Linear Regression in R – ordinary least squares regression in R

Linear Regression in R – ordinary least squares regression in R

Linear regression is a statistical method used to understand the relationship between a dependent variable and one or more independent variables. The goal of linear regression is to find the line of best fit that describes the relationship between the variables. One of the most common methods to achieve this is called ordinary least squares (OLS) regression.

In OLS regression, the goal is to minimize the sum of the squared differences between the predicted values and the actual values. This is done by finding the line of best fit that minimizes this sum of squared differences, also known as the residual sum of squares. In R, you can use the lm() function to perform OLS regression. This function takes a formula specifying the dependent and independent variables, and a dataset as its arguments and returns a linear model object.

Once you have the linear model, you can use various functions to check the model’s accuracy, such as summary() which gives you the coefficients of the model and other statistical information. You can also use plot() function to visualize the linear model and see how well it fits the data.

It’s important to note that before fitting the model, it’s a good idea to check for missing values and outliers in the dataset and handle them appropriately. Additionally, it’s also important to check if the assumptions of linear regression hold true for your data, such as linearity and independence of errors.


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 – ordinary least squares regression in R.

Linear Regression in R – ordinary least squares regression in R

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