# Example of Predict with a binary logistic regression model

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. Choose Stat > Regression > Binary Logistic Regression > Predict.
3. From Response, select American Express.
4. In the table, enter 75 for Cash and 10000 for Annual Income.
5. Click OK.

## Interpret the results

Minitab uses the stored model to estimate that the probability is 0.998870. The prediction interval indicates that the analyst can be 95% confident that the probability falls within the range of 0.0516175 to 1.00000. This wide range indicates that the model does not produce precise predictions.

### Prediction for American Express

Regression Equation P(1) = exp(Y')/(1 + exp(Y')) Y' = -7.71 + 0.1688 Cash + 0.000108 Annual Income + 0.000540 Cash*Cash - 0.000003 Cash*Annual Income
Settings Variable Setting Cash 75 Annual Income 10000
Prediction Fitted Probability SE Fit 95% CI 0.998870 0.0055832 (0.0516267, 1.00000)
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