Linear Regression in R using OLS Regression

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Linear Regression in R using OLS Regression

Linear regression is a statistical method that is used to predict a continuous outcome variable based on one or more predictor variables. In R, one way to perform linear regression is through OLS (Ordinary Least Squares) regression.

The basic process for performing OLS regression in R is as follows:

Prepare the data by loading it into R and making sure that the predictor and target variables are in the correct format.

Fit a linear model to the data using the lm() function. This function takes the target variable and predictor variable as input.

View the results of the linear model by using the summary() function, which will give you information such as the coefficients of the model, the R-squared value, and the p-values of the predictor variables.

It’s important to note that this is a basic example and in practice, you would need to do more steps such as checking for assumptions, model selection, cross-validation, and evaluating the model. OLS regression is a powerful and easy-to-use technique for performing linear regression in R, it allows you to quickly fit a linear model to your data and view the results in a clear and concise format.


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

Linear Regression in R using OLS Regression

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