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Categorical data analysis Alan Agresti

By: Agresti, AlanMaterial type: TextTextLanguage: English Series: Wiley series in probability and statisticsPublisher: Hoboken, NJ Wiley 2013Edition: 3. edDescription: XVI, 714 S. Ill., graph. DarstISBN: 9780470463635 (hardback)Subject(s): Multivariate analysis | Multivariate Analyse | Verallgemeinertes lineares Modell | Logit-Modell | Kategoriale Daten | Categories (Mathematics) | Mathematical analysisDDC classification: 519.5/35 | MAT029000 LOC classification: QA278Other classification: 31.73 | CM 4000 | SK 800 | MR 2100 | SK 840 | SK 830 | QH 234 | mat Online resources: Zentralblatt MATH Inhaltstext | Inhaltsverzeichnis Summary: "A classic in its own right, this book continues to provide an introduction to modern generalized linear models for categorical variables. The text emphasizes methods that are most commonly used in practical application, such as classical inferences for two- and three-way contingency tables, logistic regression, loglinear models, models for multinomial (nominal and ordinal) responses, and methods for repeated measurement and other forms of clustered, correlated response data. Chapter headings remain essentially with the exception of a new one on Bayesian inference for parametric models. Other major changes include an expansion of clustered data, new research on analysis of data sets with robust variables, extensive discussions of ordinal data, more on interpretation, and additional exercises throughout the book. R and SAS are now showcased as the software of choice. An author web site with solutions, commentaries, software programs, and data sets is available"--
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Item type Current library Call number Status Date due Barcode Item holds
Monographie ausleihbar Monographie ausleihbar IASS
IASS 17.91586 (Browse shelf(Opens below)) Available 000557941
Total holds: 0

Literaturverz. S. 643 - 688

In der eingedruckten CiP-Aufnahme irrtümlich als Bd. 792 der Schriftenreihe bezeichnet.

"A classic in its own right, this book continues to provide an introduction to modern generalized linear models for categorical variables. The text emphasizes methods that are most commonly used in practical application, such as classical inferences for two- and three-way contingency tables, logistic regression, loglinear models, models for multinomial (nominal and ordinal) responses, and methods for repeated measurement and other forms of clustered, correlated response data. Chapter headings remain essentially with the exception of a new one on Bayesian inference for parametric models. Other major changes include an expansion of clustered data, new research on analysis of data sets with robust variables, extensive discussions of ordinal data, more on interpretation, and additional exercises throughout the book. R and SAS are now showcased as the software of choice. An author web site with solutions, commentaries, software programs, and data sets is available"--

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