Hits: 423

# 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

#### Free Machine Learning & Data Science Coding Tutorials in Python & R for Beginners. Subscribe @ Western Australian Center for Applied Machine Learning & Data Science.

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause.The information presented here could also be found in public knowledge domains.

# Learn by Coding: v-Tutorials on Applied Machine Learning and Data Science for Beginners

**Introduction to Applied Machine Learning & Data Science for Beginners, Business Analysts, Students, Researchers and Freelancers with Python & R Codes @ Western Australian Center for Applied Machine Learning & Data Science (WACAMLDS) !!!**

Latest end-to-end Learn by Coding Projects (Jupyter Notebooks) in Python and R:

**Applied Statistics with R for Beginners and Business Professionals**

**Data Science and Machine Learning Projects in Python: Tabular Data Analytics**

**Data Science and Machine Learning Projects in R: Tabular Data Analytics**

**Python Machine Learning & Data Science Recipes: Learn by Coding**