Table of Contents
1 COVER
4 PREFACE
6 CHAPTER 1: INTRODUCTION 1.1 REGRESSION AND MODEL BUILDING 1.2 DATA COLLECTION 1.3 USES OF REGRESSION 1.4 ROLE OF THE COMPUTER
7 CHAPTER 2: SIMPLE LINEAR REGRESSION 2.1 SIMPLE LINEAR REGRESSION MODEL 2.2 LEAST-SQUARES ESTIMATION OF THE PARAMETERS 2.3 HYPOTHESIS TESTING ON THE SLOPE AND INTERCEPT 2.4 INTERVAL ESTIMATION IN SIMPLE LINEAR REGRESSION 2.5 PREDICTION OF NEW OBSERVATIONS 2.6 COEFFICIENT OF DETERMINATION 2.7 A SERVICE INDUSTRY APPLICATION OF REGRESSION 2.8 DOES PITCHING WIN BASEBALL GAMES? 2.9 USING SAS® AND R FOR SIMPLE LINEAR REGRESSION 2.10 SOME CONSIDERATIONS IN THE USE OF REGRESSION 2.11 REGRESSION THROUGH THE ORIGIN 2.12 ESTIMATION BY MAXIMUM LIKELIHOOD 2.13 CASE WHERE THE REGRESSOR x IS RANDOM PROBLEMS
8 CHAPTER 3: MULTIPLE LINEAR REGRESSION 3.1 MULTIPLE REGRESSION MODELS 3.2 ESTIMATION OF THE MODEL PARAMETERS 3.3 HYPOTHESIS TESTING IN MULTIPLE LINEAR REGRESSION 3.4 CONFIDENCE INTERVALS IN MULTIPLE REGRESSION 3.5 PREDICTION OF NEW OBSERVATIONS 3.6 A MULTIPLE REGRESSION MODEL FOR THE PATIENT SATISFACTION DATA 3.7 DOES PITCHING AND DEFENSE WIN BASEBALL GAMES? 3.8 USING SAS AND R FOR BASIC MULTIPLE LINEAR REGRESSION 3.9 HIDDEN EXTRAPOLATION IN MULTIPLE REGRESSION 3.10 STANDARDIZED REGRESSION COEFFICIENTS 3.11 MULTICOLLINEARITY 3.12 WHY DO REGRESSION COEFFICIENTS HAVE THE WRONG SIGN? PROBLEMS
9 CHAPTER 4: MODEL ADEQUACY CHECKING 4.1 INTRODUCTION 4.2 RESIDUAL ANALYSIS 4.3 PRESS STATISTIC 4.4 DETECTION AND TREATMENT OF OUTLIERS 4.5 LACK OF FIT OF THE REGRESSION MODEL PROBLEMS
10 CHAPTER 5: TRANSFORMATIONS AND WEIGHTING TO CORRECT MODEL INADEQUACIES 5.1 INTRODUCTION 5.2 VARIANCE-STABILIZING TRANSFORMATIONS 5.3 TRANSFORMATIONS TO LINEARIZE THE MODEL 5.4 ANALYTICAL METHODS FOR SELECTING A TRANSFORMATION 5.5 GENERALIZED AND WEIGHTED LEAST SQUARES 5.6 REGRESSION MODELS WITH RANDOM EFFECTS PROBLEMS
11 CHAPTER 6: DIAGNOSTICS FOR LEVERAGE AND INFLUENCE 6.1 IMPORTANCE OF DETECTING INFLUENTIAL OBSERVATIONS 6.2 LEVERAGE 6.3 MEASURES OF INFLUENCE: COOK’S D 6.4 MEASURES OF INFLUENCE: DFFITS AND DFBETAS 6.5 A MEASURE OF MODEL PERFORMANCE 6.6 DETECTING GROUPS OF INFLUENTIAL OBSERVATIONS 6.7 TREATMENT OF INFLUENTIAL OBSERVATIONS PROBLEMS
12 CHAPTER 7: POLYNOMIAL REGRESSION MODELS 7.1 INTRODUCTION 7.2 POLYNOMIAL MODELS IN ONE VARIABLE 7.3 NONPARAMETRIC REGRESSION 7.4 POLYNOMIAL MODELS IN TWO OR MORE VARIABLES 7.5 ORTHOGONAL POLYNOMIALS
