Stepwise Regression with JMP

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 a significant impact on improving the model.
  • Backward Selection
    Try out potential predictors individually 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 JMP 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”

Run Stepwise Regression in JMP:

  1. Click Analyze -> Fit Model
  2. Model Specification window appears.
  3. Select “Oxy” as the Y and add the potential factors to the model effects box
    Select “Stepwise” in the “Personality” dropdown box
  4. Click “Run Mode
  5. The “Stepwise Fit” page shows up
  6. Select the P-value Threshold for Stopping Rule
  7. Enter the “Prob to Enter” and “Prob to leave” thresholds into the corresponding text boxes.
  8. Select the stepwise regression direction
  • Forward
  • Backward
  • Mixed
  1. Click the “Go” button to let JMP automatically find the set of predictors satisfying the pre-defined significance probability thresholds.

Model summary: Two of the seven potential factors are not statistically significant since their p-value is higher than the alpha to enter. Step History: Step-by-step records on how to develop the final model. Each column indicates the model built in each step.

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