Overview for Best Subsets Regression

Use Best Subsets Regression to compare different regression models that contain subsets of the predictors you specify. Minitab selects the best-fitting models that contain one predictor, two predictors, and so on. The best-fitting models have the highest R² values. Use Best subsets regression when you have a continuous response variable and more than one continuous predictor.

Best subsets regression is an efficient way to identify models that adequately fit your data with as few predictors as possible. Models that contain a subset of predictors may estimate the regression coefficients and predict future responses with smaller variance than the model that includes all predictors.

For example, an analyst at a retail store wants to predict sales volume. The predictors include traffic, population, average income, and direct competitors near the store. The analyst uses best subsets regression to identify the set of predictors that best predict sales volume.

Where to find this analysis

To perform best subsets regression, choose Stat > Regression > Regression > Best Subsets.

When to use an alternate analysis

If you have categorical predictors, use Fit Regression Model with a stepwise procedure to select a regression model by automatically adding or removing predictors based on their statistical significance.
If you have categorical predictors that are nested or random, use Fit General Linear Model if you have all fixed factors or Fit Mixed Effects Model if you have random factors.
If your response variable is a categorical variable, use a logistic regression procedure.