Modern statistics for the social and behavioral sciences : a practical introduction / Rand Wilcox.

Format:

Book

Author/Creator:

Wilcox, Rand R., author.

Contributor:

ProQuest ebook central.

Language:

English

Subjects (All):

Social sciences--Statistical methods.

Social sciences.

Psychology--Statistical methods.

Psychology.

Physical Description:

1 online resource (xxiii, 706 pages) : illustrations

Edition:

Second edition.

Place of Publication:

Boca Raton, FL : CRC Press, Taylor & Francis Group, [2017]

System Details:

text file

Contents:

Note continued: 13.6.4. R Function cmanova

13.7. Multivariate Regression

13.7.1. Multivariate Regression Using R

13.7.2. Robust Multivariate Regression

13.7.3. R Function mlrreg and mopreg

13.8. Principal Components

13.8.1. R Functions prcomp and regpca

13.8.2. Robust Principal Components

13.8.3. R Functions outpca, robpca, robpcaS, Ppca, and Ppca.summary

13.9. Exercises

ch. 14 Robust Regression and Measures of Association

14.1. Robust Regression Estimators

14.1.1. The Theil

Sen Estimator

14.1.2. R Functions tsreg, tshdreg, and regplot

14.1.3. Least Median of Squares

14.1.4. Least Trimmed Squares and Least Trimmed Absolute Value Estimators

14.1.5. R Functions lmsreg, ltsreg, and ltareg

14.1.6. M-estimators

14.1.7. R Function chreg

14.1.8. Deepest Regression Line

14.1.9. R Function mdepreg

14.1.10. Skipped Estimators

14.1.11. R Functions opreg and opregMC

14.1.12. S-estimators and an E-type Estimator

14.1.13. R Function tstsreg

14.2. Comments on Choosing a Regression Estimator

14.3. Inferences Based on Robust Regression Estimators

14.3.1. Testing Hypotheses About the Slopes

14.3.2. Inferences About the Typical Value of Y Given X

14.3.3. R Functions regtest, regtestMC, regci, regciMC, regYci, and regYband

14.3.4. Comparing Measures of Location via Dummy Coding

14.4. Dealing with Curvature: Smoothers

14.4.1. Cleveland's Smoother

14.4.2. R Functions lowess, lplot, lplot.pred, and lplotCI

14.4.3. Smoothers Based on Robust Measures of Location

14.4.4. R Functions rplot, rplotCIS, rplotCI, rplotCIv2, rplotCIM, rplot.pred, qhdsm, and qhdsm.pred

14.4.5. Prediction When X Is Discrete: The R Function rundis

14.4.6. Seeing Curvature with More Than Two Predictors

14.4.7. R Function prplot

14.4.8. Some Alternative Methods

14.4.9. Detecting Heteroscedasticity Using a Smoother

14.4.10. R Function rhom

14.5. Some Robust Correlations and Tests of Independence

14.5.1. Kendall's tau

14.5.2. Spearman's rho

14.5.3. Winsorized Correlation

14.5.4. R Function wincor

14.5.5. OP or Skipped Correlation

14.5.6. R Function scor

14.5.7. Inferences about Robust Correlations: Dealing with Heteroscedasticity

14.5.8. R Functions corb and scorci

14.6. Measuring the Strength of an Association Based on a Robust Fit

14.7. Comparing the Slopes of Two Independent Groups

14.7.1. R Function reg2ci

14.8. Tests for Linearity

14.8.1. R Functions lintest, lintestMC, and linchk

14.9. Identifying the Best Predictors

14.9.1. Inferences Based on Independent Variables Taken in Isolation

14.9.2. R Functions regpord, ts2str, and sm2strv7

14.9.3. Inferences When Independent Variables Are Taken Together

14.9.4. R Function reglVcom

14.10. Interactions and Moderator Analyses

14.10.1. R Functions olshc4.inter, ols.plot.inter, regci.inter, reg.plot.inter and adtest

14.10.2. Graphical Methods for Assessing Interactions

14.10.3. R Functions kercon, runsm2g, regi

14.11. ANCOVA

14.11.1. Classic ANCOVA

14.11.2. Robust ANCOVA Methods Based on a Parametric Regression Model

14.11.3. R Functions ancJN, ancJNmp, anclin, reg2plot, and reg2g.p2plot

14.11.4. ANCOVA Based on the Running-interval Smoother

14.11.5. R Functions ancsm, Qancsm, ancova, ancovaWMW, ancpb, ancov-aUB, ancboot, ancdet, runmean2g, qhdsm2g, and 12plot

14.11.6. R Functions Dancts, Dancols, Dancova, Dancovapb, DancovaUB, and Dancdet

14.12. Exercises

ch. 15 Basic Methods for Analyzing Categorical Data

15.1. Goodness of Fit

15.1.1. R Functions chisq.test and pwr.chisq.test

15.2. A Test of Independence

15.2.1. R Function chi.test.ind

15.3. Detecting Differences in the Marginal Probabilities

15.3.1. R Functions contab and mcnemar.test

15.4. Measures of Association

15.4.1. The Proportion of Agreement

15.4.2. Kappa

15.4.3. Weighted Kappa

15.4.4. R Function Ckappa

15.5. Logistic Regression

15.5.1. R Functions glm and logreg

15.5.2. A Confidence Interval for the Odds Ratio

15.5.3. R Function ODDSR. CI

15.5.4. Smoothers for Logistic Regression

15.5.5. R Functions logrsm, rplot.bin, and logSM

15.6. Exercises

Appendix A Answers to Selected Exercises

Appendix B TABLES

Appendix C BASIC MATRIX ALGEBRA.

