Applied regression analysis and generalized linear models

Combining a modern, data-analytic perspective with a focus on applications in the social sciences, the Third Edition of Applied Regression Analysis and Generalized Linear Models provides in-depth coverage of regression analysis, generalized linear models, and closely related methods, such as bootstrapping and missing data. Updated throughout, this Third Edition includes new chapters on mixed-effects models for hierarchical and longitudinal data. Although the text is largely accessible to readers with a modest background in statistics and mathematics, author John Fox also presents more advanced material in optional sections and chapters throughout the book. Accompanying website resources containing all answers to the end-of-chapter exercises. Answers to odd-numbered questions, as well as datasets and other student resources are available on the author′s website. NEW! Bonus chapter on Bayesian Estimation of Regression Models also available at the author′s website. APPLIED REGRESSION ANALYSIS AND GENERALIZED LINEAR MODELS-FRONT COVER......Page 1 APPLIED REGRESSION ANALYSIS AND GENERALIZED LINEAR MODELS......Page 2 COPYRIGHT......Page 5 BRIEF CONTENTS......Page 6 CONTENTS......Page 8 PREFACE......Page 16 ABOUT THE AUTHOR......Page 25 CHAPTER 1- STATISTICAL MODELS AND SOCIAL SCIENCE......Page 26 PART I- DATA CRAFT......Page 37 CHAPTER 2- WHAT IS REGRESSION ANALYSIS?......Page 38 CHAPTER 3- EXAMINING DATA......Page 53 CHAPTER 4- TRANSFORMING DATA......Page 80 PART II- LINEAR MODELS AND LEAST SQUARES......Page 106 CHAPTER 5- LINEAR LEAST-SQUARES REGRESSION......Page 107 CHAPTER 6- STATISTICAL INFERENCE FOR REGRESSION......Page 131 CHAPTER 7- DUMMY-VARIABLE REGRESSION......Page 153 CHAPTER 8- ANALYSIS OF VARIANCE......Page 178 CHAPTER 9- STATISTICAL THEORY FOR LINEAR MODELS......Page 227 CHAPTER 10- THE VECTOR GEOMETRY OF LINEAR MODELS......Page 270 PART III- LINEAR-MODEL DIAGNOSTICS......Page 290 CHAPTER 11- UNUSUAL AND INFLUENTIAL DATA......Page 291 CHAPTER 12- DIAGNOSING NON-NORMALITY, NONCONSTANT ERROR VARIANCE, AND NONLINEARITY......Page 321 CHAPTER 13- COLLINEARITY AND ITS PURPORTED REMEDIES......Page 366 PART IV- GENERALIZED LINEAR MODELS......Page 394 CHAPTER 14- LOGIT AND PROBIT MODELS FOR CATEGORICAL RESPONSE VARIABLES......Page 395 CHAPTER 15- GENERALIZED LINEAR MODELS......Page 443 PART V- EXTENDING LINEAR AND GENERALIZED LINEAR MODELS......Page 498 CHAPTER 16- TIME-SERIES REGRESSION AND GENERALIZED LEAST SQUARES......Page 499 CHAPTER 17- NONLINEAR REGRESSION......Page 527 CHAPTER 18- NONPARAMETRIC REGRESSION......Page 553 CHAPTER 19- ROBUST REGRESSION......Page 611 CHAPTER 20- MISSING DATA IN REGRESSION MODELS......Page 630 CHAPTER 21- BOOTSTRAPPING REGRESSION MODELS......Page 672 CHAPTER 22- MODEL SELECTION, AVERAGING, AND VALIDATION......Page 694 PART VI- MIXED-EFFECTS MODELS......Page 724 CHAPTER 23- LINEAR MIXED-EFFECTS MODELS FOR HIERARCHICAL AND LONGITUDINAL DATA......Page 725 CHAPTER 24- GENERALIZED LINEAR AND NONLINEAR MIXED-EFFECTS MODELS......Page 768 APPENDIX A......Page 784 REFERENCES......Page 787 AUTHOR INDEX......Page 798 SUBJECT INDEX......Page 802 DATA SET INDEX......Page 816 Statistical Models Of Social Science -- I. Data Craft -- What Is Regression Analysis? -- Examining Data -- Transforming Data -- Ii. Linear Models And Least Squares -- Linear Least-squares Regression -- Statistical Inference For Regression -- Dummy-variable Regression -- Analysis Of Variance -- Statistical Theory For Linear Models -- The Vector Geometry Of Linear Models -- Iii. Linear-model Diagnostics -- Unusual And Influential Data -- Diagnosing Non-normality, Nonconstant Error Variance, And Nonlinearity -- Collinearity And Its Purported Remedies -- Iv. Generalities Linear Models -- Logit And Probit Models For Categorical Response Variables -- Generalized Linear Models -- V. Extending Linear And Generalized Linear Models -- Time-series Regression And Generalized Least Squares -- Nonlinear Regression -- Nonparametric Regression -- Robust Regression -- Missing Data In Regression Models -- Bootstrapping Regression Models -- Model Selection, Averaging, And Validation -- Vi. Mixed-effects Models -- Linear Mixed-effects Models For Hierarchical And Longitudinal Data -- Generalized Linear And Non Linear Mixed-effects Models. John Fox. Revised Edition Of: Applied Regression Analysis, Linear Models, And Related Methods, C1997. Includes Bibliographical References (pages 762-772) And Indexes. "Combining a modern, data-analytic perspective with a focus on applications in the social sciences, the Third Edition of 'Applied Regression Analysis and Generalized Linear Models' provides in-depth coverage of regression analysis, generalized linear models, and closely related methods, such as bootstrapping and missing data. Updated throughout, this Third Edition includes new chapters on mixed-effects models for hierarchical and longitudinal data. Although the text is largely accessible to readers with a modest background in statistics and mathematics, author John Fox also presents more advanced material in optional sections and chapters throughout the book."--taken from publisher website The strength of this text is the unified presentation of several regression topics that provides the student with a global perspective on regression analysis. The student is well served with this unified approach as it facilitates deeper research on any one topic with more advanced texts Providing a modern treatment of regression analysis, linear models and closely related methods, this book introduces students to one of the most useful and widely used statistical tools for social research.