multivariate Statistics, 6th edition provides advanced undergraduate as well as graduate students with a timely and comprehensive introduction to today's most commonly encountered statistical and multivariate techniques, while assuming only a limited knowledge of higher-level mathematics. This text’s practical approach focuses on the benefits and limitations of applications of a technique to a data set – when, why, and how to do it. Learning Goals Upon completing this book, readers should be able to: Learn to conduct numerous types of multivariate statistical analyses Find the best technique to use Understand Limitations to applications Learn how to use SPSS and SAS syntax and output
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## Advanced and Multivariate Statistical Methods

Practical Application and Interpretation

Author: Craig A. Mertler,Rachel Vannatta Reinhart

Publisher: Routledge

ISBN: 1351971662

Category: Psychology

Page: 390

View: 5457

Ideal for non-math majors, Advanced and Multivariate Statistical Methods teaches students to interpret, present, and write up results for each statistical technique without overemphasizing advanced math. This highly applied approach covers the why, what, when and how of advanced and multivariate statistics in a way that is neither too technical nor too mathematical. Students also learn how to compute each technique using SPSS software. New to the Sixth Edition Instructor ancillaries are now available with the sixth edition. All SPSS directions and screenshots have been updated to Version 23 of the software. Student learning objectives have been added as a means for students to target their learning and for instructors to focus their instruction. Key words are reviewed and reinforced in the end of chapter material to ensure that students understand the vocabulary of advanced and multivariate statistics.
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## Applied Multivariate Statistics for the Social Sciences

Author: James Paul Stevens

Publisher: Taylor & Francis

ISBN: 0805859012

Category: Social Science

Page: 651

View: 8738

This best-selling text is written for those who use, rather than develop statistical methods. Dr. Stevens focuses on a conceptual understanding of the material rather than on proving results. Helpful narrative and numerous examples enhance understanding and a chapter on matrix algebra serves as a review. Annotated printouts from SPSS and SAS indicate what the numbers mean and encourage interpretation of the results. In addition to demonstrating how to use these packages, the author stresses the importance of checking the data, assessing the assumptions, and ensuring adequate sample size by providing guidelines so that the results can be generalized. The book is noted for its extensive applied coverage of MANOVA, its emphasis on statistical power, and numerous exercises including answers to half. The new edition features: New chapters on Hierarchical Linear Modeling (Ch. 15) and Structural Equation Modeling (Ch. 16) New exercises that feature recent journal articles to demonstrate the actual use of multiple regression (Ch. 3), MANOVA (Ch. 5), and repeated measures (Ch. 13) A new appendix on the analysis of correlated observations (Ch. 6) Expanded discussions on obtaining non-orthogonal contrasts in repeated measures designs with SPSS and how to make the identification of cell ID easier in log linear analysis in 4 or 5 way designs Updated versions of SPSS (15.0) and SAS (8.0) are used throughout the text and introduced in chapter 1 A book website with data sets and more. Ideal for courses on multivariate statistics found in psychology, education, sociology, and business departments, the book also appeals to practicing researchers with little or no training in multivariate methods. Prerequisites include a course on factorial ANOVA and covariance. Working knowledge of matrix algebra is not assumed.
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## Computer-Aided Multivariate Analysis, Fourth Edition

Author: Abdelmonem Afifi,Susanne May,Virginia A. Clark

Publisher: CRC Press

ISBN: 9781584883081

Category: Mathematics

Page: 512

View: 2689

Computer-Aided Multivariate Analysis, Fourth Edition enables researchers and students with limited mathematical backgrounds to understand the concepts underlying multivariate statistical analysis, perform analysis using statistical packages, and understand the output. New topics include Loess and Poisson regression, nominal and ordinal logistic regression, interpretation of interactions in logistic and survival analysis, and imputation for missing values. This book includes new exercises and references, and updated options in the latest versions of the statistical packages. All data sets and codebooks are available for download. The authors explain the assumptions made in performing each analysis and test, how to determine if your data meets those assumptions, and what to do if they do not. What to Watch out for sections in each chapter warn of common difficulties. By reading this text, you will know what method to use with your data set, how to get the results, and how to interpret them and explain them to others. New in the Fourth Edition: Expanded explanation of checking for goodness of fit in logistic regression and survival analysis Kaplan-Meier estimates of survival curves, formal tests for comparing survival between groups, interactions and the use of time-dependent covariates in survival analysis Expanded discussion of how to handle missing values Latest features of the S-PLUS package in addition to SAS, SPSS, STATA, and STATISTICA for multivariate analysis Data sets for the problems are available at the CRC web site: http://www.crcpress.com/product/isbn/9781584883081 Commands and output for examples used in the text for each statistical package are available at the UCLA web site: http://www.ats.ucla.edu/stat/examples/cama4/
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## Discovering Statistics Using IBM SPSS Statistics

