
In Linear Regression these two variables are related through an equation, where exponent (power) of both these variables is 1. Mathematically a linear relationship represents a straight line when plotted as a graph. A non-linear relationship where the exponent of any variable is not equal to 1 creates a curve.
The functions in Seaborn to find the linear regression relationship is regplot. The below example shows its use.
import seaborn as sb from matplotlib import pyplot as plt df = sb.load_dataset('tips') sb.regplot(x = "total_bill", y = "tip", data = df) plt.show()
Its output is as follows −

Pingback: Python - Chi-Square Test - Adglob Infosystem Pvt Ltd