Logistic Analysis

Examine Relationships between a Categorical Y and a Continuous X Variable

Use the Logistic platform to fit a logistic regression model to a categorical response with a continuous x predictor. Options include a ROC curve, lift curve, and odds ratio estimates. The fitted model estimates probabilities for each x value. The Logistic platform is the nominal or ordinal by continuous personality of the Fit Y by X platform. There is a distinction between nominal and ordinal responses on this platform:

• Nominal logistic regression estimates a set of curves to partition the probability among the responses.

• Ordinal logistic regression models the probability of being less than or equal to a given response. This has the effect of estimating a single logistic curve, which is shifted horizontally to produce probabilities for the ordered categories. This model is less complex and is recommended for ordered responses.

Figure 8.1 Examples of Logistic Regression 

Contents

Overview of Logistic Regression

Nominal Logistic Regression

Ordinal Logistic Regression

Example of Nominal Logistic Regression

Launch the Logistic Platform

Data Structure

The Logistic Report

Logistic Plot

Iterations

Whole Model Test

Fit Details

Parameter Estimates

Logistic Platform Options

ROC Curves

Save Probability Formula

Inverse Prediction

Additional Examples of Logistic Regression

Example of Ordinal Logistic Regression

Additional Example of a Logistic Plot

Example of ROC Curves

Example of Inverse Prediction Using the Crosshair Tool

Example of Inverse Prediction Using the Inverse Prediction Option

Statistical Details for the Logistic Platform