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|a 519.5/35
|2 23
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| 100 |
1 |
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|a Agresti, Alan.
|
| 245 |
1 |
0 |
|a Categorical data analysis /
|c Alan Agresti.
|
| 250 |
|
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|a 3rd ed.
|
| 260 |
|
|
|a Hoboken, NJ :
|b Wiley Interscience,
|c c2013.
|
| 300 |
|
|
|a xvi, 714 p. :
|b il. ;
|c 27 cm.
|
| 490 |
1 |
|
|a Wiley series in probability and statistics
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| 504 |
|
|
|a Incluye referencias bibliográficas e índice.
|
| 505 |
0 |
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|a Preface -- 1. Introduction: Distributions and Inference for Categorical Data -- 2. Describing Contingency Tables -- 3. Inference for Two-Way Contingency Tables -- 4. Introduction to Generalized Linear Models -- 5. Logistic Regression -- 6. Building, Checking, and Applying Logistic Regression Models -- 7. Alternative Modeling of Binary Response Data -- 8. Models for Multinomial Responses -- 9. Loglinear Models for Contingency Tables -- 10. Building and Extending Loglinear Models -- 11. Models for Matched Pairs -- 12. Clustered Categorical Data: Marginal and Transitional Models -- 13. Clustered Categorical Data: Random Effects Models -- 14. Other Mixture Models for Discrete Data -- 15. Non-Model-Based Classification and Clustering -- 16. Large- and Small-Sample Theory for Parametric Models -- 17. Historical Tour of Categorical Data Analysis -- Appendix A. Statistical Software for Categorical Data Analysis -- Appendix B. Chi-Squared Distribution Values.
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| 520 |
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|a "The use of statistical methods for analyzing categorical data has increased dramatically, particularly in the biomedical, social sciences, and financial industries. Responding to new developments, this book offers a comprehensive treatment of the most important methods for categorical data analysis. Categorical Data Analysis, Third Edition summarizes the latest methods for univariate and correlated multivariate categorical responses. Readers will find a unified generalized linear models approach that connects logistic regression and Poisson and negative binomial loglinear models for discrete data with normal regression for continuous data. This edition also features: An emphasis on logistic and probit regression methods for binary, ordinal, and nominal responses for independent observations and for clustered data with marginal models and random effects models; Two new chapters on alternative methods for binary response data, including smoothing and regularization methods, classification methods such as linear discriminant analysis and classification trees, and cluster analysis; New sections introducing the Bayesian approach for methods in that chapter ; More than 100 analyses of data sets and over 600 exercises ; Notes at the end of each chapter that provide references to recent research and topics not covered in the text, linked to a bibliography of more than 1,200 sources ; A supplementary website showing how to use R and SAS; for all examples in the text, with information also about SPSS and Stata and with exercise solutions." --Contratapa.
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| 650 |
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0 |
|a Multivariate analysis.
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| 650 |
|
7 |
|a Análisis estadístico multivariable.
|2 UDESA
|
| 830 |
|
0 |
|a Wiley series in probability and statistics
|