Overview for Fit Regression Model and Linear Regression

Fit Regression Model and Linear Regression perform the same analysis from different menus. Use these analyses to describe the relationship between a set of predictors and a continuous response using the ordinary least squares method. You can include interaction and polynomial terms, perform stepwise regression, and transform skewed data.

For example, real estate appraisers want to see how the sales price of urban apartments is associated with several predictor variables including the square footage, the number of available units, the age of the building, and the distance from the city center. The appraisers can use multiple regression to determine which predictors are significantly related to sales price.

After you perform the analysis, Minitab stores the model so that you can do the following:

Predict the response for new observations.
Plot the relationships among the variables.
Find values that optimize one or more responses.

For more information, go to Stored model overview.

Where to find this analysis

To fit a regression model, choose Stat > Regression > Regression > Fit Regression Model.

You can also choose Predictive Analytics Module > Linear Regression. The version of the analysis from the Predictive Analytics Module has the following differences.

You access analyses that use the fitted model from the output pane instead of from the menu. Analyses of the fitted model are available for any model that has output in the navigator, not the most recent model only.
The fitted model is available no matter what worksheet is active, so you can predict for columns of data that are in a different worksheet from the response variable.
Minitab Statistical Software saves the model in a project file (*.MPX).

When to use an alternate analysis

If you want to plot the relationship between one continuous (numeric) predictor and a continuous response, use Fitted Line Plot.
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 has two categories, such as pass and fail, use Fit Binary Logistic Model.
If your response variable contains three or more categories that have a natural order, such as strongly disagree, disagree, neutral, agree, and strongly agree, use Ordinal Logistic Regression.
If your response variable contains three or more categories that do not have a natural order, such as scratch, dent, and tear, use Nominal Logistic Regression.
If your response variable counts occurrences, such as the number of defects, use Fit Poisson Model.

When to use a predictive analytics model

For some applications, you consider different approaches to model construction. For more information on different types of models, go to Types of predictive analytics models in Minitab Statistical Software. Minitab offers CART^® Regression, TreeNet^® Regression, Random Forests^® Regression, and MARS^® Regression analyses with the Predictive Analytics Module. The Discover Best Model (Continuous Response) analysis compares the performance of different model types in 1 analysis. Click here for more information about how to activate the module.