Statistical Analysis and Data Display: An Intermediate Course with Examples in S-Plus, R, and SAS

ISBN-13: 9781441923202 / Angielski / Miękka / 2010 / 730 str.

Richard M. Heiberger; Burt Holland

Opis

1 Audience Students seeking master's degrees in applied statistics in the late 1960s and 1970s typically took a year-long sequence in statistical methods. Popular choices of the course text book in that period prior to the availability of high speed computing and graphics capability were those authored by Snedecor and Cochran, and Steel and Torrie. By 1980, the topical coverage in these classics failed to include a great many new and important elementary techniques in the data analyst's toolkit. In order to teach the statistical methods sequence with adequate coverage of topics, it became necessary to draw material from each of four or five text sources. Obviously, such a situation makes life difficult for both students and instructors. In addition, statistics students need to become proficient with at least one high-quality statistical software package. This book can serve as a standalone text for a contemporary year-long course in statistical methods at a level appropriate for statistics majors at the master's level or other quantitatively oriented disciplines at the doctoral level. The topics include both concepts and techniques developed many years ago and a variety of newer tools not commonly found in textbooks."

Recenzje

From the reviews:

"Heiberger and Holland have produced a comprehensive, well-presented review of essential statistical topics...this is an excellent book for suppporting advanced undergraduate courses on statsitics or data analysis [and] a valuable reference source to researchers..." Biomedical Engineering Online, 2005, 4:18

"Overall, I am impressed with this book and recommend it for graduate statistical methods courses where S-PLUS, R, or SAS is used. There is good incorporation of software tools that complement the statsitical methods presented that go beyond what most statistical methods books provide, a very broad range of topics is covered with examples representing many disciplines, and the emphasis of graphing and presentation of data is excellent." Technometrics, August 2005

"Discussion of each topic begins with a clear and easy-to-read presentation of the relevant theory at a level that statistics majors (and nonmajors) would enjoy..." The American Statistician, August 2005

"Throughout the book, statistical theory is kept to a minimum, and statistical concepts and methods are well stated, without lengthy derivations of formulas, but with the aid of graphical displays. Many examples are provided to illustrate the use of statistical methods. Exercise problems are well written and will be good practice for readers." Biometrics, December 2005

"A notable aspect of this book is the strong emphasis given to presenting data using graphical displays as well as the instructions and programs provided to create these displays in ... . The authors' great emphasis on graphical methods ... of the existing statistical methods books. I am impressed with this book and recommend it for graduate statistical methods courses where S-PLUS, R, or SAS is used. There is good incorporation of software tools .... The emphasis of graphing and presentation of data is excellent." (Theresa L. Utlaut, Technometrics, Vol. 47 (3), August, 2005)

"The statistical analysis in the book is taught on interesting real examples. ... the analysis is quite deep. An extraordinary care is devoted to graphical presentation of results. Many of the graphical formats are novel. ... appendices to the book contain useful remarks on statistical software, typography, and a review of fundamental mathematical concepts. A reader who studies the book and repeats the authors' calculations gains basic statistical skill and knowledge of software for solving similar problems in applications." (EMS - European Mathematical Society Newsletter, December, 2004)

"This is a huge book with huge aims - and the authors managed to get it done. ... is a modern textbook on many statistical methods including theory, with many well selected examples, and with code written for appropriate statistical software ... . It is nice to see a book that tries to explain methods together with the usage of two widely used statistical software systems, namely SAS and the S language. This comprehensive book is a nice all rounder for statistics students ... ." (Uwe Ligges, Statistical Papers, Vol. 47, 2005)

"Although this book is aimed at postgraduate students, it would be equally valuable for non-specialists ... . This book is a review of the most widely used areas of statistics ... . A good feature of this book is that all the datasets used are freely available in online files ... . Therefore, it is possible to carry out the analyses yourself ... . There are many examples, which are well chosen to illustrate the points being made." (D. A. Goodwin, Journal of Applied Statistics, Vol. 33 (7), August, 2006)

"The authors present the book as a 'standalone text for a contemporary year-long course in statistical methods' ... . the core material is of high standard, and the book also brings scientific contributions in terms of novel graph types. ... my overall impression of the book is very good. I believe that the authors have managed to balance innovativeness and accessibility ... . it is both a valid textbook for students and teachers alike, and a useful reference book for practitioners." (Gaj Vidmar, ISCB News, Issue 41, June, 2006)

