Chapter 2

Binary Logistic Regression with PROC LOGISTIC: Basics

2.1   Introduction

2.2   Dichotomous Dependent Variables: Example

2.3   Problems with Ordinary Linear Regression

2.4   Odds and Odds Ratios

2.5   The Logistic Regression Model

2.6   Estimation of the Logistic Model: General Principles

2.7   Maximum Likelihood Estimation with PROC LOGISTIC

2.8   Interpreting Coefficients

2.9   CLASS Variables

2.10 Multiplicative Terms in the MODEL Statement

 

2.1 Introduction

A great many variables in the social sciences are dichotomous—employed vs. unemployed, married vs. unmarried, guilty vs. not guilty, voted vs. didn't vote. It's hardly surprising, then, that social scientists frequently want to estimate regression models in which the dependent ...

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