how to find simple linear regression equation in r studio

Let’s say we have a dataset of the blood pressure and age of a certain group of people. Let’s try to understand the practical application of linear regression in R with another example. The best-fit line would be of the form:ī0 and B1 – Regression parameter Practical Application of Linear Regression Using R One limitation of linear regression is that it is sensitive to outliers.

The linear regression algorithm is basically fitting a straight line to our dataset using the least squares method so that we can predict future events. The above idea of prediction sounds magical, but it’s pure statistics. Now, by analyzing the correlation between the marketing budget and the sales data, we can predict next year’s sales if the company allocates a certain amount of money to the marketing department. Note that we are not calculating the dependency of the dependent variable on the independent variable, but just the association.įor example, a firm is investing some amount of money in the marketing of a product, and it has also collected sales data throughout the years. The two variables involved are the dependent variable (response variable), which responds to the change of the independent variable (predictor variable). Simple linear regression analysis is a technique to find the association between two variables. Practical Application of Linear Regression Using R.This article was published as a part of the Data Science Blogathon. Learn to implement linear regression algorithms through the sample codes in R found in this tutorial.Explore the various applications of linear regression.Understand the definition and significance of Linear regression.

How to find simple linear regression equation in r studio