Applied Regression Analysis and Generalized Linear ModelsCombining 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. |
Contents
CHAPTER 1 STATISTICAL MODELS AND SOCIAL SCIENCE | 1 |
PART I DATA CRAFT | 12 |
CHAPTER 2 WHAT IS REGRESSION ANALYSIS? | 13 |
CHAPTER 3 EXAMINING DATA | 28 |
CHAPTER 4 TRANSFORMING DATA | 55 |
PART II LINEAR MODELS AND LEAST SQUARES | 81 |
CHAPTER 5 LINEAR LEASTSQUARES REGRESSION | 82 |
CHAPTER 6 STATISTICAL INFERENCE FOR REGRESSION | 106 |
CHAPTER 15 GENERALIZED LINEAR MODELS | 418 |
PART V EXTENDING LINEAR AND GENERALIZED LINEAR MODELS | 473 |
CHAPTER 16 TIMESERIES REGRESSION AND GENERALIZED LEAST SQUARES | 474 |
CHAPTER 17 NONLINEAR REGRESSION | 502 |
CHAPTER 18 NONPARAMETRIC REGRESSION | 528 |
CHAPTER 19 ROBUST REGRESSION | 586 |
CHAPTER 20 MISSING DATA IN REGRESSION MODELS | 605 |
CHAPTER 21 BOOTSTRAPPING REGRESSION MODELS | 647 |
CHAPTER 7 DUMMYVARIABLE REGRESSION | 128 |
CHAPTER 8 ANALYSIS OF VARIANCE | 153 |
CHAPTER 9 STATISTICAL THEORY FOR LINEAR MODELS | 202 |
CHAPTER 10 THE VECTOR GEOMETRY OF LINEAR MODELS | 245 |
PART III LINEARMODEL DIAGNOSTICS | 265 |
CHAPTER 11 UNUSUAL AND INFLUENTIAL DATA | 266 |
CHAPTER 12 DIAGNOSING NONNORMALITY NONCONSTANT ERROR VARIANCE AND NONLINEARITY | 296 |
CHAPTER 13 COLLINEARITY AND ITS PURPORTED REMEDIES | 341 |
PART IV GENERALIZED LINEAR MODELS | 369 |
CHAPTER 14 LOGIT AND PROBIT MODELS FOR CATEGORICAL RESPONSE VARIABLES | 370 |
CHAPTER 22 MODEL SELECTION AVERAGING AND VALIDATION | 669 |
PART VI MIXEDEFFECTS MODELS | 699 |
CHAPTER 23 LINEAR MIXEDEFFECTS MODELS FOR HIERARCHICAL AND LONGITUDINAL DATA | 700 |
CHAPTER 24 GENERALIZED LINEAR AND NONLINEAR MIXEDEFFECTS MODELS | 743 |
APPENDIX A | 759 |
| 762 | |
| 773 | |
| 777 | |
DATA SET INDEX | 791 |
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Common terms and phrases
¼ α ¼ μ analysis ANOVA model asymptotic autocorrelations average bootstrap Chapter collinearity components computed confidence intervals constant correlation covariance matrix data set degrees of freedom dummy regressors Duncan’s error variance example Exercise explanatory variables F-statistic F-test factor Figure fitted values function GLMs graph imputation income independent interaction intercept kernel least-squares estimator least-squares regression likelihood-ratio likelihood-ratio tests linear model linear regression local-polynomial log-likelihood loge logit model main effects maximum-likelihood estimator mean methods missing data mixed-effects models ML estimates model matrix nonlinear nonparametric regression normally distributed null hypothesis observations occupational prestige outliers parameters population random effects regression coefficients regression model represent residual sum response variable scatterplot Section selection simple regression slope span standard errors statistical models studentized residuals subspace sum of squares Table tion transformation vector weight X-values X1 and X2 Yi ¼ þÁÁÁþ