Note continued: 7.8.9. R Function ks

7.8.10. Comparing All Quantiles Simultaneously: An Extension of the Kolmogorov-Smirnov Test

7.8.11. R Function sband

7.9. Graphical Methods for Comparing Groups

7.9.1. Error Bars

7.9.2. R Functions ebarplot and ebarplot.med

7.9.3. Plotting the Shift Function

7.9.4. Plotting the Distributions

7.9.5. R Function sumplot2g

7.9.6. Other Approaches

7.10. Comparing Measures of Variation

7.10.1. R Function comvar2

7.10.2. Brown-Forsythe Method

7.10.3. Comparing Robust Measures of Variation

7.11. Measuring Effect Size

7.11.1. R Functions yuenv2 and akp.effect

7.12. Comparing Correlations and Regression Slopes

7.12.1. R Functions twopcor, twolsreg, and tworegwb

7.13. Comparing Two Binomials

7.13.1. Storer-Kim Method

7.13.2. Beal's Method

7.13.3. R Functions twobinom, twobici, bi2KMSv2, and power.prop.test

7.13.4. Comparing Two Discrete Distributions

7.13.5. R Function disc2com

7.14. Making Decisions About which Method to Use

7.15. Exercises

ch. 8 Comparing Two Dependent Groups

8.1. The Paired T Test

8.1.1. When Does the Paired T Test Perform Well?

8.1.2. R Function t. test

8.2. Comparing Robust Measures of Location

8.2.1. R Functions yuend, ydbt, and dmedpb

8.2.2. Comparing Marginal M-Estimators

8.2.3. R Function rmmest

8.2.4. Measuring Effect Size

8.2.5. R Function D.akp.effect

8.3. Handling Missing Values

8.3.1. R Functions rm2miss and rmmismcp

8.4. A Different Perspective when Using Robust Measures of Location

8.4.1. R Functions loc2dif and 12drmci

8.5. The Sign Test

8.5.1. R Function signt

8.6. Wilcoxon Signed Rank Test

8.6.1. R Function wilcox.test

8.7. Comparing Variances

8.7.1. R Function comdvar

8.8. Comparing Robust Measures of Scale

8.8.1. R Function rmrvar

8.9. Comparing All Quantiles

8.9.1. R Functions lband

8.10. Plots for Dependent Groups

8.10.1. R Function g2plotdifxy

8.11. Exercises

ch. 9 One-Way Anova

9.1. Analysis of Variance for Independent Groups

9.1.1. A Conceptual Overview

9.1.2. ANOVA via Least Squares Regression and Dummy Coding

9.1.3. R Functions anova, anoval, aov, and fac2list

9.1.4. Controlling Power and Choosing the Sample Sizes

9.1.5. R Functions power.anova.test and anova.power

9.2. Dealing with Unequal Variances

9.2.1. Welch's Test

9.3. Judging Sample Sizes and Controlling Power when Data are Available

9.3.1. R Functions bdanoval and bdanova2

9.4. Trimmed Means

9.4.1. R Functions t1way, tlwayv2, t1wayF, and g5plot

9.4.2. Comparing Groups Based on Medians

9.4.3. R Function med1way

9.5. Bootstrap Methods

9.5.1. A Bootstrap-t Method

9.5.2. R Functions t1waybt and BFBANOVA

9.5.3. Two Percentile Bootstrap Methods

9.5.4. R Functions b1way, pbadepth, and Qanova

9.5.5. Choosing a Method

9.6. Random Effects Model

9.6.1. A Measure of Effect Size

9.6.2. A Heteroscedastic Method

9.6.3. A Method Based on Trimmed Means

9.6.4. R Function rananova

9.7. Rank-Based Methods

9.7.1. The Kruskall

Wallis Test

9.7.2. R Function kruskal.test

9.7.3. Method BDM

9.7.4. R Functions bdm and bdmP

