If your regression model seems to take too long to execute, often you can reduce the amount of time by just rethinking the scope of the problem you are trying to run.

Typically regression models take a long time to run if you:

- Included
**Categorical predictors**with large number of values. In Q, one dummy variable is created from each unique value of a categorical variable. Check to see that the Variable Type for predictor variables like Income is Numeric, and not Categorical or Ordered Categorical. If they are, each unique value of Income will become a separate predictor variable in your model. This means that if you have 1000 unique incomes, you will end up with 999 predictors, instead of just 1 as you intended. - Included too many
**Independent questions**. If is generally bad research practice to include 100s or 1000s of predictor variables in your model. Before you run your regression, decide on which variables might reasonally have an effect on outcome variable. For example, you would not use variables like shoe size or hair color to predict income so don't include them in your model. Avoid using options like**Use All**when selecting your predictors.

## Next

How to Troubleshoot Regression Problems