Understanding and applying basic statistical methods using R / Rand R. Wilcox.

Format:

Book

Author/Creator:

Wilcox, Rand R., author.

Language:

English

Subjects (All):

R (Computer program language).

Statistics--Computer programs.

Statistics.

Physical Description:

1 online resource (501 pages) : illustrations, tables

Edition:

1st ed.

Place of Publication:

Hoboken, New Jersey : Wiley, 2017.

Summary:

Features a straightforward and concise resource for introductory statistical concepts, methods, and techniques using R Understanding and Applying Basic Statistical Methods Using R uniquely bridges the gap between advances in the statistical literature and methods routinely used by non-statisticians. Providing a conceptual basis for understanding the relative merits and applications of these methods, the book features modern insights and advances relevant to basic techniques in terms of dealing with non-normality, outliers, heteroscedasticity (unequal variances), and curvature. Featuring a guide to R, the book uses R programming to explore introductory statistical concepts and standard methods for dealing with known problems associated with classic techniques. Thoroughly class-room tested, the book includes sections that focus on either R programming or computational details to help the reader become acquainted with basic concepts and principles essential in terms of understanding and applying the many methods currently available. Covering relevant material from a wide range of disciplines, Understanding and Applying Basic Statistical Methods Using R also includes: Numerous illustrations and exercises that use data to demonstrate the practical importance of multiple perspectives Discussions on common mistakes such as eliminating outliers and applying standard methods based on means using the remaining data Detailed coverage on R programming with descriptions on how to apply both classic and more modern methods using R A companion website with the data and solutions to all of the exercises Understanding and Applying Basic Statistical Methods Using R is an ideal textbook for an undergraduate and graduate-level statistics courses in the science and/or social science departments. The book can also serve as a reference for professional statisticians and other practitioners looking to better understand modern statistical methods as well as R programming. Rand R. Wilcox, PhD, is Professor in the Department of Psychology at the University of Southern California, Fellow of the Association for Psychological Science, and an associate editor for four statistics journals. He is also a member of the International Statistical Institute. The author of more than 320 articles published in a variety of statistical journals, he is also the author eleven other books on statistics. Dr. Wilcox is creator of WRS (Wilcox' Robust Statistics), which is an R package for performing robust statistical methods. His main research interest includes statistical methods, particularly robust methods for comparing groups and studying associations.

Contents:

