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

    • Title:Modern statistics for the social and behavioral sciences : a practical introduction / Rand Wilcox.
    •    
    • Author/Creator:Wilcox, Rand R.
    • Published/Created:Boca Raton, FL : CRC Press, ©2012.
    • Holdings

       
    • Library of Congress Subjects:Social sciences--Statistical methods.
      Psychology--Statistical methods.
    • Description:xx, 840 p. : ill. ; 25 cm.
    • Notes:Includes bibliographical references and index.
    • ISBN:9781439834565 (hbk. : alk. paper)
      1439834563 (hbk. : alk. paper)
    • Contents:Machine generated contents note: 1. Introduction
      1.1. Samples versus Populations
      1.2. Software
      1.3. R Basics
      1.3.1. Entering Data
      1.3.2. R Functions and Packages
      1.3.3. Data Sets
      1.3.4. Arithmetic Operations
      2. Numerical And Graphical Summaries Of Data
      2.1. Basic Summation Notation
      2.2. Measures of Location
      2.2.1. Sample Mean
      2.2.2. R Function Mean
      2.2.3. Sample Median
      2.2.4. R Function for the Median
      2.2.5. Criticism of the Median: It Might Trim Too Many Values
      2.2.6. R Function for the Trimmed Mean
      2.2.7. Winsorized Mean
      2.2.8. R Function winmean
      2.2.9. What Is a Measure of Location?
      2.3. Measures of Variation or Scale
      2.3.1. Sample Variance and Standard Deviation
      2.3.2. R Functions for the Variance and Standard Deviation
      2.3.3. Interquartile Range
      2.3.4. R Function idealf
      2.3.5. Winsorized Variance
      2.3.6. R Function winvar
      2.3.7. Median Absolute Deviation
      2.3.8. R Function mad
      2.3.9. Average Absolute Distance from the Median
      2.3.10. Other Robust Measures of Variation
      2.3.11. R Functions bivar, pbvar, tauvar, and tbs
      2.4. Detecting Outliers
      2.4.1. Method Based on the Mean and Variance
      2.4.2. Better Outlier Detection Rule: The MAD-Median Rule
      2.4.3. R Function out
      2.4.4. Boxplot
      2.4.5. R Function boxplot
      2.4.6. Modifications of the Boxplot Rule for Detecting Outliers
      2.4.7. R Function outbox
      2.4.8. Other Measures of Location
      2.4.9. R Functions mom and onestep
      2.5. Histograms
      2.5.1. R Functions hist and splot
      2.6. Kernel Density Estimators
      2.6.1. R Functions kdplot and akerd
      2.7. Stem-and-Leaf Displays
      2.7.1. R Function stem
      2.8. Skewness
      2.8.1. Transforming Data
      2.9. Choosing a Measure of Location
      2.10. Covariance and Pearson's Correlation
      2.11. Exercises
      3. Probability And Related Concepts
      3.1. Basic Probability
      3.2. Expected Values
      3.3. Conditional Probability and Independence
      3.4. Population Variance
      3.5. Binomial Probability Function
      3.6. Continuous Variables and the Normal Curve
      3.6.1. Computing Probabilities Associated with Normal Distributions
      3.6.2. R Function pnorm
      3.7. Understanding the Effects of Non-Normality
      3.7.1. Skewness
      3.8. Pearson's Correlation and the Population Covariance
      3.8.1. Computing the Population Covariance and Pearson's Correlation
      3.9. Some Rules about Expected Values
      3.10. Chi-Squared Distributions
      3.11. Exercises
      4. Sampling Distributions And Confidence Intervals
      4.1. Random Sampling
      4.2. Sampling Distributions
      4.2.1. Sampling Distribution of the Sample Mean
      4.2.2. Computing Probabilities Associated with the Sample Mean
      4.3. Confidence Interval for the Population Mean
      4.3.1. Known Variance
      4.3.2. Confidence Intervals When σ Is Not Known
      4.3.3. R Functions pt and qt
      4.3.4. Confidence Interval for the Population Mean Using Student's T
      4.3.5. R Function t.test
      4.4. Judging Location Estimators Based on Their Sampling Distribution
      4.4.1. Trimming and Accuracy: Another Perspective
      4.5. Approach to Non-Normality: The Central Limit Theorem
      4.6. Student's T and Non-Normality
      4.7. Confidence Intervals for the Trimmed Mean
      4.7.1. Estimating the Standard Error of a Trimmed Mean
      4.7.2. R Function trimse
      4.8. Confidence Interval for the Population Trimmed Mean
      4.8.1. R Function trimci
      4.9. Transforming Data
      4.10. Confidence Interval for the Population Median
      4.10.1. R Function sint
      4.10.2. Estimating the Standard Error of the Sample Median
      4.10.3. R Function msmedse
      4.10.4. More Concerns about Tied Values
      4.11. Remark About MOM and M-Estimators
      4.12. Confidence Intervals for the Probability of Success
      4.12.1. R Functions binomci and acbinomci
      4.13. Exercises
      5. Hypothesis Testing
      5.1. Basics of Hypothesis Testing
      5.1.1. P-Value or Significance Level
      5.1.2. R Function t.test
      5.1.3. Criticisms of Two-Sided Hypothesis Testing and P-Values
      5.1.4. Summary and Generalization
      5.2. Power and Type II Errors
      5.2.1. Understanding How n, α, and σ Are Related to Power
      5.3. Testing Hypotheses about the Mean When σ Is Not Known
      5.4. Controlling Power and Determining n
      5.4.1. Choosing n Prior to Collecting Data
      5.4.2. R Function power.t.test
      5.4.3. Stein's Method: Judging the Sample Size When Data Are Available
      5.4.4. R Functions stein1 and stein2
      5.5. Practical Problems with Student's T Test
      5.6. Hypothesis Testing Based on a Trimmed Mean
      5.6.1. R Function trimci
      5.6.2. R Functions stein1.tr and stein2.tr
      5.7. Testing Hypotheses about the Population Median
      5.7.1. R Function sintv2
      5.8. Making Decisions about Which Measure of Location to Use
      5.9. Exercises
      6. Regression And Correlation
      6.1. Least Squares Principle
      6.2. Confidence Intervals and Hypothesis Testing
      6.2.1. Classic Inferential Techniques
      6.2.2. Multiple Regression
      6.2.3. R Functions ols, lm, and olsplot
      6.3. Standardized Regression
      6.4. Practical Concerns about Least Squares Regression and How They Might Be Addressed
      6.4.1. Effect of Outliers on Least Squares Regression
      6.4.2. Beware of Bad Leverage Points
      6.4.3. Beware of Discarding Outliers among the Y Values
      6.4.4. Do Not Assume Homoscedasticity or That the Regression Line Is Straight
      6.4.5. Violating Assumptions When Testing Hypotheses
      6.4.6. Dealing with Heteroscedasticity: The HC4 Method
      6.4.7. R Functions olshc4 and hc4test
      6.5. Pearson's Correlation and the Coefficient of Determination
      6.5.1. Closer Look at Interpreting r
      6.6. Testing H0: ρ = 0
      6.6.1. R Functions cor.test and pwr.t.test
      6.6.2. R Function pwr.r.test
      6.6.3. Testing H0: ρ = 0 When There is Heteroscedasticity
      6.6.4. R Function pcorhc4
      6.6.5. When Is It Safe to Conclude That Two Variables Are Independent?
      6.7. Regression Method for Estimating the Median of Y and Other Quantiles
      6.7.1. R Function rqfit
      6.8. Detecting Heteroscedasticity
      6.8.1. R Function khomreg
      6.9. Concluding Remarks
      6.10. Exercises
      7. Bootstrap Methods
      7.1. Bootstrap-t Method
      7.1.1. Symmetric Confidence Intervals
      7.1.2. Exact Nonparametric Confidence Intervals for Means Are Impossible
      7.2. Percentile Bootstrap Method
      7.3. Inferences about Robust Measures of Location
      7.3.1. Using the Percentile Method
      7.3.2. R Functions onesampb, momci, and trimpb
      7.3.3. Bootstrap-t Method Based on Trimmed Means
      7.3.4. R Function trimcibt
      7.4. Estimating Power When Testing Hypotheses about a Trimmed Mean
      7.4.1. R Functions powt1est and powt1an
