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Wiley Series in Probability and Statistics Ser.: Statistical Analysis of...

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Características del artículo

Estado
En buen estado: Libro que se ha leído pero que está en buen estado. Daños mínimos en la tapa, ...
ISBN
9780471290087

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Product Identifiers

Publisher
Wiley & Sons, Incorporated, John
ISBN-10
0471290084
ISBN-13
9780471290087
eBay Product ID (ePID)
1075470

Product Key Features

Number of Pages
488 Pages
Publication Name
Statistical Analysis of Categorical Data
Language
English
Subject
Probability & Statistics / General, Probability & Statistics / Multivariate Analysis
Publication Year
1999
Type
Textbook
Subject Area
Mathematics
Author
Chris J. Lloyd
Series
Wiley Series in Probability and Statistics Ser.
Format
Hardcover

Dimensions

Item Height
1.1 in
Item Weight
28 Oz
Item Length
9.5 in
Item Width
6.2 in

Additional Product Features

Intended Audience
Scholarly & Professional
LCCN
98-039058
Reviews
"In conclusion, were I to teach a course in categorical data analysis, I would select this textbook...I compliment the author on his work. I feel that he has succeeded in meeting his goals." (Technometrics Vol. 42, No. 3 August 2000) The problems at the end of the chapters are appropriate for a course at this level and involve examples that will give the student a feel for the material, and there are also examples with which to work through the mathematics. (Technometrics, August 2000, Vol. 42, No. 3)
Dewey Edition
21
Series Volume Number
346
Illustrated
Yes
Dewey Decimal
519.5/35
Table Of Content
The Tools of Statistical Inference. Distribution Theory for Count Data. Binary Contingency Tables. Binomial Regression Models. Smoothing Binomial Data. Poisson Regression Models. Conditional Inference. Index.
Synopsis
In this volume, influential statistician Christopher Lloyd presents a comprehensive, self-contained discussion of the special statistical methods used to analyze and report count data, also known as categorical data. The emphasis here is on modern methods, with some attention paid to traditional methods as well., Accessible, up-to-date coverage of a broad range of modern and traditional methods. The ability to understand and analyze categorical, or count, data is crucial to the success of statisticians in a wide variety of fields, including biomedicine, ecology, the social sciences, marketing, and many more. Statistical Analysis of Categorical Data provides thorough, clear, up-to-date explanations of all important methods of categorical data analysis at a level accessible to anyone with a solid undergraduate knowledge of statistics. Featuring a liberal use of real-world examples as well as a regression-based approach familiar to most students, this book reviews pertinent statistical theory, including advanced topics such as Score statistics and the transformed central limit theorem. It presents the distribution theory of Poisson as well as multinomial variables, and it points out the connections between them. Complete with numerous illustrations and exercises, this book covers the full range of topics necessary to develop a well-rounded understanding of modern categorical data analysis, including: ∗ Logistic regression and log-linear models. ∗ Exact conditional methods. ∗ Generalized linear and additive models. ∗ Smoothing count data with practical implementations in S-plus software. ∗ Thorough description and analysis of five important computer packages. Supported by an ftp site, which describes the facilities important to a statistician wanting to analyze and report on categorical data, Statistical Analysis of Categorical Data is an excellent resource for students, practicing statisticians, and researchers with a special interest in count data., Supported by an ftp site, which describes the facilities important to a statistician wanting to analyze and report on categorical data, Statistical Analysis of Categorical Data is an excellent resource for students, practicing statisticians, and researchers with a special interest in count data., Accessible, up-to-date coverage of a broad range of modern and traditional methods. The ability to understand and analyze categorical, or count, data is crucial to the success of statisticians in a wide variety of fields, including biomedicine, ecology, the social sciences, marketing, and many more. Statistical Analysis of Categorical Data provides thorough, clear, up-to-date explanations of all important methods of categorical data analysis at a level accessible to anyone with a solid undergraduate knowledge of statistics. Featuring a liberal use of real-world examples as well as a regression-based approach familiar to most students, this book reviews pertinent statistical theory, including advanced topics such as Score statistics and the transformed central limit theorem. It presents the distribution theory of Poisson as well as multinomial variables, and it points out the connections between them. Complete with numerous illustrations and exercises, this book covers the full range of topics necessary to develop a well-rounded understanding of modern categorical data analysis, including: * Logistic regression and log-linear models. * Exact conditional methods. * Generalized linear and additive models. * Smoothing count data with practical implementations in S-plus software. * Thorough description and analysis of five important computer packages. Supported by an ftp site, which describes the facilities important to a statistician wanting to analyze and report on categorical data, Statistical Analysis of Categorical Data is an excellent resource for students, practicing statisticians, and researchers with a special interest in count data., Zur Analyse kategorisierter Daten sind h ufig spezielle statistische Methoden erforderlich, die in der Praxis st ndig variiert und erweitert werden. F r Leser mit soliden Grundkenntnissen der Statistik wurden hier die modernsten Verfahren zusammengefa t und den traditionelleren Methoden gegen bergestellt. Dar ber hinaus werden f nf Programmpakete (Minitab, Glim, S-plus, LogXacT und StatXacT) ausf hrlich beschrieben. (04/99)
LC Classification Number
QA278.L564 1999

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