OUTLINE FOR CHAPTER
16
Bivariate, Multiple, and Logistic
Regression
 Introduction
 Different kinds of regression
 Three important differences between regression and correlation
 The "focus"
 Labels for the variables
 Inferential tests and confidence intervals
 Bivariate Regression
 Purpose and data
 Scatter diagrams, regression lines, and regression equations
 Interpreting a, b, r, and r2
in bivariate regression
 Inferential tests in bivariate regression
 Multiple Regression
 Introduction
 The popularity of multiple regression
 Similarities/differences between multiple regression and bivariate
regression
 The regression equation
 Its basic form
 Standardized regression equations and beta weights
 Dummy variables
 Three kinds of multiple regression
 Simultaneous
 Stepwise
 Hierarchical
 R, R2, DR2,
and adjusted R2 in multiple regression
 Inferential tests in multiple regression
 Logistic Regression
 Introduction
 The origin and growing popularity of logistic regression
 Similarities/differences between logistic regression and multiple
regression
 Variables
 Objectives of a logistic regression
 Odds, odds ratios, and adjusted odds ratios
 Tests of significance
 Wald test
 Confidence intervals
 "Modeling the data"
 Final Comments
 Multicollinearity
 The limitations of "control"
 Statistical significance vs. practical significance
 Regression and causal connections
