Predictive Analytics Parametric Models by Tamhane Ajit (10 results)
Language: English
Published by Wiley, 2020
Series: Wiley Series in Probability and Statistics, Book 315 of 358. Book 315 of 358 - Wiley Series in Probability and Statistics
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Published by John Wiley & Sons Inc, 2020
Series: Wiley Series in Probability and Statistics, Book 315 of 358. Book 315 of 358 - Wiley Series in Probability and Statistics
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Published by John Wiley & Sons Inc, 2020
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Hardcover. Condition: Brand New. har/psc edition. 359 pages. 10.50x7.50x1.00 inches. In Stock.
Language: English
Published by John Wiley & Sons Inc, 2020
Series: Wiley Series in Probability and Statistics, Book 315 of 358. Book 315 of 358 - Wiley Series in Probability and Statistics
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Buch. Condition: Neu. Predictive Analytics | Parametric Models for Regression and Classification Using R | Ajit C Tamhane | Buch | Preface xiiiAcknowledgments xvAbbreviations xviiAbout the companion website xxi1 Introduction 11.1 Supervised versus unsupervised learning 21.2 Parametric versus nonparametric models 31.3 Types of da…ta 41.4 Overview of parametric predictive analytics 52 | Englisch | 2020 | Wiley | EAN 9781118948897 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.
Language: English
Published by Wiley Okt 2020, 2020
Series: Wiley Series in Probability and Statistics, Book 315 of 358. Book 315 of 358 - Wiley Series in Probability and Statistics
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Buch. Condition: Neu. Neuware - Provides a foundation in classical parametric methods of regression and classification essential for pursuing advanced topics in predictive analytics and statistical learningThis book covers a broad range of topics in parametric regression and classification including multiple regression, logistic… regression (binary and multinomial), discriminant analysis, Bayesian classification, generalized linear models and Cox regression for survival data. The book also gives brief introductions to some modern computer-intensive methods such as classification and regression trees (CART), neural networks and support vector machines.The book is organized so that it can be used by both advanced undergraduate or masters students with applied interests and by doctoral students who also want to learn the underlying theory. This is done by devoting the main body of the text of each chapter with basic statistical methodology illustrated by real data examples. Derivations, proofs and extensions are relegated to the Technical Notes section of each chapter, Exercises are also divided into theoretical and applied. Answers to selected exercises are provided. A solution manual is available to instructors who adopt the text.Data sets of moderate to large sizes are used in examples and exercises. They come from a variety of disciplines including business (finance, marketing and sales), economics, education, engineering and sciences (biological, health, physical and social). All data sets are available at the book's web site. Open source software R is used for all data analyses. R codes and outputs are provided for most examples. R codes are also available at the book's web site.Predictive Analytics: Parametric Models for Regression and Classification Using R is ideal for a one-semester upper-level undergraduate and/or beginning level graduate course in regression for students in business, economics, finance, marketing, engineering, and computer science. It is also an excellent resource for practitioners in these fields.