      7.5. Bootstrap Estimate of Standard Errors
      7.5.1. R Function bootse
      7.6. Inferences about Pearson's Correlation: Dealing with Heteroscedasticity
      7.6.1. R Function pcorb
      7.7. Bootstrap Methods for Least Squares Regression
      7.7.1. R Functions hc4wtest, olswbtest, lsfitci
      7.8. Detecting Associations Even When There Is Curvature
      7.8.1. R Functions indt and medind
      7.9. Quantile Regression
      7.9.1. R Functions qregci and rqtest
      7.9.2. Test for Homoscedasticity Using a Quantile Regression Approach
      7.9.3. R Function qhomt
      7.10. Regression: Which Predictors Are Best?
      7.10.1. R Function regpre
      7.10.2. Least Angle Regression
      7.10.3. R Function larsR
      7.11. Comparing Correlations
      7.11.1. R Functions TWOpov and TWOpNOV
      7.12. Empirical Likelihood
      7.13. Exercises
      8. Comparing Two Independent Groups
      8.1. Student's T Test
      8.1.1. Choosing the Sample Sizes
      8.1.2. R Function power.t.test
      8.2. Relative Merits of Student's T
      8.3. Welch's Heteroscedastic Method for Means
      8.3.1. R Function t.test
      8.3.2. Tukey's Three-Decision Rule
      8.3.3. Non-Normality and Welch's Method
      8.3.4. Three Modern Insights Regarding Methods for Comparing Means
      8.4. Methods for Comparing Medians and Trimmed Means
      8.4.1. Yuen's Method for Trimmed Means
      8.4.2. R Functions yuen and fac2list
      8.4.3. Comparing Medians
      8.4.4. R Function msmed
      8.5. Percentile Bootstrap Methods for Comparing Measures of Location
      8.5.1. Using Other Measures of Location
      8.5.2. Comparing Medians
      8.5.3. R Function medpb2
      8.5.4. Some Guidelines on When to Use the Percentile Bootstrap Method
      8.5.5. R Functions trimpb2 and pb2gen
      8.6. Bootstrap-t Methods for Comparing Measures of Location
      8.6.1. Comparing Means
      8.6.2. Bootstrap-t Method When Comparing Trimmed Means
      8.6.3. R Functions yuenbt and yhbt
      8.6.4. Estimating Power and Judging the Sample Sizes
      8.6.5. R Functions powest and pow2an
      8.7. Permutation Tests
      8.7.1. R Function permg
      8.8. Rank-Based and Nonparametric Methods
      8.8.1. Wilcoxon
      -Mann
      -Whitney Test
      8.8.2. R Functions wmw and wilcox.test
      8.8.3. Handling Tied Values and Heteroscedasticity
      8.8.4. Cliff's Method
      8.8.5. R functions cid and cidv2
      8.8.6. Brunner
      -Munzel Method
      8.8.7. R function bmp
      8.8.8. Kolmogorov
      -Smirnov Test
      8.8.9. R Function ks
      8.8.10. Comparing All Quantiles Simultaneously: An Extension of the Kolmogorov
      -Smirnov Test
      8.8.11. R Function sband
      8.9. Graphical Methods for Comparing Groups
      8.9.1. Error Bars
      Contents note continued: 8.9.2. R Function ebarplot
      8.9.3. Plotting the Shift Function
      8.9.4. Plotting the Distributions
      8.9.5. R Function sumplot2g
      8.9.6. Other Approaches
      8.10. Comparing Measures of Scale
      8.11. Methods for Comparing Measures of Variation
      8.11.1. R Function comvar2
      8.11.2. Brown
      -Forsythe Method
      8.11.3. Comparing Robust Measures of Variation
      8.12. Measuring Effect Size
      8.12.1. R Functions yuenv2 and akp.effect
      8.13. Comparing Correlations and Regression Slopes
      8.13.1. R Functions twopcor, twolsreg, and tworegwb
      8.14. Comparing Two Binomials
      8.14.1. Storer
      -Kim Method
      8.14.2. Beal's Method
      8.14.3. R Functions twobinom, twobici, and power.prop.test
      8.15. Making Decisions about Which Method to Use
      8.16. Exercises
      9. Comparing Two Dependent Groups
      9.1. Paired T Test
      9.1.1. When Does the Paired T Test Perform Well?
      9.1.2. R Function t.test
      9.2. Comparing Robust Measures of Location
      9.2.1. R Functions yuend, ydbt, and dmedpb
