This document discusses regression analysis and correlation. Regression analysis models the relationship between a response variable and explanatory variables. It finds the line of best fit to describe how the response variable changes with the explanatory variables. Correlation measures the strength and direction of association between two variables. There are different types of correlation depending on the type of data. Regression assumptions include that the error terms have zero mean, are normally distributed, have constant variance, and are independent. Regression analysis involves checking these assumptions, creating a scatter plot, estimating coefficients to find the line of best fit, and making inferences.