Use Fit Regression Model 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 .
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.