# 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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