Spis treści

1 Introduction and Motivation.- 2 Data and Statistics.- 3 Statistics Concepts.- 4 Graphs.- 5 Introductory Inference.- 6 One-Way Analysis of Variance.- 7 Multiple Comparisons.- 8 Linear Regression by Least Squares.- 9 Multiple Regression—More Than One Predictor.- 10 Multiple Regression—Dummy Variables and Contrasts.- 11 Multiple Regression—Regression Diagnostics.- 12 Two-Way Analysis of Variance.- 13 Design of Experiments—Factorial Designs.- 14 Design of Experiments—Complex Designs.- 15 Bivariate Statistics—Discrete Data.- 16 Nonparametrics.- 17 Logistic Regression.- 18 Time Series Analysis.- A Software.- A.1 Statistical Software.- A.2 Text Editing Software.- A.2.1 Emacs.- A.2.2 Microsoft Word.- A.3 Word Processing Software.- A.3.2 Microsoft Word.- A.4 Graphics Display Software.- A.5 Operating Systems.- A.6 Mathematical Fonts.- A.7 Directory Structure.- A.7.1 HOME Directory.- A.7.2 HH Book Online Files.- B.1 Create Your Working Directory and Make the HH Library Available.- B.1.3 Windows and R.- B.1.6 Unix and R.- B.4 HH Library Functions.- B.5 Learning the S Language.- B.6 S Language Style.- C SAS.- C.1 Make the HH Library Available.- C.1.1 Windows.- C.1.2 Unix.- C.2 Using SAS with HH.- C.2.1 Reading HH Datasets.- C.2.2 Any Other Data Files.- C.2.3 ASCII Data Files with TAB Characters.- C.2.4 Windows and Unix EOL (End-of-Line) Conventions.- C.3 Macros.- C.4 Learning the SAS Language.- C.5 SAS Coding Conventions.- D Probability Distributions.- D.1.1 An Example Involving Calculations with the Binomial Distribution.- D.2 Noncentral Probability Distributions.- E Editors.- E.1 Working Style.- E.2 Typography.- E.3 Emacs and ESS.- E.3.1 ESS.- E.3.2 Mouse and Keyboard.- E.3.3 Learning Emacs.- E.3.4 Requirements.- E.4 Microsoft Word.- E.4.1 Learning Word.- E.4.2 Requirements 6.- E.5 Microsoft Excel.- E.5.1 Database Management.- E.5.2 Organizing Calculations.- E.5.3 Excel as a Statistical Calculator.- E.6 Exhortations, Some of Which Are Writing Style.- E.6.1 Writing Style.- E.6.2 Programming Style and Common Errors.- E.6.3 Presentation of Results.- F Mathematics Preliminaries.- F.1 Algebra Review.- F.2 Elementary Differential Calculus.- F.3 An Application of Differential Calculus.- F.4 Topics in Matrix Algebra.- F.4.1 Elementary Operations.- F.4.2 Linear Independence.- F.4.3 Rank.- F.4.4 Quadratic Forms.- F.4.5 Orthogonal Transformations.- F.4.6 Orthogonal Basis.- F.4.8 Matrix Factorization—Cholesky.- F.4.9 Orthogonal Polynomials.- F.4.10 Projection Matrices.- F.4.11 Geometry ot Mlatrices.- F.4.12 Eigenvalues and Eigenvectors.- F.4.13 Singular Value Decomposition.- F.4.14 Generalized Inverse.- F.4.15 Solving Linear Equations.- F.5 Combinations and Permutations.- F.5.1 Factorial.- F.5.2 Permutations.- F.5.3 Combinations.- F.6 Exercises.- G Graphs Based on Cartesian Products.- G.1 Structured Sets of Graphs.- G.1.1 Cartesian Products.- G.1.2 Trellis Paradigm.- G.2 Scatterplot Matrices: splom and xysplom.- G.3 Cartesian Products of Sets of Functions.- G.4 Graphs Requiring Multiple Calls to xysplom.- G.5 Asymmetric Roles for the Row and Column Sets.- G.6 Rotated Plots.- G.7 Squared Residual Plots.- G.8 Alternate Presentations.- References.- List of Datasets.

Blurb

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.

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 authors provide and discuss S-Plus, 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.

Richard M. Heiberger and Burt Holland are both Professors in the Department of Statistics at Temple University and elected Fellows of the American Statistical Association. Richard M. Heiberger participated in the design of the S-Plus linear model and analysis of variance commands while on research leave at Bell Labs in 1987–88 and has been closely involved as a beta tester and user of S-Plus. Burt Holland has made many research contributions to linear modeling and simultaneous statistical inference, and frequently serves as a consultant to medical investigators. Both teach the Temple University course sequence that inspired them to write this text.