From the very simple to the very complex, "Statistical Inference on General Regression Data" provides a unified introduction to a wide range of regression models, including the general linear model (GLM), the nonlinear regression model, the multivariate linear regression model (MANOVA), the logistic regression model, the Poisson regression model, the multinomial regression model, the L1-regression model, and the Cox regression model. The book explains the use of statistical inference packages to analyze data and includes code for performing likelihood inference using SIP, R, and SAS. It also addresses the concept of residuals in general regression models and presents special cases.