Statistical Analysis and Data Display: An Intermediate Course with Examples in R (Springer Texts in Statistics) - Tapa dura

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Richard M. Heiberger is Professor Emeritus in the Department of Statistics of Temple University, an elected Fellow of the American Statistical Association, and a former Chair of the Section on Statistical Computing of the American Statistical Association.  He was Graduate Chair for the Department of Statistics and Acting Associate Vice Provost for the University.  He participated in the design of the linear model and analysis of variance functions while on research leave at Bell Labs.  He has taught short courses at the Joint Statistics Meetings, the American Statistical Association Conference on Statistical Practice, the R Users Conference, and the Deming Conference on Applied Statistics.  He has consulted with several pharmaceutical companies.

Burt Holland was Professor in the Department of Statistics of Temple University, an elected Fellow of the American Statistical Association, Chair of the Department of Statistics of Temple University, and Chair of Collegial Assembly of the Fox School.  He has taught short courses at the Joint Statistics Meetings and the Deming Conference on Applied Statistics.  He has made many contributions to linear modeling and simultaneous statistical inference.  He frequently served as consultant to medical investigators.  He developed a very popular General Education course on Statistics and the News.

De la contraportada

This contemporary presentation of statistical methods features

extensive use of graphical displays for exploring data and for

displaying the analysis.  The authors demonstrate how to analyze

data showing code, graphics, and accompanying tabular listings for

all the methods they cover. They emphasize how to construct and

interpret graphs. They discuss principles of graphical design. They

identify situations where visual impressions from graphs may need

confirmation from traditional tabular results. All chapters have

exercises.

The authors provide and discuss R functions for all the new graphical

display formats. All graphs and tabular output in the book were

constructed using these functions. Complete R scripts for all examples

and figures are provided for readers to use as models for their own

analyses.

This book can serve as a standalone text for statistics majors at the

master s level and for other quantitatively oriented disciplines at

the doctoral level, 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. The authors illustrate classical concepts and

techniques with a variety of case studies using both newer graphical

tools and traditional tabular displays.

The Second Edition features graphs that are completely redrawn using

the more powerful graphics infrastructure provided by R's lattice

package. There are new sections in several of the chapters, revised

sections in all chapters and several completely new appendices.

New graphical material includes:

an expanded chapter on graphics;

a section on graphing Likert Scale Data to build on the importance of

rating scales in fields from population studies to psychometrics;

a discussion on design of graphics that will work for readers with

color-deficient vision;

an expanded discussion on the design of multi-panel graphics;

expanded and new sections in the discrete bivariate statistics chapter

on the use of mosaic plots for contingency tables including the n×2×2

tables for which the Mantel Haenszel Cochran test is appropriate;

an interactive (using the shiny package) presentation of the graphics

for the normal and t-tables that is introduced early and used in many

chapters.

The new appendices include discussions of R, the HH package

designed for R (the material in the HH package was distributed as a

set of standalone functions with the First Edition of this book), the

R Commander package, the RExcel system, the shiny package, and a

minimal discussion on writing R packages. There is a new appendix on

computational precision illustrating and explaining the FAQ

(Frequently Asked Questions) about the differences between the

familiar real number system and the less-familiar floating point

system used in computers. The probability distributions appendix has

been expanded to include more distributions (all the distributions in

base R) and to include graphs of each. The editing appendix from the

First Edition has been split into four expanded appendices on working

style, writing style, use of a powerful editor, and use of LaTeX for

document preparation.