![]() We’ll attempt to fit a simple linear regression model using hours as the explanatory variable and exam score as the response variable. Step 1: Load the Dataįor this example, we’ll create a fake dataset that contains the following two variables for 15 students: This tutorial provides a step-by-step explanation of how to perform simple linear regression in R. This equation can help us understand the relationship between the explanatory and response variable, and (assuming it’s statistically significant) it can be used to predict the value of a response variable given the value of the explanatory variable. ![]() b 0: The intercept of the regression line.In a nutshell, this technique finds a line that best “fits” the data and takes on the following form: Simple linear regression is a technique that we can use to understand the relationship between a single explanatory variable and a single response variable.
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