000 02427 a2200241 4500
003 OSt
020 _a9781119405269
040 _cIIT Kanpur
041 _aeng
082 _a519.5
_bAg82i3
100 _aAgresti, Alan
245 _aAn introduction to categorical data analysis
_cAlan Agresti
250 _a3rd ed.
260 _bJohn Wiley
_c2019
_aHoboken, NJ
300 _axiii,375p
440 _aWiley series in probability and statistics
490 _a/ edited by David J. Balding
500 _aIncludes bibliographical references and index
520 _aThe use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. An Introduction to Categorical Data Analysis, Third Edition summarizes these methods and shows readers how to use them using software. Readers will find a unified generalized linear models approach that connects logistic regression and loglinear models for discrete data with normal regression for continuous data. Adding to the value in the new edition is: • Illustrations of the use of R software to perform all the analyses in the book • A new chapter on alternative methods for categorical data, including smoothing and regularization methods (such as the lasso), classification methods such as linear discriminant analysis and classification trees, and cluster analysis • New sections in many chapters introducing the Bayesian approach for the methods of that chapter • More than 70 analyses of data sets to illustrate application of the methods, and about 200 exercises, many containing other data sets • An appendix showing how to use SAS, Stata, and SPSS, and an appendix with short solutions to most odd-numbered exercises Written in an applied, nontechnical style, this book illustrates the methods using a wide variety of real data, including medical clinical trials, environmental questions, drug use by teenagers, horseshoe crab mating, basketball shooting, correlates of happiness, and much more. An Introduction to Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and biostatisticians as well as methodologists in the social and behavioral sciences, medicine and public health, marketing, education, and the biological and agricultural sciences.
650 _aMultivariate analysis
650 _aData analysis
942 _cBK
999 _c560339
_d560339