Applied Logistic Regression, Second Edition: Book and Solutions Manual Set - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Applied Logistic Regression, Second Edition: Book and Solutions Manual Set write by David W. Hosmer, Jr.. This book was released on 2001-11-13. Applied Logistic Regression, Second Edition: Book and Solutions Manual Set available in PDF, EPUB and Kindle. From the reviews of the First Edition. "An interesting, useful, and well-written book on logistic regression models. . . Hosmer and Lemeshow have used very little mathematics, have presented difficult concepts heuristically and through illustrative examples, and have included references.
Solutions Manual to accompany Applied Logistic Regression
Solutions Manual to accompany Applied Logistic Regression - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Solutions Manual to accompany Applied Logistic Regression write by David W. Hosmer, Jr.. This book was released on 2001-10-11. Solutions Manual to accompany Applied Logistic Regression available in PDF, EPUB and Kindle. Presenting information on logistic regression models, this work explains difficult concepts through illustrative examples. This is a solutions manual to accompany applied Logistic Regression, 2nd Edition.
Applied Linear Statistical Models
Applied Linear Statistical Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Applied Linear Statistical Models write by Michael H. Kutner. This book was released on 2005. Applied Linear Statistical Models available in PDF, EPUB and Kindle. Linear regression with one predictor variable; Inferences in regression and correlation analysis; Diagnosticis and remedial measures; Simultaneous inferences and other topics in regression analysis; Matrix approach to simple linear regression analysis; Multiple linear regression; Nonlinear regression; Design and analysis of single-factor studies; Multi-factor studies; Specialized study designs.
An Introduction to Categorical Data Analysis
An Introduction to Categorical Data Analysis - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook An Introduction to Categorical Data Analysis write by Alan Agresti. This book was released on 2018-10-11. An Introduction to Categorical Data Analysis available in PDF, EPUB and Kindle. 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, 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.
The Elements of Statistical Learning
The Elements of Statistical Learning - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook The Elements of Statistical Learning write by Trevor Hastie. This book was released on 2013-11-11. The Elements of Statistical Learning available in PDF, EPUB and Kindle. During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.
