Predicting Real-valued Outputs: An introduction to regression

This document provides an introduction to regression analysis, which is used to predict real-valued outputs. It discusses single and multivariate linear regression, including how to calculate the maximum likelihood estimate of the regression coefficient(s). It also covers extensions such as adding a constant term, handling varying noise levels in the data, and nonlinear regression models. The goal is to estimate the parameters that best predict the output values given the input features.