# Understanding Regression Analysis: A Conditional Distribution Approach (Paperback)

### Description

*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 applications of statistical significance (

*p*-values), incorporating the American Statistical Association guidelines

Use of "data-generating process" terminology rather than "population"

Random-

*X*framework is assumed throughout (the fixed-

*X*case is presented as a special case of the random-

*X*case)

Clear explanations of probabilistic modelling, including likelihood-based methods

Use of simulations throughout to explain concepts and to perform data analyses

This book has a strong orientation towards science in general, as well as chapter-review and self-study questions, so it can be used as a textbook for research-oriented students in the social, biological and medical, and physical and engineering sciences. As well, its mathematical emphasis makes it ideal for a text in mathematics and statistics courses. With its numerous worked examples, it is also ideally suited to be a reference book for all scientists.