Cover

Title Page

Copyright

Contents

List of Symbols

Preface

About the Companion Website

Chapter 1 Introduction

1.1 Samples Versus Populations

1.2 Comments on Software

1.3 R Basics

1.3.1 Entering Data

1.3.2 Arithmetic Operations

1.3.3 Storage Types and Modes

1.3.4 Identifying and Analyzing Special Cases

1.4 R Packages

1.5 Access to Data Used in this Book

1.6 Accessing More Detailed Answers to the Exercises

1.7 Exercises

Chapter 2 Numerical Summaries of Data

2.1 Summation Notation

2.2 Measures of Location

2.2.1 The Sample Mean

2.2.2 The Median

2.2.3 Sample Mean versus Sample Median

2.2.4 Trimmed Mean

2.2.5 R function mean, tmean, and median

2.3 Quartiles

2.3.1 R function idealf and summary

2.4 Measures of Variation

2.4.1 The Range

2.4.2 R function Range

2.4.3 Deviation Scores, Variance, and Standard Deviation

2.4.4 R Functions var and sd

2.4.5 The Interquartile Range

2.4.6 MAD and the Winsorized Variance

2.4.7 R Functions winvar, winsd, idealfIQR, and mad

2.5 Detecting Outliers

2.5.1 A Classic Outlier Detection Method

2.5.2 The Boxplot Rule

2.5.3 The MAD-Median Rule

2.5.4 R Functions outms, outbox, and out

2.6 Skipped Measures of Location

2.6.1 R Function MOM

2.7 Summary

2.8 Exercises

Chapter 3 Plots Plus More Basics on Summarizing Data

3.1 Plotting Relative Frequencies

3.1.1 R Functions table, plot, splot, barplot, and cumsum

3.1.2 Computing the Mean and Variance Based on the Relative Frequencies

3.1.3 Some Features of the Mean and Variance

3.2 Histograms and Kernel Density Estimators

3.2.1 R Function hist

3.2.2 What Do Histograms Tell Us?

3.2.3 Populations, Samples, and Potential Concerns about Histograms

3.2.4 Kernel Density Estimators

3.2.5 R Functions Density and Akerd.

3.3 Boxplots and Stem-and-Leaf Displays

3.3.1 R Function stem

3.3.2 Boxplot

3.3.3 R Function boxplot

3.4 Summary

3.5 Exercises

Chapter 4 Probability and Related Concepts

4.1 The Meaning of Probability

4.2 Probability Functions

4.3 Expected Values, Population Mean and Variance

4.3.1 Population Variance

4.4 Conditional Probability and Independence

4.4.1 Independence and Dependence

4.5 The Binomial Probability Function

4.5.1 R Functions dbinom and pbinom

4.6 The Normal Distribution

4.6.1 Some Remarks about the Normal Distribution

4.6.2 The Standard Normal Distribution

4.6.3 Computing Probabilities for Any Normal Distribution

4.6.4 R Functions pnorm and qnorm

4.7 Nonnormality and The Population Variance

4.7.1 Skewed Distributions

4.7.2 Comments on Transforming Data

4.8 Summary

4.9 Exercises

Chapter 5 Sampling Distributions

5.1 Sampling Distribution of P, the Proportion of Successes

5.2 Sampling Distribution of the Mean Under Normality

5.2.1 Determining Probabilities Associated with the Sample Mean

5.2.2 But Typically Is Not Known. Now What?

5.3 Nonnormality and the Sampling Distribution of the Sample Mean

5.3.1 Approximating the Binomial Distribution

5.3.2 Approximating the Sampling Distribution of the Sample Mean: The General Case

5.4 Sampling Distribution of the Median and 20% Trimmed Mean

5.4.1 Estimating the Standard Error of the Median

5.4.2 R Function msmedse

5.4.3 Approximating the Sampling Distribution of the Sample Median

5.4.4 Estimating the Standard Error of a Trimmed Mean

5.4.5 R Function trimse

5.4.6 Estimating the Standard Error When Outliers Are Discarded: A Technically Unsound Approach

5.5 The Mean Versus the Median and 20% Trimmed Mean

5.6 Summary

5.7 Exercises

Chapter 6 Confidence Intervals.

