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COMMENTS ON THE USE OF INDICATOR VARIABLES 8.3 REGRESSION APPROACH TO ANALYSIS OF VARIANCE PROBLEMS

      14  CHAPTER 9: MULTICOLLINEARITY 9.1 INTRODUCTION 9.2 SOURCES OF MULTICOLLINEARITY 9.3 EFFECTS OF MULTICOLLINEARITY 9.4 MULTICOLLINEARITY DIAGNOSTICS 9.5 METHODS FOR DEALING WITH MULTICOLLINEARITY 9.6 USING SAS TO PERFORM RIDGE AND PRINCIPAL-COMPONENT REGRESSION PROBLEMS

      15  CHAPTER 10: VARIABLE SELECTION AND MODEL BUILDING 10.1 INTRODUCTION 10.2 COMPUTATIONAL TECHNIQUES FOR VARIABLE SELECTION 10.3 STRATEGY FOR VARIABLE SELECTION AND MODEL BUILDING 10.4 CASE STUDY: GORMAN AND TOMAN ASPHALT DATA USING SAS PROBLEMS

      16  CHAPTER 11: VALIDATION OF REGRESSION MODELS 11.1 INTRODUCTION 11.2 VALIDATION TECHNIQUES 11.3 DATA FROM PLANNED EXPERIMENTS PROBLEMS

      17  CHAPTER 12: INTRODUCTION TO NONLINEAR REGRESSION 12.1 LINEAR AND NONLINEAR REGRESSION MODELS 12.2 ORIGINS OF NONLINEAR MODELS 12.3 NONLINEAR LEAST SQUARES 12.4 TRANFORMATION TO A LINEAR MODEL 12.5 PARAMETER ESTIMATION IN A NONLINEAR SYSTEM 12.6 STATISTICAL INFERENCE IN NONLINEAR REGRESSION 12.7 EXAMPLES OF NONLINEAR REGRESSION MODELS 12.8 USING SAS AND R PROBLEMS

      18  CHAPTER 13: GENERALIZED LINEAR MODELS 13.1 INTRODUCTION 13.2 LOGISTIC REGRESSION MODELS 13.3 POISSON REGRESSION 13.4 THE GENERALIZED LINEAR MODEL PROBLEMS

      19  CHAPTER 14: REGRESSION ANALYSIS OF TIME SERIES DATA 14.1 INTRODUCTION TO REGRESSION MODELS FOR TIME SERIES DATA 14.2 DETECTING AUTOCORRELATION: THE DURBIN–WATSON TEST 14.3 ESTIMATING THE PARAMETERS IN TIME SERIES REGRESSION MODELS PROBLEMS

      20  CHAPTER 15: OTHER TOPICS IN THE USE OF REGRESSION ANALYSIS 15.1 ROBUST REGRESSION 15.2 EFFECT OF MEASUREMENT ERRORS IN THE REGRESSORS 15.3 INVERSE ESTIMATION—THE CALIBRATION PROBLEM 15.4 BOOTSTRAPPING IN REGRESSION 15.5 CLASSIFICATION AND REGRESSION TREES (CART) 15.6 NEURAL NETWORKS 15.7 DESIGNED EXPERIMENTS FOR REGRESSION PROBLEMS

      21  APPENDIX A: STATISTICAL TABLES

      22  APPENDIX B: DATA SETS FOR EXERCISES

      23  APPENDIX C: SUPPLEMENTAL TECHNICAL MATERIAL C.1 BACKGROUND ON BASIC TEST STATISTICS C.2 BACKGROUND FROM THE THEORY OF LINEAR MODELS C.3 IMPORTANT RESULTS ON SS_R AND SS_RES C.4 GAUSS–MARKOV THEOREM, VAR(ε) = σ²I C.5 COMPUTATIONAL ASPECTS OF MULTIPLE REGRESSION C.6 RESULT ON THE INVERSE OF A MATRIX C.7 DEVELOPMENT OF THE PRESS STATISTIC C.8 DEVELOPMENT OF C.9 OUTLIER TEST BASED ON R-STUDENT C.10 INDEPENDENCE OF RESIDUALS AND FITTED VALUES C.11 GAUSS-MARKOV THEOREM, VAR(ε) = V C.12 BIAS IN MS_RES WHEN THE MODEL IS UNDERSPECIFIED C.13 COMPUTATION OF INFLUENCE DIAGNOSTICS C.14 GENERALIZED LINEAR MODELS

      24  APPENDIX D: INTRODUCTION TO SAS D.1 BASIC DATA ENTRY D.2 CREATING PERMANENT SAS DATA SETS Скачать книгу