Understanding Regression Analysis: A Conditional Distribution Approach

Understanding Regression Analysis unifies diverse regression applications including the classical model, ANOVA models, generalized models including Poisson, Negative binomial, logistic, and survival, neural networks, and decision trees under a common umbrella -- namely, the conditional distribution model.It explains why the conditional distribution model is the correct model, and it also explains (proves) why the assumptions of the classical regression model are wrong.Unlike other regression books, this one from the outset takes a realistic approach that all models are just approximations.Hence, the emphasis is to model Nature’s processes realistically, rather than to assume (incorrectly) that Nature works in particular, constrained ways. Key features of the book include: Numerous worked examples using the R software Key points and self-study questions displayed "just-in-time" within chapters Simple mathematical explanations ("baby proofs") of key concepts Clear explanations and applic