6.1 Confidence Interval for the Mean

6.1.1 Computing a Confidence Interval Given 2

6.2 Confidence Intervals for the Mean Using s ( Not Known)

6.2.1 R Function t.test

6.3 A Confidence Interval for The Population Trimmed Mean

6.3.1 R Function trimci

6.4 Confidence Intervals for The Population Median

6.4.1 R Function msmedci

6.4.2 Underscoring a Basic Strategy

6.4.3 A Distribution-Free Confidence Interval for the Median Even When There Are Tied Values

6.4.4 R Function sint

6.5 The Impact of Nonnormality on Confidence Intervals

6.5.1 Student's T and Nonnormality

6.5.2 Nonnormality and the 20% Trimmed Mean

6.5.3 Nonnormality and the Median

6.6 Some Basic Bootstrap Methods

6.6.1 The Percentile Bootstrap Method

6.6.2 R Functions trimpb

6.6.3 Bootstrap-t

6.6.4 R Function trimcibt

6.7 Confidence Interval for The Probability of Success

6.7.1 Agresti-Coull Method

6.7.2 Blyth's Method

6.7.3 Schilling-Doi Method

6.7.4 R Functions acbinomci and binomLCO

6.8 Summary

6.9 Exercises

Chapter 7 Hypothesis Testing

7.1 Testing Hypotheses about the Mean, Known

7.1.1 Details for Three Types of Hypotheses

7.1.2 Testing for Exact Equality and Tukey's Three-Decision Rule

7.1.3 p-Values

7.1.4 Interpreting p-Values

7.1.5 Confidence Intervals versus Hypothesis Testing

7.2 Power and Type II Errors

7.2.1 Power and p-Values

7.3 Testing Hypotheses about the mean, Not Known

7.3.1 R Function t.test

7.4 Student's T and Nonnormality

7.4.1 Bootstrap-t

7.4.2 Transforming Data

7.5 Testing Hypotheses about Medians

7.5.1 R Function msmedci and sintv2

7.6 Testing Hypotheses Based on a Trimmed Mean

7.6.1 R Functions trimci, trimcipb, and trimcibt

7.7 Skipped Estimators

7.7.1 R Function momci

7.8 Summary

7.9 Exercises.

Chapter 8 Correlation and Regression

8.1 Regression Basics

8.1.1 Residuals and a Method for Estimating the Median of Y Given X

8.1.2 R function qreg and Qreg

8.2 Least Squares Regression

8.2.1 R Functions lsfit, lm, ols, plot, and abline

8.3 Dealing with Outliers

8.3.1 Outliers among the Independent Variable

8.3.2 Dealing with Outliers among the Dependent Variable

8.3.3 R Functions tsreg and tshdreg

8.3.4 Extrapolation Can Be Dangerous

8.4 Hypothesis Testing

8.4.1 Inferences about the Least Squares Slope and Intercept

8.4.2 R Functions lm, summary, and ols

8.4.3 Heteroscedcasticity: Some Practical Concerns and How to Address Them

8.4.4 R Function olshc4

8.4.5 Outliers among the Dependent Variable: A Cautionary Note

8.4.6 Inferences Based on the Theil-Sen Estimator

8.4.7 R Functions regci and regplot

8.5 Correlation

8.5.1 Pearson's Correlation

8.5.2 Inferences about the Population Correlation, p

8.5.3 R Functions pcor and pcorhc4

8.6 Detecting Outliers When Dealing with Two or More Variables

8.6.1 R Functions out and outpro

8.7 Measures of Association: Dealing with Outliers

8.7.1 Kendall's Tau

8.7.2 R Functions tau and tauci

8.7.3 Spearman's Rho

8.7.4 R Functions spear and spearci

8.7.5 Winsorized and Skipped Correlations

8.7.6 R Functions scor, scorci, scorciMC, wincor, and wincorci

8.8 Multiple Regression

8.8.1 Least Squares Regression

8.8.2 Hypothesis Testing

8.8.3 R Function olstest

8.8.4 Inferences Based on a Robust Estimator

8.8.5 R Function regtest

8.9 Dealing with Curvature

8.9.1 R Function lplot and rplot

8.10 Summary

8.11 Exercises

Chapter 9 Comparing Two Independent Groups

9.1 Comparing Means

9.1.1 The Two-Sample Student's T Test

9.1.2 Violating Assumptions When Using Student's T.

9.1.3 Why Testing Assumptions Can Be Unsatisfactory

9.1.4 Interpreting Student's T When It Rejects

9.1.5 Dealing with Unequal Variances: Welch's Test

9.1.6 R Function t.test

9.1.7 Student's T versus Welch's Test

9.1.8 The Impact of Outliers When Comparing Means

9.2 Comparing Medians

9.2.1 A Method Based on the McKean-Schrader Estimator

9.2.2 A Percentile Bootstrap Method

9.2.3 R Functions msmed, medpb2, split, and fac2list

9.2.4 An Important Issue: The Choice of Method can Matter

9.3 Comparing Trimmed Means

9.3.1 R Functions yuen, yuenbt, and trimpb2

9.3.2 Skipped Measures of Location and Deleting Outliers

9.3.3 R Function pb2gen

9.4 Tukey's Three-Decision Rule

9.5 Comparing Variances

9.5.1 R Function comvar2

9.6 Rank-Based (Nonparametric) Methods

9.6.1 Wilcoxon-Mann-Whitney Test

9.6.2 R Function wmw

9.6.3 Handling Heteroscedasticity

9.6.4 R Functions cid and cidv2

9.7 Measuring Effect Size

9.7.1 Cohen's d

9.7.2 Concerns about Cohen's d and How They Might Be Addressed

9.7.3 R Functions akp.effect, yuenv2, and med.effect

9.8 Plotting Data

9.8.1 R Functions ebarplot, ebarplot.med, g2plot, and boxplot

9.9 Comparing Quantiles

9.9.1 R Function qcomhd

9.10 Comparing Two Binomial Distributions

9.10.1 Improved Methods

9.10.2 R Functions twobinom and twobicipv

9.11 A Method for Discrete or Categorical Data

9.11.1 R Functions disc2com, binband, and splotg2

9.12 Comparing Regression Lines

9.12.1 Classic ANCOVA

9.12.2 R Function CLASSanc

9.12.3 Heteroscedastic Methods for Comparing the Slopes and Intercepts

9.12.4 R Functions olsJ2 and ols2ci

9.12.5 Dealing with Outliers among the Dependent Variable

9.12.6 R Functions reg2ci, ancGpar, and reg2plot

9.12.7 A Closer Look at Comparing Nonparallel Regression Lines

9.12.8 R Function ancJN.

9.13 Summary.

Notes:

Includes bibliographical references (p. [465]-472) and index

Includes bibliographical references and index.

Description based on print version record.

ISBN:

1-119-06140-7

OCLC:

949759319