Computer-Aided Multivariate Analysis, Fourth Edition (Chapman & Hall/CRC Texts in Statistical Science) - Tapa dura

Críticas

For the past 20 years, whenever I had and occasion to review a multivariate methodthis was the book that I grabbed first. These books kept the mathematical content to the minimally necessary material and used a wealth of nice examples. One of its attractions is that it is a practical text that works well with nonstatisticians who have had a decent statistics course. It also continues to be an excellent book for the statistician's bookshelf. Technometrics, November 2004 "This book is an excellent presentation of computer-aided multivariate analysis. I believe that it will be a very useful addition to any scholarly libraryit provides a comprehensive introduction to available techniques for analyzing data of this form, written in a style that should appeal to non-specialists as well as to statisticians." -Zentralblatt MATH 105

Reseña del editor

Computer-Aided Multivariate Analysis, Fourth Edition enables researchers and students with limited mathematical backgrounds to understand the concepts underlying multivariate statistical analysis, perform analysis using statistical packages, and understand the output. New topics include Loess and Poisson regression, nominal and ordinal logistic regression, interpretation of interactions in logistic and survival analysis, and imputation for missing values. This book includes new exercises and references, and updated options in the latest versions of the statistical packages. All data sets and codebooks are available for download.

The authors explain the assumptions made in performing each analysis and test, how to determine if your data meets those assumptions, and what to do if they do not. What to Watch out for sections in each chapter warn of common difficulties. By reading this text, you will know what method to use with your data set, how to get the results, and how to interpret them and explain them to others.

New in the Fourth Edition:

• Expanded explanation of checking for goodness of fit in logistic regression and survival analysis
• Kaplan-Meier estimates of survival curves, formal tests for comparing survival between groups, interactions and the use of time-dependent covariates in survival analysis
• Expanded discussion of how to handle missing values
• Latest features of the S-PLUS package in addition to SAS, SPSS, STATA, and STATISTICA for multivariate analysis
• Data sets for the problems are available at the CRC web site: http://www.crcpress.com/product/isbn/9781584883081
• Commands and output for examples used in the text for each statistical package are available at the UCLA web site: http://www.ats.ucla.edu/stat/examples/cama4/