      9.2.2. Comparing Marginal M-Estimators
      9.2.3. R Function rmmest
      9.3. Handling Missing Values
      9.3.1. R Functions rm2miss and rmmismcp
      9.4. Different Perspective When Using Robust Measures of Location
      9.4.1. R Functions loc2dif and 12drmci
      9.5. Sign Test
      9.5.1. R Function signt
      9.6. Wilcoxon Signed Rank Test
      9.6.1. R Function wilcox.test
      9.7. Comparing Variances
      9.8. Comparing Robust Measures of Scale
      9.8.1. R Function rmrvar
      9.9. Comparing All Quantiles
      9.9.1. R Function lband
      9.10. Plots for Dependent Groups
      9.10.1. R Function g2plotdifxy
      9.11. Exercises
      10. One-Way Anova
      10.1. Analysis of Variance for Independent Groups
      10.1.1. Conceptual Overview
      10.1.2. ANOVA via Least Squares Regression and Dummy Coding
      10.1.3. R Functions anova, anoval, aov, and fac2list
      10.1.4. Controlling Power and Choosing the Sample Sizes
      10.1.5. R Functions power.anova.test and anova.power
      10.2. Dealing with Unequal Variances
      10.2.1. Welch's Test
      10.3. Judging Sample Sizes and Controlling Power When Data Are Available
      10.3.1. R Functions bdanoval and bdanova2
      10.4. Trimmed Means
      10.4.1. R Functions t1way, t1wayv2, and t1wayF
      10.4.2. Comparing Groups Based on Medians
      10.4.3. R Function med1way
      10.5. Bootstrap Methods
      10.5.1. Bootstrap-t Method
      10.5.2. R Function t1waybt
      10.5.3. Two Percentile Bootstrap Methods
      10.5.4. R Functions b1way and pbadepth
      10.5.5. Choosing a Method
      10.6. Random Effects Model
      10.6.1. Measure of Effect Size
      10.6.2. Heteroscedastic Method
      10.6.3. Method Based on Trimmed Means
      10.6.4. R Function rananova
      10.7. Rank-Based Methods
      10.7.1. Kruskall
      -Wallis Test
      10.8. R Function kruskal.test
      10.8.1. Method BDM
      10.8.2. R Function bdm
      10.9. Exercises
      11. Two-Way And Three-Way Designs
      11.1. Basics of a Two-Way ANOVA Design
      11.1.1. Interactions
      11.1.2. R Functions interaction.plot and interplot
      11.1.3. Interactions When There Are More than Two Levels
      11.2. Testing Hypotheses about Main Effects and Interactions
      11.2.1. R Function anova
      11.2.2. Inferences about Disordinal Interactions
      11.2.3. Two-Way ANOVA Model
      11.3. Heteroscedastic Methods for Trimmed Means, Including Means
      11.3.1. R Function t2way
      11.4. Bootstrap Methods
      11.4.1. R Functions pbad2way and t2waybt
      11.5. Testing Hypotheses Based on Medians
      11.5.1. R Function m2way
      11.6. Rank-Based Method for a Two-Way Design
      11.6.1. R Function bdm2way
      11.6.2. Patel
      -Hoel Approach to Interactions
      11.7. Three-Way ANOVA
      11.7.1. R Functions anova and t3way
      11.8. Exercises
      12. Comparing More Than Two Dependent Groups
      12.1. Comparing Means in a One-Way Design
      12.1.1. R Function aov
      12.2. Comparing Trimmed Means When Dealing with a One-Way Design
      12.2.1. R Functions rmanova and rmdat2mat
      12.2.2. Bootstrap-t Method for Trimmed Means
      12.2.3. R Function rmanovab
      12.3. Percentile Bootstrap Methods for a One-Way Design
      12.3.1. Method Based on Marginal Measures of Location
      12.3.2. R Function bd1way
      12.3.3. Inferences Based on Difference Scores
      12.3.4. R Function rmdzero
      12.4. Rank-Based Methods for a One-Way Design
      12.4.1. Friedman's Test
      12.4.2. R Function friedman.test
      12.4.3. Method BPRM
      12.4.4. R Function bprm
      12.5. Comments on Which Method to Use
      12.6. Between-by-Within Designs
      12.6.1. Method for Trimmed Means
      12.6.2. R Function bwtrim and bw2list
      12.6.3. Bootstrap-t Method
      12.6.4. R Function tsplitbt
