Stepwise Regression with Minitab

What is Stepwise Regression?

Stepwise regression is a statistical method to automatically select regression models with the best sets of predictive variables from a large set of potential variables. There are different statistical methods used in stepwise regression to evaluate the potential variables in the model:

  • F-test
  • T-test
  • R-square
  • AIC

Three Approaches to Stepwise Regression

  • Forward Selection
    Bring in potential predictors one by one and keep them if they have significant impact on improving the model.
  • Backward Selection
    Try out potential predictors one by one and eliminate them if they are insignificant to improve the fit.
  • Mixed Selection
    Is a combination of both forward selection and backward selection. Add and remove variables based on pre-defined significance threshold levels.

How to Use Minitab to Run a Stepwise Regression

Case study: We want to build a regression model to predict the oxygen uptake of a person who runs 1.5 miles. The potential predictors are:

  • Age
  • Weight
  • Runtime
  • Runpulse
  • RstPulse
  • MaxPulse

Data File: “Stepwise Regression” tab in “Sample Data.xlsx”

Sample Data Glance

Steps to run stepwise regression in Minitab:

  1. Click Stat → Regression → Regression → Fit Regression Model
  2. A new window named “Regression” appears.
  3. Select “Oxy” as the “Responses” and select all the other variables into the “Continuous Predictors” box.
  4. Click the “Stepwise” button and a new window named “Regression: Stepwise” pops up.
  5. Select the method of stepwise regression and enter the alphas to enter/remove. In this example, we use the “Forward selection” method and the alpha to enter is 0.25.
  6. Click “OK” in the window “Stepwise – Methods.”
  7. Click “OK” in the window “Stepwise Regression.”
  8. The results appear in the session window.

Model summary: One out of six potential factors is not statistically significant since its p-value is higher than the alpha to enter. Step History: Step-by-step records on how to come up with the final model. Each column indicates the model built in each step.

About Michael Parker

Michael Parker is the President and CEO of the Lean Sigma Corporation, a management consulting firm and online six sigma training, certification, and courseware provider. Michael has over 25 years of experience leading and executing lean six sigma programs and projects. As a Fortune 50 senior executive, Michael led oversight of project portfolios as large as 150 concurrent projects exceeding $100 million in annual capital expenditures. Michael has also managed multi-site operations with the accountability of over 250 quality assurance managers, analysts, and consultants. He is an economist by education, earning his Bachelor of Science degree from Radford University while also lettering four years as an NCAA Division I scholarship athlete. Michael earned his Six Sigma Master Black Belt certification from Bank of America and his Black Belt certification from R.R. Donnelley & Sons.

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