The book will serve as a reference for data analysts and statistical methodologists.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Vendeur : Preferred Books, Rancho Cucamonga, CA, Etats-Unis
Soft cover. Etat : Fine. Fine condition. Looks unread. N° de réf. du vendeur 011982
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Vendeur : HPB-Emerald, Dallas, TX, Etats-Unis
paperback. Etat : Very Good. Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority! N° de réf. du vendeur S_459829101
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Vendeur : Book Alley, Pasadena, CA, Etats-Unis
Paperback. Etat : Good. Used with handling wear and some light soiling to page edges. May have limited internal markings although none were noted. Binding is tight. N° de réf. du vendeur mon0000573626
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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for dealing with nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with 'too many variables to analyze and not enough observations,' and powerful model validation techniques based on the bootstrap. This text realistically deals with model uncertainty and its effects on inference to achieve 'safe data mining'. There are many books that are excellent sources of knowledge about individual stastical tools (survival models, general linear models, etc.), but the art of data analysis is about choosing and using multiple tools. In the words of Chatfield '.students typically know the technical details of regressin for example, but not necessarily when and how to apply it. This argues the need for a better balance in the literature and in statistical teaching between techniques and problem solving strategies.' Whether analyzing risk factors, adjusting for biases in observational studies, or developing predictive models, there are common problems that few regression texts address. For example, there are missing data in the majority of datasets one is likely to encounter (other than those used in textbooks!) but most regression texts do not include methods for dealing with such data effectively, and texts on missing data do not cover regression modeling. 596 pp. Englisch. N° de réf. du vendeur 9781441929181
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Vendeur : moluna, Greven, Allemagne
Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Written by a well-known biostatistical researcher and expert on S-PlusProvides full-scale case studies of non-trivial datasetsCase studies use S-PLUSMany texts are excellent sources of knowledge about individual statistical tools, b. N° de réf. du vendeur 4173387
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Vendeur : PAPER CAVALIER UK, London, Royaume-Uni
Etat : good. A good reading copy. May contain markings or be a withdrawn library copy. N° de réf. du vendeur 9781441929181-4
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Vendeur : Lucky's Textbooks, Dallas, TX, Etats-Unis
Etat : New. N° de réf. du vendeur ABLIING23Mar2411530294546
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Vendeur : California Books, Miami, FL, Etats-Unis
Etat : New. N° de réf. du vendeur I-9781441929181
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Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for dealing with nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with 'too many variables to analyze and not enough observations,' and powerful model validation techniques based on the bootstrap. This text realistically deals with model uncertainty and its effects on inference to achieve 'safe data mining'. There are many books that are excellent sources of knowledge about individual stastical tools (survival models, general linear models, etc.), but the art of data analysis is about choosing and using multiple tools. In the words of Chatfield '.students typically know the technical details of regressin for example, but not necessarily when and how to apply it. This argues the need for a better balance in the literature and in statistical teaching between techniques and problem solving strategies.' Whether analyzing risk factors, adjusting for biases in observational studies, or developing predictive models, there are common problems that few regression texts address. For example, there are missing data in the majority of datasets one is likely to encounter (other than those used in textbooks!) but most regression texts do not include methods for dealing with such data effectively, and texts on missing data do not cover regression modeling. N° de réf. du vendeur 9781441929181
Quantité disponible : 1 disponible(s)
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
Etat : New. In. N° de réf. du vendeur ria9781441929181_new
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