Bivariate, Multiple, and Logistic Regression

  1. Introduction
    1. Different kinds of regression
    2. Three important differences between regression and correlation
      1. The purpose
      2. Labels for the variables
      3. The focus of 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, adjusted R2, and sr2 in multiple regression
    5. Inferential tests in multiple regression
    6. Moderated and mediated 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. Nagelkerke's R2
      4. Hit rates, sentitivity, and specificity
  5. Final Comments
    1. Multicollinearity
    2. Model specification
    3. The limitations of "control"
    4. Statistical significance vs. practical significance
    5. The inflated Type I error rate


Copyright © 2012

Schuyler W. Huck
All rights reserved.

| Book Info | Author Info |

Site URL: www.readingstats.com

Top | Site Map
Site Design: John W. Taylor V