Linear Regression in R using Stepwise Regression

Linear Regression is a statistical method that helps to understand the relationship between a dependent variable and one or more independent variables. It is represented by an equation in the form of Y = a + bX, where Y is the dependent variable, X is the independent variable, a is the y-intercept and b is the slope of the line. It helps to make predictions about the dependent variable, based on the values of the independent variables.

Stepwise Regression is a method used to simplify linear regression models by adding or removing variables from the model one at a time. It starts with an empty model and then adds variables one by one, based on their statistical significance, until the model reaches a desired level of accuracy. The goal of stepwise regression is to select a subset of variables that gives the best explanation of the dependent variable, while minimizing the number of variables in the model.

It’s important to note that stepwise regression can be sensitive to the order of the data and should be used with caution as it can overfit the data. It’s not recommended to use it in practice as it’s not always reliable, instead other methods like lasso, Ridge etc are used for variable selection.

 

In this Data Science Recipe, you will learn: Linear Regression in R using Stepwise Regression.

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