| Document Type
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BL
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| Record Number
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809723
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| Doc. No
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b623739
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| Main Entry
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Heiberger, Richard M.,1945-
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| Title & Author
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Statistical analysis and data display : : an intermediate course with examples in R /\ Richard M. Heiberger, Burt Holland.
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| Edition Statement
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Second edition.
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| Series Statement
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Springer texts in statistics
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| Page. NO
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1 online resource (xxxi, 898 pages) :: illustrations
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| ISBN
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9781493921225
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: 1493921223
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9781493921218
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| Bibliographies/Indexes
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Includes bibliographical references and index.
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| Contents
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Intro; Preface; 1 Audience; 2 Motivation; 3 Structure; 4 Computation; 4.1 R; 4.2 The HH Package in R; 4.3 S-Plus, now called S+; 4.4 SAS; 5 Chapters in the Second Edition; 5.1 Revised Chapters; 5.2 Revised Appendices; 6 Exercises; Acknowledgments: First Edition; Acknowledgments; Contents; Author Bios; 1 Introduction and Motivation; 1.1 Statistics in Context; 1.2 Examples of Uses of Statistics; 1.2.1 Investigation of Salary Discrimination; 1.2.2 Measuring Body Fat; 1.2.3 Minimizing Film Thickness; 1.2.4 Surveys; 1.2.5 Bringing Pharmaceutical Products to Market; 1.3 The Rest of the Book
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1.3.1 Fundamentals1.3.2 Linear Models; 1.3.3 Other Techniques; 1.3.4 New Graphical Display Techniques; 1.3.5 Appendices on Software; 1.3.6 Appendices on Mathematics and Probability; 1.3.7 Appendices on Statistical Analysis and Writing; 2 Data and Statistics; 2.1 Types of Data; 2.2 Data Display and Calculation; 2.2.1 Presentation; 2.2.2 Rounding; 2.3 Importing Data; 2.3.1 Datasets for This Book; 2.3.2 Other Data sources; 2.4 Analysis with Missing Values; 2.5 Data Rearrangement; 2.6 Tables and Graphs; 2.7 R Code Files for Statistical Analysis and Data Display (HH)
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2.A Appendix: Missing Values in R3 Statistics Concepts; 3.1 A Brief Introduction to Probability; 3.2 Random Variables and Probability Distributions; 3.2.1 Discrete Versus Continuous Probability Distributions; 3.2.2 Displaying Probability Distributions-Discrete Distributions; 3.2.3 Displaying Probability Distributions-Continuous Distributions; 3.3 Concepts That Are Used When Discussing Distributions; 3.3.1 Expectation and Variance of Random Variables; 3.3.2 Median of Random Variables; 3.3.3 Symmetric and Skewed Distributions; 3.3.4 Displays of Univariate Data; 3.3.4.1 Histogram
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3.8 Examples of Statistical Tests3.9 Power and Operating Characteristic (O.C.) (Beta) Curves; 3.10 Efficiency; 3.11 Sampling; 3.11.1 Simple Random Sampling; 3.11.2 Stratified Random Sampling; 3.11.3 Cluster Random Sampling; 3.11.4 Systematic Random Sampling; 3.11.5 Standard Errors of Sample Means; 3.11.6 Sources of Bias in Samples; 3.12 Exercises; 4 Graphs; 4.1 What Is a Graph?; 4.2 Example-Ecological Correlation; 4.3 Scatterplots; 4.4 Scatterplot Matrix; 4.5 Array of Scatterplots; 4.6 Example-Life Expectancy; 4.6.1 Study Objectives; 4.6.2 Data Description; 4.6.3 Initial Graphs
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| Abstract
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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.
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| Subject
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Mathematical statistics-- Data processing.
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Statistics-- Data processing.
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R (Computer program language)
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Statistics.
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Statistical Theory and Methods.
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Statistics and Computing/Statistics Programs.
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Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
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Mathematical statistics-- Data processing.
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R (Computer program language)
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Statistics-- Data processing.
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Mathematical Statistics.
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Mathematics.
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Physical Sciences Mathematics.
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| Subject
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SAS (Computer file)
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| Dewey Classification
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519.5/0285
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| LC Classification
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QA276.4
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| Added Entry
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Holland, Burt
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