Linear Regression in R – stepwise linear regression in R

Linear regression is a statistical method used to understand the relationship between a dependent variable (also known as the outcome or response variable) and one or more independent variables (also known as predictors or explanatory variables). In other words, it is used to predict the value of a dependent variable based on the values of one or more independent variables.

Stepwise linear regression is a method of building a linear regression model by adding or removing independent variables in a step-by-step process. The goal of stepwise linear regression is to find the best subset of independent variables that can predict the value of the dependent variable.

The stepwise linear regression process typically begins with an empty model, and then variables are added to the model one at a time based on their statistical significance. The variable that has the highest correlation with the dependent variable is added first, followed by the variable that has the next highest correlation and so on. The process continues until all the variables that meet a certain criterion, such as the p-value, are included in the model.

The stepwise linear regression process can also include a step where variables that are already in the model are removed if they are found to be insignificant. This is done to eliminate variables that do not contribute to the model’s ability to predict the dependent variable.

Using stepwise linear regression in R is relatively easy. You would need to install the “MASS” package in R, and then you can use the “stepAIC” function in the package to fit a stepwise linear regression model to your data. The function takes two main arguments: the independent variables and the dependent variable. You can also specify the criterion that you want to use for adding and removing variables in the model.

Once the model is fit, you can use the “predict” function to make predictions on new data. Additionally, you can use the “summary” function to get a summary of the model’s performance, including information about the R-squared value, which is a measure of how well the model fits the data.

In summary, stepwise linear regression is a method of building a linear regression model by adding or removing independent variables in a step-by-step process, the goal of stepwise linear regression is to find the best subset of independent variables that can predict the value of the dependent variable. It is relatively easy to implement in R using the “MASS” package and the “stepAIC” function.

In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in R programming: Linear Regression in R – stepwise linear regression in R.