North American Edition

Author: Andy Field

Publisher: SAGE

ISBN: 152644030X

Category: Social Science

Page: 816

View: 659

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## Experimental Designs Using ANOVA

Author: Barbara G. Tabachnick,Linda S. Fidell

Publisher: Brooks/Cole

ISBN: 9780534405144

Category: Mathematics

Page: 724

View: 9977

This text reflects the practical approach of the authors. Barbara Tabachnick and Linda Fidell emphasize the use of statistical software in design and analysis of research in addition to conceptual understanding fostered by the presentation and interpretation of fundamental equations. EXPERIMENTAL DESIGN USING ANOVA includes the regression approach to ANOVA alongside the traditional approach, making it clearer and more flexible. The text includes details on how to perform both simple and complicated analyses by hand through traditional means, through regression, and through SPSS and SAS.
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## Applied Multivariate Statistics with R

Author: Daniel Zelterman

Publisher: Springer

ISBN: 3319140930

Category: Medical

Page: 393

View: 8195

This book brings the power of multivariate statistics to graduate-level practitioners, making these analytical methods accessible without lengthy mathematical derivations. Using the open source, shareware program R, Professor Zelterman demonstrates the process and outcomes for a wide array of multivariate statistical applications. Chapters cover graphical displays, linear algebra, univariate, bivariate and multivariate normal distributions, factor methods, linear regression, discrimination and classification, clustering, time series models, and additional methods. Zelterman uses practical examples from diverse disciplines to welcome readers from a variety of academic specialties. Those with backgrounds in statistics will learn new methods while they review more familiar topics. Chapters include exercises, real data sets, and R implementations. The data are interesting, real-world topics, particularly from health and biology-related contexts. As an example of the approach, the text examines a sample from the Behavior Risk Factor Surveillance System, discussing both the shortcomings of the data as well as useful analyses. The text avoids theoretical derivations beyond those needed to fully appreciate the methods. Prior experience with R is not necessary.
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## Applied Multivariate Statistics for the Social Sciences

Analyses with SAS and IBM’s SPSS, Sixth Edition

Author: Keenan A. Pituch,James P. Stevens

Publisher: Routledge

ISBN: 1317805925

Category: Psychology

Page: 794

View: 4996

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## Applied Multivariate Statistical Analysis: Pearson New International Edition

Author: Richard A. Johnson,Dean W. Wichern

Publisher: Pearson Higher Ed

ISBN: 1292037571

Category: Psychology

Page: 776

View: 7330

For courses in Multivariate Statistics, Marketing Research, Intermediate Business Statistics, Statistics in Education, and graduate-level courses in Experimental Design and Statistics. Appropriate for experimental scientists in a variety of disciplines, this market-leading text offers a readable introduction to the statistical analysis of multivariate observations. Its primary goal is to impart the knowledge necessary to make proper interpretations and select appropriate techniques for analyzing multivariate data. Ideal for a junior/senior or graduate level course that explores the statistical methods for describing and analyzing multivariate data, the text assumes two or more statistics courses as a prerequisite.
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## Introduction to Robust Estimation and Hypothesis Testing

Author: Rand R. Wilcox

ISBN: 012804781X

Category: Mathematics

Page: 810

View: 8878

Introduction to Robust Estimating and Hypothesis Testing, 4th Editon, is a ‘how-to’ on the application of robust methods using available software. Modern robust methods provide improved techniques for dealing with outliers, skewed distribution curvature and heteroscedasticity that can provide substantial gains in power as well as a deeper, more accurate and more nuanced understanding of data. Since the last edition, there have been numerous advances and improvements. They include new techniques for comparing groups and measuring effect size as well as new methods for comparing quantiles. Many new regression methods have been added that include both parametric and nonparametric techniques. The methods related to ANCOVA have been expanded considerably. New perspectives related to discrete distributions with a relatively small sample space are described as well as new results relevant to the shift function. The practical importance of these methods is illustrated using data from real world studies. The R package written for this book now contains over 1200 functions. New to this edition 35% revised content Covers many new and improved R functions New techniques that deal with a wide range of situations Extensive revisions to cover the latest developments in robust regression Covers latest improvements in ANOVA Includes newest rank-based methods Describes and illustrated easy to use software
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## Applied Multivariate Statistical Analysis

Author: Wolfgang Karl Härdle,Léopold Simar

Publisher: Springer

ISBN: 3662451719

Page: 580

View: 6516

Focusing on high-dimensional applications, this 4th edition presents the tools and concepts used in multivariate data analysis in a style that is also accessible for non-mathematicians and practitioners. All chapters include practical exercises that highlight applications in different multivariate data analysis fields. All of the examples involve high to ultra-high dimensions and represent a number of major fields in big data analysis. The fourth edition of this book on Applied Multivariate Statistical Analysis offers the following new features: A new chapter on Variable Selection (Lasso, SCAD and Elastic Net) All exercises are supplemented by R and MATLAB code that can be found on www.quantlet.de. The practical exercises include solutions that can be found in Härdle, W. and Hlavka, Z., Multivariate Statistics: Exercises and Solutions. Springer Verlag, Heidelberg.
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## Statistical Concepts

