Handbook of Regression Analysis

pt. I The Multiple Linear Regression Model 1.Multiple Linear Regression 1.1.Introduction 1.2.Concepts and Background Material 1.2.1.The Linear Regression Model 1.2.2.Estimation Using Least Squares 1.2.3.Assumptions 1.3.Methodology 1.3.1.Interpreting Regression Coefficients 1.3.2.Measuring the Strength of the Regression Relationship 1.3.3.Hypothesis Tests and Confidence Intervals for ? 1.3.4.Fitted Values and Predictions 1.3.5.Checking Assumptions Using Residual Plots 1.4.Example -Estimating Home Prices 1.5.Summary 2.Model Building 2.1.Introduction 2.2.Concepts and Background Material 2.2.1.Using Hypothesis Tests to Compare Models 2.2.2.Collinearity 2.3.Methodology 2.3.1.Model Selection 2.3.2.Example -Estimating Home Prices (continued) 2.4.Indicator Variables and Modeling Interactions 2.4.1.Example -Electronic Voting and the 2004 Presidential Election 2.5.Summary Contents note continued: pt. II Addressing Violations of Assumptions 3.Diagnostics for Unusual Observations 3.1.Introduction 3.2.Concepts and Background Material 3.3.Methodology 3.3.1.Residuals and Outliers 3.3.2.Leverage Points 3.3.3.Influential Points and Cook's Distance 3.4.Example Estimating Home Prices (continued) 3.5.Summary 4.Transformations and Linearizable Models 4.1.Introduction 4.2.Concepts and Background Material: The Log-Log Model 4.3.Concepts and Background Material: Semilog Models 4.3.1.Logged Response Variable 4.3.2.Logged Predictor Variable 4.4.Example Predicting Movie Grosses After One Week 4.5.Summary 5.Time Series Data and Autocorrelation 5.1.Introduction 5.2.Concepts and Background Material 5.3.Methodology: Identifying Autocorrelation 5.3.1.The Durbin-Watson Statistic 5.3.2.The Autocorrelation Function (ACF) 5.3.3.Residual Plots and the Runs Test Contents note continued: 5.4.Methodology: Addressing Autocorrelation 5.4.1.Detrending and Deseasonalizing 5.4.2.Example e-Commerce Retail Sales 5.4.3.Lagging and Differencing 5.4.4.Example Stock Indexes 5.4.5.Generalized Least Squares (GLS): The Cochrane-Orcutt Procedure 5.4.6.Example Time Intervals Between Old Faithful Eruptions 5.5.Summary pt. III Categorical Predictors 6.Analysis of Variance 6.1.Introduction 6.2.Concepts and Background Material 6.2.1.One-Way ANOVA 6.2.2.Two-Way ANOVA 6.3.Methodology 6.3.1.Codings for Categorical Predictors 6.3.2.Multiple Comparisons 6.3.3.Levene's Test and Weighted Least Squares 6.3.4.Membership in Multiple Groups 6.4.Example DVD Sales of Movies 6.5.Higher-Way ANOVA 6.6.Summary 7.Analysis of Covariance 7.1.Introduction 7.2.Methodology 7.2.1.Constant Shift Models 7.2.2.Varying Slope Models 7.3.Example International Grosses of Movies 7.4.Summary Contents note continued: pt. IV OTHER REGRESSION MODELS 8.Logistic Regression 8.1.Introduction 8.2.Concepts and Background Material 8.2.1.The Logit Response Function 8.2.2.Bernoulli and Binomial Random Variables 8.2.3.Prospective and Retrospective Designs 8.3.Methodology 8.3.1.Maximum Likelihood Estimation 8.3.2.Inference, Model Comparison, and Model Selection 8.3.3.Goodness-of-Fit 8.3.4.Measures of Association and Classification Accuracy 8.3.5.Diagnostics 8.4.Example Smoking and Mortality 8.5.Example Modeling Bankruptcy 8.6.Summary 9.Multinomial Regression 9.1.Introduction 9.2.Concepts and Background Material 9.2.1.Nominal Response Variable 9.2.2.Ordinal Response Variable 9.3.Methodology 9.3.1.Estimation 9.3.2.Inference, Model Comparisons, and Strength of Fit 9.3.3.Lack of Fit and Violations of Assumptions 9.4.Example City Bond Ratings 9.5.Summary 10.Count Regression 10.1.Introduction Contents note continued: 10.2.Concepts and Background Material 10.2.1.The Poisson Random Variable 10.2.2.Generalized Linear Models 10.3.Methodology 10.3.1.Estimation and Inference 10.3.2.Offsets 10.4.Overdispersion and Negative Binomial Regression 10.4.1.Quasi-likelihood 10.4.2.Negative Binomial Regression 10.5.Example Unprovoked Shark Attacks in Florida 10.6.Other Count Regression Models 10.7.Poisson Regression and Weighted Least Squares 10.7.1.Example International Grosses of Movies (continued) Summary 11.Nonlinear Regression 11.1.Introduction 11.2.Concepts and Background Material 11.3.Methodology 11.3.1.Nonlinear Least Squares Estimation 11.3.2.Inference for Nonlinear Regression Models 11.4.Example Michaelis-Menten Enzyme Kinetics 11.5.Summary.