{"product_id":"understanding-regression-analysis-a-conditional-distribution-approach-paperback","title":"Understanding Regression Analysis: A Conditional Distribution Approach - Paperback","description":"\u003cp\u003eby \u003cb\u003ePeter H. Westfall\u003c\/b\u003e (Author), \u003cb\u003eAndrea L. Arias\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cem\u003eUnderstanding Regression Analysis\u003c\/em\u003e 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 \u003ci\u003ecorrect \u003c\/i\u003emodel, and it also explains (proves) why the assumptions of the classical regression model are \u003ci\u003ewrong\u003c\/i\u003e. 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.\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eKey features\u003c\/strong\u003e of the book include: \u003c\/p\u003e \u003cul\u003e \u003cp\u003e \u003c\/p\u003e\n\u003cli\u003eNumerous worked examples using the R software\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\n\u003cli\u003eKey points and self-study questions displayed \"just-in-time\" within chapters\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\n\u003cli\u003eSimple mathematical explanations (\"baby proofs\") of key concepts\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\n\u003cli\u003eClear explanations and applications of statistical significance (\u003ci\u003ep\u003c\/i\u003e-values), incorporating the American Statistical Association guidelines\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\n\u003cli\u003eUse of \"data-generating process\" terminology rather than \"population\"\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e\n\u003ci\u003e \u003c\/i\u003e\u003cp\u003e \u003c\/p\u003e\n\u003cli\u003eRandom-\u003ci\u003eX\u003c\/i\u003e framework is assumed throughout (the fixed-\u003ci\u003eX\u003c\/i\u003e case is presented as a special case of the random-\u003ci\u003eX\u003c\/i\u003e case)\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\n\u003cli\u003eClear explanations of probabilistic modelling, including likelihood-based methods\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\n\u003cli\u003eUse of simulations throughout to explain concepts and to perform data analyses\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e\n\u003c\/ul\u003e \u003cp\u003eThis 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.\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003ePeter H. Westfall\u003c\/strong\u003e has a Ph.D. in Statistics from the University of California at Davis, as well as many years of teaching, research, and consulting experience, in a variety of statistics-related disciplines. He has published over 100 papers on statistical theory, methods, and applications; and he has written several books, spanning academic, practitioner, and textbook genres. He is former editor of \u003ci\u003eThe American Statistician\u003c\/i\u003e, and a Fellow of the American Statistical Association.\u003c\/p\u003e\u003cp\u003eAndrea L. Arias is a Senior Operations Research Specialist at BNSF Railway. She has a Ph.D. in Industrial Engineering with a minor in Business Statistics from Texas Tech University, and a Doctoral Degree in Industrial Engineering from Pontificia Universidad Católica de Valparaiso, Chile. Her main areas of expertise include Mathematical Programming, Network Optimization, Statistics and Simulation. She is an active member of the Institute for Operations Research and the Management Sciences (INFORMS.)\u003c\/p\u003e\u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 514\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 1.04 x 10 x 7 IN\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e May 06, 2022\u003c\/div\u003e","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":42727399817279,"sku":"9780367493516","price":120.5,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0105\/8226\/1823\/files\/67ed53081f8b01912247eb27bd6ec694.webp?v=1765114677","url":"https:\/\/dhlswag.com\/products\/understanding-regression-analysis-a-conditional-distribution-approach-paperback","provider":"BBB","version":"1.0","type":"link"}