Example of Predict for Binary Logistic

A financial analyst investigates the factors that are associated with the probability that a college student has certain credit cards. The analyst randomly samples college students for a survey. The survey asks the students questions about their education and finances.

After fitting the model, the analyst estimates the probability that a student who has \$75 cash and an annual income of \$10,000 has an American Express credit card.

1. Open the sample data, CreditSurvey.MTW.
2. Open the Binary Logistic Regression dialog box.
• Mac: Statistics > Regression > Binary Logistic Regression
• PC: STATISTICS > Binary Logistic > Binary Logistic Regression
3. From the drop-down list, select Response in binary response/frequency format.
4. In Response, enter American Express.
5. In Continuous predictors, enter Cash Annual Income.
6. Click OK.
7. Open the Predict for Binary Logistic dialog box.
• Mac: Statistics > Regression > Predict for Binary Logistic
• PC: STATISTICS > Binary Logistic > Predict for Binary Logistic
8. From Response, select American Express.
9. In the table, enter 75 for Cash and 10000 for Annual Income.
10. Click OK.

Interpret the results

Minitab uses the stored model to estimate that the probability is 0.3998. The confidence interval indicates that the analyst can be 95% confident that the probability falls within the range of approximately 0.07 to 0.85. This wide range indicates that the model does not produce precise predictions.

 Regression Equation
 P(1) = exp(Y')/(1 + exp(Y')) Y' = −1.889 + 0.01782 Cash + 0.00001462 Annual Income
 Settings
 Variable Setting Cash 75 Annual Income 10000
 Prediction
 Fit SE Fit 95% CI 0.3998 0.2657 (0.0707, 0.8537)
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