      12.6.5. Inferences Based on M-estimators and Other Robust Measures of Location
      12.6.6. R Functions sppba, sppbb, and sppbi
      12.6.7. Rank-Based Test
      12.6.8. R Function bwrank
      12.7. Within-by-Within Design
      12.7.1. R Function wwtrim
      12.8. Three-Way Designs
      12.8.1. R Functions bbwtrim, bwwtrim, and wwwtrim
      12.8.2. Data Management: R Functions bw2list and bbw2list
      12.9. Exercises
      13. Multiple Comparisons
      13.1. One-Way ANOVA, Independent Groups
      13.1.1. Fisher's Least Significant Difference Method
      13.1.2. Tukey
      -Kramer Method
      13.1.3. R Function TukeyHSD
      13.1.4. Tukey
      -Kramer and the ANOVA F Test
      13.1.5. Step-Down Method
      13.1.6. Dunnett's T3
      13.1.7. Games
      -Howell Method
      13.1.8. Comparing Trimmed Means
      13.1.9. R Function lincon
      13.1.10. Alternative Methods for Controlling FWE
      13.1.11. Percentile Bootstrap Methods for Comparing Trimmed Means, Medians, and M-estimators
      13.1.12. R Functions medpb, tmcppb, pbmcp, and mcppb20
      13.1.13. Bootstrap-t Method
      13.1.14. R Function linconb
      13.1.15. Rank-Based Methods
      13.1.16. R Functions cidmul, cidmulv2, and bmpmul
      13.2. Two-Way, between-by-between Design
      13.2.1. Scheffe's Homoscedastic Method
      13.2.2. Heteroscedastic Methods
      13.2.3. Extension of Welch
      -Sidak and Kaiser
      -Bowden Methods to Trimmed Means
      13.2.4. R Function kbcon
      13.2.5. R Function con2way
      13.2.6. Linear Contrasts Based on Medians
      13.2.7. R Functions msmed and mcp2med
      13.2.8. Bootstrap Methods
      13.2.9. R Functions linconb, mcp2a, and bbmcppb
      13.2.10. Patel
      -Hoel Rank-Based Interaction Method
      13.2.11. R Function rimul
      13.3. Judging Sample Sizes
      13.3.1. Tamhane's Procedure
      13.3.2. R Function tamhane
      13.3.3. Hochberg's Procedure
      13.3.4. R Function hochberg
      13.4. Methods for Dependent Groups
      13.4.1. Linear Contrasts Based on Trimmed Means
      13.4.2. R Function rmmcp
      13.4.3. Comparing M-estimators
      13.4.4. R Functions rmmcppb, dmedpb, and dtrimpb
      13.4.5. Bootstrap-t Method
      13.4.6. R Function bptd
      13.5. Between-by-within Designs
      13.5.1. R Functions bwmcp, bwamcp, bwbmcp, bwimcp, spmcpa, spmcpb, spmcpi, and bwmcppb
      13.6. Within-by-within Designs
      13.6.1. Three-Way Designs
      13.6.2. R Functions con3way, mcp3atm, and rm3mcp
      13.6.3. Bootstrap Methods for Three-Way Designs
      13.6.4. R Functions bbwmcp, bwwmcp, bbbmcppb, bbwmcppb, bwwmcppb, and wwwmcppb
      13.7. Exercises
      14. Some Multivariate Methods
      14.1. Location, Scatter, and Detecting Outliers
      14.1.1. Detecting Outliers via Robust Measures of Location and Scatter
      14.1.2. R Functions cov.mve and com.mcd
      14.1.3. More Measures of Location and Covariance
      14.1.4. R Functions rmba, tbs, and ogk
      14.1.5. R Function out
      14.1.6. Projection-Type Outlier Detection Method
      14.1.7. R Functions outpro, outproMC, outproad, outproadMC, and out3d
      14.1.8. Skipped Estimators of Location
      14.1.9. R Functions smean
      14.2. One-Sample Hypothesis Testing
      14.2.1. Comparing Dependent Groups
      14.2.2. R Functions smeancrv2, hotel1, and rmdzeroOP
      14.3. Two-Sample Case
      14.3.1. R Functions smean2, mat2grp, and matsplit
      14.4. MANOVA
      14.4.1. R Function manova
      14.4.2. Robust MANOVA Based on Trimmed Means
      14.4.3. R Functions MULtr.anova and MULAOVp
      14.4.4. Multivariate Extension of the Wilcoxon
      -Mann
      -Whitney Test
      14.4.5. Explanatory Measure of Effect Size: A Projection-Type Generalization
      14.4.6. R Function mulwmwv2
      14.5. Rank-Based Multivariate Methods
      14.5.1. Munzel
      -Brunner Method
      14.5.2. R Function mulrank