A Second Course

Author: Richard G. Lomax,Debbie L. Hahs-Vaughn

Publisher: Routledge

ISBN: 1136490051

Category: Psychology

Page: 532

View: 8556

Statistical Concepts consists of the last 9 chapters of An Introduction to Statistical Concepts, 3rd ed. Designed for the second course in statistics, it is one of the few texts that focuses just on intermediate statistics. The book highlights how statistics work and what they mean to better prepare students to analyze their own data and interpret SPSS and research results. As such it offers more coverage of non-parametric procedures used when standard assumptions are violated since these methods are more frequently encountered when working with real data. Determining appropriate sample sizes is emphasized throughout. Only crucial equations are included. The new edition features: New co-author, Debbie L. Hahs-Vaughn, the 2007 recipient of the University of Central Florida's College of Education Excellence in Graduate Teaching Award. A new chapter on logistic regression models for today's more complex methodologies. Much more on computing confidence intervals and conducting power analyses using G*Power. All new SPSS version 19 screenshots to help navigate through the program and annotated output to assist in the interpretation of results. Sections on how to write-up statistical results in APA format and new templates for writing research questions. New learning tools including chapter-opening vignettes, outlines, a list of key concepts, "Stop and Think" boxes, and many more examples, tables, and figures. More tables of assumptions and the effects of their violation including how to test them in SPSS. 33% new conceptual, computational, and all new interpretative problems. A website with Power Points, answers to the even-numbered problems, detailed solutions to the odd-numbered problems, and test items for instructors, and for students the chapter outlines, key concepts, and datasets. Each chapter begins with an outline, a list of key concepts, and a research vignette related to the concepts. Realistic examples from education and the behavioral sciences illustrate those concepts. Each example examines the procedures and assumptions and provides tips for how to run SPSS and develop an APA style write-up. Tables of assumptions and the effects of their violation are included, along with how to test assumptions in SPSS. Each chapter includes computational, conceptual, and interpretive problems. Answers to the odd-numbered problems are provided. The SPSS data sets that correspond to the book’s examples and problems are available on the web. The book covers basic and advanced analysis of variance models and topics not dealt with in other texts such as robust methods, multiple comparison and non-parametric procedures, and multiple and logistic regression models. Intended for courses in intermediate statistics and/or statistics II taught in education and/or the behavioral sciences, predominantly at the master's or doctoral level. Knowledge of introductory statistics is assumed.
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## Statistical Power Analysis

A Simple and General Model for Traditional and Modern Hypothesis Tests, Third Edition

Author: Kevin R. Murphy,Brett Myors,Allen Wolach

Publisher: Routledge

ISBN: 1136910417

Category: Education

Page: 224

View: 7882

First Published in 2009. Routledge is an imprint of Taylor & Francis, an informa company.
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## Introductory Statistics

Author: Sheldon M. Ross

ISBN: 012804361X

Category: Mathematics

Page: 828

View: 3213

Introductory Statistics, Fourth Edition, reviews statistical concepts and techniques in a manner that will teach students not only how and when to utilize the statistical procedures developed, but also how to understand why these procedures should be used. The text's main merits are the clarity of presentation, contemporary examples and applications from diverse areas, an explanation of intuition, and the ideas behind the statistical methods. Concepts are motivated, illustrated, and explained in a way that attempts to increase one's intuition. To quote from the preface, it is only when a student develops a feel or intuition for statistics that she or he is really on the path toward making sense of data. Ross achieves this goal through a coherent mix of mathematical analysis, intuitive discussions, and examples. Applications and examples refer to real-world issues, such as gun control, stock price models, health issues, driving age limits, school admission ages, use of helmets, sports, scientific fraud, and many others. Examples relating to data mining techniques using the number of Google queries or Twitter tweets are also considered. For this fourth edition, new topical coverage includes sections on Pareto distribution and the 80-20 rule, Benford's law, added material on odds and joint distributions and correlation, logistic regression, A-B testing, and more modern (big data) examples and exercises. Includes new section on Pareto distribution and the 80-20 rule, Benford’s law, odds, joint distribution and correlation, logistic regression, A-B testing, and examples from the world of analytics and big data Comprehensive edition that includes the most commonly used statistical software packages (SAS, SPSS, Minitab), ISM, SSM, and an online graphing calculator manual Presents a unique, historical perspective, profiling prominent statisticians and historical events to motivate learning by including interest and context Provides exercises and examples that help guide the student towards indpendent learning using real issues and real data, e.g. stock price models, health issues, gender issues, sports, and scientific fraud
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## Research Methods in Public Administration and Nonprofit Management

