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EXAMPLE: CAN OFFSPRING HEIGHT BE PREDICTED? 7.4 THEORY OF REGRESSION ANALYSIS: A DEEPER LOOK 7.5 MULTILEVEL YEARNINGS 7.6 THE LEAST‐SQUARES LINE 7.7 MAKING PREDICTIONS WITHOUT REGRESSION 7.8 MORE ABOUT ε_i 7.9 MODEL ASSUMPTIONS FOR LINEAR REGRESSION 7.10 ESTIMATION OF MODEL PARAMETERS IN REGRESSION 7.11 NULL HYPOTHESES FOR REGRESSION 7.12 SIGNIFICANCE TESTS AND CONFIDENCE INTERVALS FOR MODEL PARAMETERS 7.13 OTHER FORMULATIONS OF THE REGRESSION MODEL 7.14 THE REGRESSION MODEL IN MATRICES: ALLOWING FOR MORE COMPLEX MULTIVARIABLE MODELS 7.15 ORDINARY LEAST‐SQUARES IN MATRICES 7.16 ANALYSIS OF VARIANCE FOR REGRESSION 7.17 MEASURES OF MODEL FIT FOR REGRESSION: HOW WELL DOES THE LINEAR EQUATION FIT? 7.18 ADJUSTED R² 7.19 WHAT “EXPLAINED VARIANCE” MEANS AND MORE IMPORTANTLY, WHAT IT DOES NOT MEAN 7.20 VALUES FIT BY REGRESSION 7.21 LEAST‐SQUARES REGRESSION IN R: USING MATRIX OPERATIONS 7.22 LINEAR REGRESSION USING R 7.23 REGRESSION DIAGNOSTICS: A CHECK ON MODEL ASSUMPTIONS 7.24 REGRESSION IN SPSS: PREDICTING QUANTITATIVE FROM VERBAL 7.25 POWER ANALYSIS FOR LINEAR REGRESSION IN R 7.26 CHAPTER SUMMARY AND HIGHLIGHTS REVIEW EXERCISES Further Discussion and Activities

      14  8 MULTIPLE LINEAR REGRESSION 8.1 THEORY OF PARTIAL CORRELATION 8.2 SEMIPARTIAL CORRELATIONS 8.3 MULTIPLE REGRESSION 8.4 SOME PERSPECTIVE ON REGRESSION COEFFICIENTS: “EXPERIMENTAL COEFFICIENTS”? 8.5 MULTIPLE REGRESSION MODEL IN MATRICES 8.6 ESTIMATION OF PARAMETERS 8.7 CONCEPTUALIZING MULTIPLE R 8.8 INTERPRETING REGRESSION COEFFICIENTS: CORRELATED VERSUS UNCORRELATED PREDICTORS 8.9 ANDERSON’S IRIS DATA: PREDICTING SEPAL LENGTH FROM PETAL LENGTH AND PETAL WIDTH 8.10 FITTING OTHER FUNCTIONAL FORMS: A BRIEF LOOK AT POLYNOMIAL REGRESSION 8.11 MEASURES OF COLLINEARITY IN REGRESSION: VARIANCE INFLATION FACTOR AND TOLERANCE 8.12 R‐SQUARED AS A FUNCTION OF PARTIAL AND SEMIPARTIAL CORRELATIONS: THE STEPPING STONES TO FORWARD AND STEPWISE REGRESSION 8.13 MODEL‐BUILDING STRATEGIES: SIMULTANEOUS, HIERARCHICAL, FORWARD, STEPWISE 8.14 POWER ANALYSIS FOR MULTIPLE REGRESSION 8.15 INTRODUCTION TO STATISTICAL MEDIATION: CONCEPTS AND CONTROVERSY 8.16 BRIEF SURVEY OF RIDGE AND LASSO REGRESSION: PENALIZED REGRESSION MODELS AND THE CONCEPT OF SHRINKAGE 8.17 CHAPTER SUMMARY AND HIGHLIGHTS Review Exercises Further Discussion and Activities

      15  9 INTERACTIONS IN MULTIPLE LINEAR REGRESSION 9.1 THE ADDITIVE REGRESSION MODEL WITH TWO PREDICTORS 9.2 WHY THE INTERACTION IS THE PRODUCT TERM x_iz_i: DRAWING AN ANALOGY TO FACTORIAL ANOVA 9.3 A MOTIVATING EXAMPLE OF INTERACTION IN REGRESSION: CROSSING A CONTINUOUS PREDICTOR WITH A DICHOTOMOUS PREDICTOR 9.4 ANALYSIS OF COVARIANCE 9.5 CONTINUOUS MODERATORS 9.6 SUMMING UP THE IDEA OF INTERACTIONS IN REGRESSION 9.7 DO MODERATORS REALLY “MODERATE” ANYTHING? 9.8 INTERPRETING MODEL COEFFICIENTS IN THE CONTEXT OF MODERATORS 9.9 MEAN‐CENTERING PREDICTORS: IMPROVING THE INTERPRETABILITY OF SIMPLE SLOPES 9.10 MULTILEVEL REGRESSION: ANOTHER SPECIAL CASE OF THE MIXED MODEL 9.11 CHAPTER SUMMARY AND HIGHLIGHTS REVIEW EXERCISES

      16  10 LOGISTIC REGRESSION AND THE GENERALIZED LINEAR MODEL 10.1 NONLINEAR MODELS 10.2 GENERALIZED LINEAR MODELS 10.3 CANONICAL LINKS 10.4 DISTRIBUTIONS AND GENERALIZED LINEAR MODELS 10.5 DISPERSION PARAMETERS AND DEVIANCE 10.6 LOGISTIC REGRESSION

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