      14.5.3. Choi
      -Marden Multivariate Rank Test
      14.5.4. R Function cmanova
      14.6. Multivariate Regression
      14.6.1. Multivariate Regression Using R
      14.6.2. Robust Multivariate Regression
      14.6.3. R Function mlrreg and mopreg
      14.7. Principal Components
      14.7.1. R Functions prcomp and regpca
      14.7.2. Robust Principal Components
      14.7.3. R Functions outpca, robpca, robpcaS, Ppca, and Ppca.summary
      14.8. Exercises
      15. Robust Regression And Measures Of Association
      15.1. Robust Regression Estimators
      15.1.1. Theil
      -Sen Estimator
      15.1.2. R Functions tsreg and regplot
      15.1.3. Least Median of Squares
      15.1.4. Least Trimmed Squares and Least Trimmed Absolute Value Estimators
      15.1.5. R Functions lmsreg, ltsreg, and ltareg
      15.1.6. M-Estimators
      15.1.7. R Function chreg
      15.1.8. Deepest Regression Line
      15.1.9. R Function mdepreg
      15.1.10. Skipped Estimators
      15.1.11. R Functions opreg and opregMC
      15.1.12. S-estimators and an E-Type Estimator
      15.1.13. R Function tsts
      Contents note continued: 15.2. Comments on Choosing a Regression Estimator
      15.3. Testing Hypotheses When Using Robust Regression Estimators
      15.3.1. R Functions regtest, regtestMC, regci, and regciMC
      15.3.2. Comparing Measures of Location via Dummy Coding
      15.4. Dealing with Curvature: Smoothers
      15.4.1. Cleveland's Smoother
      15.4.2. R Functions lowess and lplot
      15.4.3. Smoothers Based on Robust Measures of Location
      15.4.4. R Functions rplot and rplotsm
      15.4.5. More Smoothers
      15.4.6. R Functions kerreg, runpd, and qsmcobs
      15.4.7. Prediction When X Is Discrete: The R Function rundis
      15.4.8. Seeing Curvature with More than Two Predictors
      15.4.9. R Function prplot
      15.4.10. Some Alternative Methods
      15.5. Some Robust Correlations and Tests of Independence
      15.5.1. Kendall's tau
      15.5.2. Spearman's rho
      15.5.3. Winsorized Correlation
      15.5.4. R Function wincor
      15.5.5. OP Correlation
      15.5.6. R Function scor
      15.5.7. Inferences about Robust Correlations: Dealing with Heteroscedasticity
      15.5.8. R Function corb
      15.6. Measuring the Strength of an Association Based on a Robust Fit
      15.7. Comparing the Slopes of Two Independent Groups
      15.7.1. R Functions reg2ci, runmean2g, and l2plot
      15.8. Tests for Linearity
      15.8.1. R Functions lintest, lintestMC, and linchk
      15.9. Identifying the Best Predictors
      15.9.1. R Functions regpord, ts2str, and sm2strv7
      15.10. Detecting Interactions and Moderator Analysis
      15.10.1. R Functions adtest
      15.10.2. Graphical Methods for Assessing Interactions
      15.10.3. R Functions kercon, runsm2g, regi, ols.plot.inter, and reg.plot.inter
      15.11. ANCOVA
      15.11.1. Classic ANCOVA
      15.11.2. Some Modern ANCOVA Methods
      15.11.3. R Functions ancsm, Qancsm, ancova, ancpb, ancbbpb, and ancboot
      15.12. Exercises
      16. Basic Methods For Analyzing Categorical Data
      16.1. Goodness of Fit
      16.1.1. R Functions chisq.test and pwr.chisq.test
      16.2. Test of Independence
      16.2.1. R Function chi.test.ind
      16.3. Detecting Differences in the Marginal Probabilities
      6.3.1. R Functions contab and mcnemar.test
      16.4. Measures of Association
      16.4.1. Proportion of Agreement
      16.4.2. Kappa
      16.4.3. Weighted Kappa
      16.4.4. R Function Ckappa
      16.5. Logistic Regression
      16.5.1. R Functions glm and logreg
      16.5.2. Confidence Interval for the Odds Ratio
      16.5.3. R Function ODDSR.CI
      16.5.4. Smoothers for Logistic Regression
      16.5.5. R Functions logrsm, rplot.bin, and logSM
      16.6. Exercises.
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