Applied Regression Analysis, Linear Models, and Related Methods

Abstract

PART ONE: PRELIMINARIES Statistics and Social Science What Is Regression Analysis? Examining Data Transforming Data PART TWO: 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 PART THREE: LINEAR-MODEL DIAGNOSTICS Unusual and Influential Data Diagnosing Nonlinearity, Nonconstant Error Variance, and Nonnormality Collinearity and Its Purported Remedies PART FOUR: BEYOND LINEAR LEAST SQUARES Extending Linear Least Squares Time Series, Nonlinear, Robust, and Nonparametric Regression Logit and Probit Models Assessing Sampling Variation Bootstrapping and Cross-Validation