An Introduction to Categorical Data Analysis (3rd Edition) – eBook PDF
A valuable new edition of a standard reference
The 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, 3rd Edition (PDF) summarizes these methods and shows readers how to use them using software. College students 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 3rd edition is:
• Illustrations of the use of R software to perform all the analyses in the ebook
• New sections in many chapters introducing the Bayesian approach for the methods of that chapter
• An appendix showing how to use Stata, SAS, and SPSS, and an appendix with short solutions to most odd-numbered exercises
• More than 70 analyses of data sets to illustrate application of the methods, and about 200 exercises, many containing other data sets
• 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
Written in an applied, nontechnical style, this ebook An Introduction to Categorical Data Analysis 3e illustrates the methods using a wide variety of real data, including environmental questions, drug use by teenagers, medical clinical trials, basketball shooting, correlates of happiness, horseshoe crab mating, and much more.
An Introduction to Categorical Data Analysis, Third Edition is an invaluable tool for biostatisticians and statisticians as well as methodologists in the social and behavioral sciences, medicine and public health, education, marketing, and the biological and agricultural sciences.
NOTE: This sale only includes the ebook An Introduction to Categorical Data Analysis 3rd edition in PDF. No access codes included.