In this seventh lecture of the course, we delve into simple linear regression analysis. We discuss basic principles of regression as a way to leverage covariance information to improve predictive models. We conduct a simple regression by hand, compute the standard error of the estimate, and then discuss the assumptions of simple regression, including linearity, normality, and homoscedasticity.