9.8. Exercises

ch. 10 Two-Way and Three-Way Designs

10.1. Basics of a Two-Way Anova Design

10.1.1. Interactions

10.1.2. R Functions interaction.plot and interplot

10.1.3. Interactions When There Are More Than Two Levels

10.2. Testing Hypotheses About Main Effects and Interactions

10.2.1. R function anova

10.2.2. Inferences About Disordinal Interactions

10.2.3. The Two-Way ANOVA Model

10.3. Heteroscedastic Methods for Trimmed Means, Includingmeans

10.3.1. R Function t2way

10.4. Bootstrap Methods

10.4.1. R Functions pbad2way and t2waybt

10.5. Testing Hypotheses Based on Medians

10.5.1. R Function m2way

10.6. A Rank-Based Method for a Two-Way Design

10.6.1. R Function bdm2way

10.6.2. The Patel

Hoel Approach to Interactions

10.7. Three-Way Anova

10.7.1. R Functions anova and t3way

10.8. Exercises

ch. 11 Comparing More than Two Dependent Groups

11.1. Comparing Means in a One-Way Design

11.1.1. R Function aov

11.2. Comparing Trimmed Means When Dealing With a One-Way Design

11.2.1. R Functions rmanova and rmdat2mat

11.2.2. A Bootstrap-t Method for Trimmed Means

11.2.3. R Function rmanovab

11.3. Percentile Bootstrap Methods for a One-Way Design

11.3.1. Method Based on Marginal Measures of Location

11.3.2. R Function bdlway

11.3.3. Inferences Based on Difference Scores

11.3.4. R Function rmdzero

11.4. Rank-Based Methods for a One-Way Design

11.4.1. Friedman's Test

11.4.2. R Function friedman.test

11.4.3. Method BPRM

11.4.4. R Function bprm

11.5. Comments on Which Method to Use

11.6. Between-By-Within Designs

11.6.1. Method for Trimmed Means

11.6.2. R Function bwtrim and bw2list

11.6.3. A Bootstrap-t Method

11.6.4. R Function tsplitbt

11.6.5. Inferences Based on M-estimators and Other Robust Measures of Location

11.6.6. R Functions sppba, sppbb, and sppbi

11.6.7. A Rank-Based Test

11.6.8. R Function bwrank

11.7. Within-By-Within Design

11.7.1. R Function wwtrim

11.8. Three-Way Designs

11.8.1. R Functions bbwtrim, bwwtrim, and wwwtrim

11.8.2. Data Management: R Functions bw2list and bbw2list

11.9. Exercises

ch. 12 Multiple Comparisons

12.1. One-Way Anova and Related Situations, Independent Groups

12.1.1. Fisher's Least Significant Difference Method

12.1.2. The Tukey

Kramer Method

12.1.3. R Function TukeyHSD

12.1.4. Tukey

Kramer and the ANOVA F Test

12.1.5. Step-Down Methods

12.1.6. Dunnett's T3

12.1.7. Games

Howell Method

12.1.8. Comparing Trimmed Means

12.1.9. R Functions lincon, stepmcp and twoKlin

12.1.10. Alternative Methods for Controlling FWE

12.1.11. Percentile Bootstrap Methods for Comparing Trimmed Means, Medians, and M-estimators

12.1.12. R Functions medpb, linconpb, pbmcp, and p.adjust

12.1.13. A Bootstrap-t Method

12.1.14. R Function linconbt

12.1.15. Rank-Based Methods

12.1.16. R Functions cidmul, cidmulv2, and bmpmul

12.1.17. Comparing the Individual Probabilities of Two Discrete Distributions

12.1.18. R Functions binband, splotg2, cumrelf, and cumrelfT

12.1.19. Comparing the Quantifies of Two Independent Groups

