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A contemporary presentation of statistical methods featuring 200 graphical displays for exploring data and displaying analyses. Many of the displays appear here for the first time. Discusses construction and interpretation of graphs, principles of graphical design, and relation between graphs and traditional tabular results. Can serve as a graduate-level standalone statistics text and as a reference book for researchers. In-depth discussions of regression analysis, analysis of variance, and design of experiments are followed by introductions to analysis of discrete bivariate data, nonparametrics, logistic regression, and ARIMA time series modeling. Concepts and techniques are illustrated with a variety of case studies. S-Plus, , and SAS executable functions are provided and discussed. S functions are provided for each new graphical display format. All code, transcript and figure files are provided for readers to use as templates for their own analyses.
The book focuses on the practice of regression and analysis of variance. It clearly demonstrates the different methods available and in which situations each one applies. It covers all of the standard topics, from the basics of estimation to missing data, factorial designs, and block designs, but it also includes discussion of topics, such as model uncertainty, rarely addressed in books of this type. The presentation incorporates an abundance of examples that clarify both the use of each technique and the conclusions one can draw from the results.
A highly recommended book on how to do statistical data analysis using or S-Plus. In the first chapters it gives an introduction to the S language. Then it covers a wide range of statistical methodology, including linear and generalized linear models, non-linear and smooth regression, tree-based methods, random and mixed effects, exploratory multivariate analysis, classification, survival analysis, time series analysis, spatial statistics, and optimization. The `on-line complements’ available at the books homepage provide updates of the book, as well as further details of technical material.
A companion book to a text or course on applied regression (such as “Applied Regression, Linear Models, and Related Methods” by the same author). It introduces S, and concentrates on how to use linear and generalized-linear models in S while assuming familiarity with the statistical methodology.
This book, written in Spanish, is oriented to researchers interested in applying multivariate analysis techniques to real processes. It combines the theoretical basis with applied examples coded in .
There are many books that are excellent sources of knowledge about individual statistical tools (survival models, general linear models, etc.), but the art of data analysis is about choosing and using multiple tools. In the words of Chatfield “... students typically know the technical details of regression for example, but not necessarily when and how to apply it. This argues the need for a better balance in the literature and in statistical teaching between techniques and problem solving strategies.” Whether analyzing risk factors, adjusting for biases in observational studies, or developing predictive models, there are common problems that few regression texts address. For example, there are missing data in the majority of datasets one is likely to encounter (other than those used in textbooks!) but most regression texts do not include methods for dealing with such data effectively, and texts on missing data do not cover regression modeling.
This provides an in-depth guide to writing software in the S language which forms the basis of both the commercial S-Plus and the Open Source data analysis software systems.
This is a book for statistical practitioners, particularly those who design and analyze studies for survival and event history data. Its goal is to extend the toolkit beyond the basic triad provided by most statistical packages: the Kaplan-Meier estimator, log-rank test, and Cox regression model.
A comprehensive guide to the use of the nlme package for linear and nonlinear mixed-effects models.
Integrates theory of statistics with the practice of statistics through a collection of case studies (”labs”), and uses to analyze the data.
This “Green Book” describes version 4 of S, a major revision of S designed by John Chambers to improve its usefulness at every stage of the programming process.
This is also called the “White Book“. It described software for statistical modeling in S and introduced the S3 version of classes and methods.
This book is often called the “Blue Book“, and introduced what is now known as S version 3, or S3.