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Publicado por Chapman and Hall/CRC 2022-05, 2022
ISBN 10: 0367493519 ISBN 13: 9780367493516
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Añadir al carritoCondición: New. Peter H. Westfall 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 pap.
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Añadir al carritoTaschenbuch. Condición: Neu. Understanding Regression Analysis | A Conditional Distribution Approach | Peter H. Westfall (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2022 | Chapman and Hall/CRC | EAN 9780367493516 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.
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Publicado por Chapman And Hall/CRC Mai 2022, 2022
ISBN 10: 0367493519 ISBN 13: 9780367493516
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 softwareKey points and self-study questions displayed 'just-in-time' within chaptersSimple mathematical explanations ('baby proofs') of key conceptsClear explanations and applications of statistical significance (p-values), incorporating the American Statistical Association guidelinesUse 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 methodsUse of simulations throughout to explain concepts and to perform data analysesThis 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. 516 pp. Englisch.
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Añadir al carritoTaschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 softwareKey points and self-study questions displayed 'just-in-time' within chaptersSimple mathematical explanations ('baby proofs') of key conceptsClear explanations and applications of statistical significance (p-values), incorporating the American Statistical Association guidelinesUse 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 methodsUse of simulations throughout to explain concepts and to perform data analysesThis 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.