![]() ![]() DataFrame()īefore we fit a simple linear regression model, we should first visualize the data to gain an understanding of it.įirst, we want to make sure that the relationship between hours and score is roughly linear, since that is an underlying assumption of simple linear regression. The following code shows how to create this fake dataset in Python: import pandas as pdĭf = pd. ![]() 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 Python. 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.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |