OUTLINE FOR CHAPTER 16

Bivariate, Multiple, and Logistic Regression

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

 

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Schuyler W. Huck
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