Statistical Analysis and Data Display: An Intermediate Course with Examples in R
Springer | Springer Texts in Statistics | January 24, 2016 | ISBN-10: 1493921215 | 898 pages | pdf | 18.99 mb
Authors: Heiberger, Richard M., Holland, Burt
New edition features color throughout, reworking of the material to emphasize R (but also utilize SAS), and new material
New chapters include Ch. 19 on Likert Scale Data to pick up on the importance of rating scales in fields from population studies to phsychometrics and Ch. 20 on Medical, Pharmaceutical and Social Science Examples
Unique features: chapters introducing the statistics and probability methods used, R package-specific appendices, exercises, code for the graphics used throughout the book, and the ability for readers to also use SAS code
Appendices on Shiny, R Commander, LaTeX, word processors and other topics
About this Textbook
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 computer listings-for all the methods they cover. They emphasize how to construct and interpret graphs, discuss principles of graphical design, and show how accompanying traditional tabular results are used to confirm the visual impressions derived directly from the graphs. Many of the graphical formats are novel and appear here for the first time in print. All chapters have exercises.
The second edition features new chapters, sections and revisions. New chapters cover Likert Scale Data to build on the importance of rating scales in fields from population studies to psychometrics.
This book can serve as a standalone text for statistics majors at the masters 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 authors provide and discuss R and SAS executable functions and macros for all new graphical display formats. All graphs and tabular output in the book were constructed using these programs. Complete transcripts for all examples and figures are provided for readers to use as models for their own analyses.
Number of Illustrations and Tables
15 illus., 326 in colour
Statistical Theory and Methods
Statistics and Computing / Statistics Programs
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences