Let's now play with linear regression and actually compute some linear regression and r-squared. We can start by creating a little bit of Python code here that generates some random-ish data that is in fact linearly correlated.
In this example I'm going to fake some data about page rendering speeds and how much people purchase, just like a previous example. We're going to fabricate a linear relationship between the amount of time it takes for a website to load and the amount of money people spend on that website:
%matplotlib inlineimport numpy as npfrom pylab import *pageSpeeds = np.random.normal(3.0, 1.0, 1000)purchaseAmount = 100 - (pageSpeeds + np.random.normal(0, 0.1,1000)) * 3
