Variable selection in linear regression model using stepwise regression
Stepwise regression is a dimensionality reduction method in which less
important predictor variables are successively removed in an automatic
iterative process. You can perform stepwise regression with or without the
LinearModel object, or by using the Regression Learner app.
|Regression Learner||Train regression models to predict data using supervised machine learning|
Stepwise Regression Using
|Perform stepwise regression|
|Fit linear regression model|
|Add terms to linear regression model|
|Remove terms from linear regression model|
|Improve linear regression model by adding or removing terms|
Stepwise Regression Without Using Object
|Interactive stepwise regression|
|Fit linear regression model using stepwise regression|
|Linear regression model|
- Stepwise Regression
In stepwise regression, predictors are automatically added to or trimmed from a model.
- Linear Regression with Interaction Effects
Construct and analyze a linear regression model with interaction effects and interpret the results.
- Wilkinson Notation
Wilkinson notation provides a way to describe regression and repeated measures models without specifying coefficient values.