Understanding Regression Analysis av Peter H. (Texas Tech University Lubbock USA) Westfall, Andrea L. Arias

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<P><EM>Understanding Regression Analysis</EM> 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 <I>correct </I>model, and it also explains (proves) why the assumptions of the classical regression model are <I>wrong</I>. 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.</P><P><STRONG>Key features</STRONG> of the book include:</P><UL><P><LI>Numerous worked examples using the R software</LI><P></P><P><LI>Key points and self-study questions displayed "just-in-time" within chapters</LI><P></