12.1.20. R Functions qcomhd and qcomhdMC

12.1.21. Multiple Comparisons for Binomial and Categorical Data

12.1.22. R Functions skmcp and discmcp

12.2. Two-Way, Between-By-Between Design

12.2.1. Scheffe's Homoscedastic Method

12.2.2. Heteroscedastic Methods

12.2.3. Extension of Welch

Sidak and Kaiser

Bowden Methods to Trimmed Means

12.2.4. R Function kbcon

12.2.5. R Functions con2way and conCON

12.2.6. Linear Contrasts Based on Medians

12.2.7. R Functions msmed and mcp2med

12.2.8. Bootstrap Methods

12.2.9. R Functions mcp2a, bbmcppb, bbmcp

12.2.10. The Patel

Hoel Rank-Based Interaction Method

12.2.11. R Function rimul

12.3. Judging Sample Sizes

12.3.1. Tamhane's Procedure

12.3.2. R Function tamhane

12.3.3. Hochberg's Procedure

12.3.4. R Function hochberg

12.4. Methods for Dependent Groups

12.4.1. Linear Contrasts Based on Trimmed Means

12.4.2. R Function rmmcp

12.4.3. Comparing M-estimators

12.4.4. R Functions rmmcppb, dmedpb, dtrimpb, and boxdif

12.4.5. Bootstrap-t Method

12.4.6. R Function bptd

12.4.7. Comparing the Quantiles of the Marginal Distributions

12.4.8. R Function Dqcomhd

12.5. Between-By-Within Designs

12.5.1. R Functions bwmcp, bwamcp, bwbmcp, bwimcp, spmcpa, spmcpb, spmcpi, and bwmcppb

12.6. Within-By-Within Designs

12.6.1. Three-Way Designs

12.6.2. R Functions con3way, mcp3atm, and rm3mcp

12.6.3. Bootstrap Methods for Three-Way Designs

12.6.4. R Functions bbwmcp, bwwmcp, bwwmcppb, bbbmcppb, bbwmcppb, bwwmcppb, and wwwmcppb

12.7. Exercises

ch. 13 Some Multivariate Methods

13.1. Location, Scatter, and Detecting Outliers

13.1.1. Detecting Outliers Via Robust Measures of Location and Scatter

13.1.2. R Functions cov.mve and cov.mcd

13.1.3. More Measures of Location and Covariance

13.1.4. R Functions rmba, tbs, and ogk

13.1.5. R Function out

13.1.6. A Projection-Type Outlier Detection Method

13.1.7. R Functions outpro, outproMC, outproad, outproadMC, and out3d

13.1.8. Skipped Estimators of Location

13.1.9. R Function smean

13.2. One-Sample Hypothesis Testing

13.2.1. Comparing Dependent Groups

13.2.2. R Functions smeancrv2, hotel, and rmdzeroOP

13.3. Two-Sample Case

13.3.1. R Functions smcan2, mat2grp, matsplit, and mat2list

13.3.2. R functions matsplit, mat2grp, and mat2list

13.4. Manova

13.4.1. R Function manova

13.4.2. Robust MANOVA Based on Trimmed Means

13.4.3. R Functions MULtr.anova and MULAOVp

13.5. A Multivariate Extension of the Wilcoxon-Mann-Whitney Test

13.5.1. Explanatory Measure of Effect Size: A Projection-Type Generalization

13.5.2. R Function mulwmwv2

13.6. Rank-Based Multivariate Methods

13.6.1. The Munzel

Brunner Method

13.6.2. R Function mulrank

13.6.3. The Choi

Marden Multivariate Rank Test

Notes:

"A Chapman & Hall book."

Includes bibliographical references and index.

Electronic reproduction. Ann Arbor, MI Available via World Wide Web.

Description based on print version record.

ISBN:

9781498796798

1498796796

Publisher Number:

99987208731

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