Data Analysis : A Model Comparison Approach to Regression, ANOVA, and Beyond (Paperback, 4 ed)

Section A: Statistical Machinery? 1. Introduction to Data Analysis 2. Simple Models: Definitions of Error and Parameter Estimates 3. Simple Models: Models of Error and Sampling Distributions 4. Simple Models: Statistical Inferences about Parameter Estimates 5. Statistical Power: Power, Effect Sizes, and Confidence Intervals? Section B: Increasingly Complex Models? 6. Simple Regression: Models with a Single Continuous Predictor 7. Multiple Regression: Models with Multiple Continuous Predictors 8. Moderated and Nonlinear Multiple Regression models 9. One-Way ANOVA: Models with a Single Categorical Predictor 10. Factorial ANOVA: Models with Multiple Categorical Predictors and Product Terms 11. ANCOVA: Models with Continuous and Categorical Predictors? Section C: Violations of Assumptions About Error? 12. Repeated-Measures ANOVA: Models with Nonindependent Errors 13. Incorporating Continuous Predictors with Nonindependent Data: Towards Mixed Models 14. Outliers and Ill-Mannered Error 15. Logistic Regression: Dependent Categorical Variables