Beyond quantifying the amount of association between two variables, as was the goal in a previous chapter, regression analysis aims at describing that association and/or at predicting one of the variables based on the other ones. Examples of applications where this is needed abound in engineering and a broad range of industries. For example, in the insurance industry, when pricing a policy, the predictor variable encapsulates the available information about what is being insured, and the response variable is a measure of risk that the insurance company would take if underwriting the policy. In this context, a procedure is solely evaluated based on its performance at predicting that risk, and can otherwise be very complicated and have no simple interpretation. The chapter covers both local methods such as kernel regression (e.g., local averaging) and empirical risk minimization over a parametric model (e.g., linear models fitted by least squares). Cross-validation is introduced as a method for estimating the prediction power of a certain regression or classification metod.