Author: David E. McNabb

Publisher: Routledge

ISBN: 135172147X

Category: Political Science

Page: 552

View: 4378

Now in a thoroughly revised and refreshed fourth edition, Research Methods in Public Administration and Nonprofit Management is beloved by students and professors alike for its exceptional clarity and accessibility and plentiful illustrations. This new edition integrates quantitative, qualitative, and mixed-methods approaches, as well as specific up-to-date instruction in the use of statistical software programs such as Excel and SPSS. Changes to this edition include: A new section, featuring two new chapters, to explore mixed-methods approaches to research, including fundamentals, research design, data collection, and analyzing and interpreting findings A new, dedicated chapter on Big Data research Updated exhibits and examples throughout the book A new companion website to accompany the book containing PowerPoint slides for each chapter New exhibits, tables, figures, and exercises, as well as key terms and discussion questions at the end of each chapter Research Methods in Public Administration and Nonprofit Management, 4e is an ideal textbook for use in all research methods courses in undergraduate and graduate public administration, public affairs, and nonprofit management courses.
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## Bayesian Statistics

An Introduction

Author: Peter M. Lee

Publisher: John Wiley & Sons

ISBN: 1118359771

Category: Mathematics

Page: 488

View: 9101

Bayesian Statistics is the school of thought that combines prior beliefs with the likelihood of a hypothesis to arrive at posterior beliefs. The first edition of Peter Lee’s book appeared in 1989, but the subject has moved ever onwards, with increasing emphasis on Monte Carlo based techniques. This new fourth edition looks at recent techniques such as variational methods, Bayesian importance sampling, approximate Bayesian computation and Reversible Jump Markov Chain Monte Carlo (RJMCMC), providing a concise account of the way in which the Bayesian approach to statistics develops as well as how it contrasts with the conventional approach. The theory is built up step by step, and important notions such as sufficiency are brought out of a discussion of the salient features of specific examples. This edition: Includes expanded coverage of Gibbs sampling, including more numerical examples and treatments of OpenBUGS, R2WinBUGS and R2OpenBUGS. Presents significant new material on recent techniques such as Bayesian importance sampling, variational Bayes, Approximate Bayesian Computation (ABC) and Reversible Jump Markov Chain Monte Carlo (RJMCMC). Provides extensive examples throughout the book to complement the theory presented. Accompanied by a supporting website featuring new material and solutions. More and more students are realizing that they need to learn Bayesian statistics to meet their academic and professional goals. This book is best suited for use as a main text in courses on Bayesian statistics for third and fourth year undergraduates and postgraduate students.
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## Statistics As Principled Argument

Author: Robert P. Abelson

Publisher: Psychology Press

ISBN: 1135694419

Category: Psychology

Page: 240

View: 6914

In this illuminating volume, Robert P. Abelson delves into the too-often dismissed problems of interpreting quantitative data and then presenting them in the context of a coherent story about one's research. Unlike too many books on statistics, this is a remarkably engaging read, filled with fascinating real-life (and real-research) examples rather than with recipes for analysis. It will be of true interest and lasting value to beginning graduate students and seasoned researchers alike. The focus of the book is that the purpose of statistics is to organize a useful argument from quantitative evidence, using a form of principled rhetoric. Five criteria, described by the acronym MAGIC (magnitude, articulation, generality, interestingness, and credibility) are proposed as crucial features of a persuasive, principled argument. Particular statistical methods are discussed, with minimum use of formulas and heavy data sets. The ideas throughout the book revolve around elementary probability theory, t tests, and simple issues of research design. It is therefore assumed that the reader has already had some access to elementary statistics. Many examples are included to explain the connection of statistics to substantive claims about real phenomena.
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## Applied Multivariate Research

Design and Interpretation

Author: Lawrence S. Meyers,Glenn Gamst,A.J. Guarino

Publisher: SAGE Publications

ISBN: 1506329780

Category: Social Science

Page: 1016

View: 6272

Using a conceptual, non-mathematical approach, the updated Third Edition of Applied Multivariate Research: Design and Interpretation provides full coverage of the wide range of multivariate topics that graduate students across the social and behavioral sciences encounter. Authors Lawrence S. Meyers, Glenn Gamst, and A. J. Guarino integrate innovative multicultural topics in examples throughout the book, which include both conceptual and practical coverage of: statistical techniques of data screening; multiple regression; multilevel modeling; exploratory factor analysis; discriminant analysis; structural equation modeling; structural equation modeling invariance; survival analysis; multidimensional scaling; and cluster analysis.
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