Statistical Analysis and Data Display [[electronic resource] ] : An Intermediate Course with Examples in R / / by Richard M. Heiberger, Burt Holland

• Autore: Heiberger Richard M
• Pubblicazione: New York, NY : , : Springer New York : , : Imprint : Springer, , 2015
• Edizione: 2nd ed. 2015.
• Descrizione fisica: 1 online resource (XXXI, 898 p. 341 illus., 326 illus. in color.)
• Disciplina: 519.50285
• Soggetto topico: Statistics; R (Computer program language); Statistical Theory and Methods; Statistics and Computing/Statistics Programs; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
• Persona (resp. second.): HollandBurt
• Note generali: Bibliographic Level Mode of Issuance: Monograph
• Nota di bibliografia: Includes bibliographical references (pages [873]-885) and indexes.
• Nota di contenuto: 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 -- 1.3.1 Fundamentals -- 1.3.2 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) -- 2.A Appendix: Missing Values in R -- 3 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. ; 3.3.4.2 Stem-and-Leaf Display -- 3.3.4.3 Boxplots -- 3.3.5 Multivariate Distributions-Covarianceand Correlation -- 3.4 Three Probability Distributions -- 3.4.1 The Binomial Distribution -- 3.4.2 The Normal Distribution -- 3.4.3 The (Student's) t Distribution -- 3.5 Sampling Distributions -- 3.6 Estimation -- 3.6.1 Statistical Models -- 3.6.2 Point and Interval Estimators -- 3.6.3 Criteria for Point Estimators -- 3.6.4 Confidence Interval Estimation -- 3.6.5 Example-Confidence Interval on the Mean μ of a Population Having Known Standard Deviation -- 3.6.6 Example-One-Sided Confidence Intervals -- 3.7 Hypothesis Testing -- 3.8 Examples of Statistical Tests -- 3.9 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 -- 4.7 Scatterplot Matrices-Continued -- 4.8 Data Transformations -- 4.9 Life Expectancy Example-Continued -- 4.10 Color Vision -- 4.11 Exercises -- 4.A Appendix: R Graphics -- 4.A.1 Cartesian Products -- 4.A.2 Trellis Paradigm -- 4.A.3 Implementation of Trellis Graphics -- 4.A.4 Coordinating Sets of Related Graphs -- 4.A.5 Cartesian Product of Model Parameters -- 4.A.6 Examples of Cartesian Products -- 4.A.7 latticeExtra-Extra Graphical Utilities Basedon Lattice -- 4.B Appendix: Graphs Used in This Book -- 4.B.1 Structured Sets of Graphs -- 4.B.2 Combining Panels -- 4.B.3 Regression Diagnostics. ; 4.B.4 Graphs Requiring Multiple Calls to xyplot -- 4.B.5 Asymmetric Roles for the Row and Column Sets -- 4.B.6 Rotated Plots -- 4.B.7 Squared Residual Plots -- 4.B.8 Adverse Events Dotplot -- 4.B.9 Microplots -- 4.B.10 Alternate Presentations -- 5 Introductory Inference -- 5.1 Normal (z) Intervals and Tests -- 5.1.1 Test of a Hypothesis Concerning the Mean of a Population Having Known Standard Deviation -- 5.1.2 Confidence Intervals for Unknown Population Proportion p -- 5.1.3 Tests on an Unknown Population Proportion p -- 5.1.4 Example-One-Sided Hypothesis Test Concerning a Population Proportion -- 5.2 t-Intervals and Tests for the Mean of a Population Having Unknown Standard Deviation -- 5.2.1 Example-Inference on a Population Mean μ -- 5.3 Confidence Interval on the Variance or Standard Deviation of a Normal Population -- 5.4 Comparisons of Two Populations Based on IndependentSamples -- 5.4.1 Confidence Intervals on the Difference Between Two Population Proportions -- 5.4.2 Confidence Interval on the Difference Between Two Means -- 5.4.3 Tests Comparing Two Population Means When the Samples Are Independent -- 5.4.4 Comparing the Variances of Two Normal Populations -- 5.5 Paired Data -- 5.5.1 Example-t-test on Matched Pairs of Means -- 5.6 Sample Size Determination -- 5.6.1 Sample Size for Estimation -- 5.6.2 Sample Size for Hypothesis Testing -- 5.7 Goodness of Fit -- 5.7.1 Chi-Square Goodness-of-Fit Test -- 5.7.2 Example-Test of Goodness-of-Fit to a Discrete Uniform Distribution -- 5.7.3 Example-Test of Goodness-of-Fit to a Binomial Distribution -- 5.8 Normal Probability Plots and Quantile Plots -- 5.8.1 Normal Probability Plots -- 5.8.2 Example-Comparing t-Distributions -- 5.9 Kolmogorov-Smirnov Goodness-of-Fit Tests -- 5.9.1 Example-Kolmogorov-Smirnov Goodness-of-Fit Test -- 5.10 Maximum Likelihood -- 5.10.1 Maximum Likelihood Estimation. ; 5.10.2 Likelihood Ratio Tests -- 5.11 Exercises -- 6 One-Way Analysis of Variance -- 6.1 Example-Catalyst Data -- 6.2 Fixed Effects -- 6.3 Multiple Comparisons-Tukey Procedure for Comparing All Pairs of Means -- 6.4 Random Effects -- 6.5 Expected Mean Squares (EMS) -- 6.6 Example-Catalyst Data-Continued -- 6.7 Example-Batch Data -- 6.8 Example-Turkey Data -- 6.8.1 Study Objectives -- 6.8.2 Data Description -- 6.8.3 Analysis -- 6.8.4 Interpretation -- 6.8.5 Specification of Analysis -- 6.9 Contrasts -- 6.9.1 Mathematics of Contrasts -- 6.9.2 Scaling -- 6.9.2.1 Absolute-Sum-2 Scaling -- 6.9.2.2 Normalized Scaling -- 6.9.2.3 Integer Scaling -- 6.10 Tests of Homogeneity of Variance -- 6.11 Exercises -- 6.A Appendix: Computation for the Analysis of Variance -- 6.B Object Oriented Programming -- 7 Multiple Comparisons -- 7.1 Multiple Comparison Procedures -- 7.1.1 Bonferroni Method -- 7.1.2 Tukey Procedure for All Pairwise Comparisons -- 7.1.3 The Dunnett Procedure for Comparing One Mean with All Others -- 7.1.3.1 Computing Note-Specifying the Alternative Hypothesis -- 7.1.4 Simultaneously Comparing All Possible Contrasts Scheffé and Extended Tukey -- 7.1.4.1 The Scheffé Procedure -- 7.1.4.2 Scheffé Intervals with the Turkey Data -- 7.1.4.3 The Extended Tukey Procedure -- 7.2 The Mean-Mean Multiple Comparisons Display (MMC Plot) -- 7.2.1 Difficulties with Standard Displays -- 7.2.2 Hsu and Peruggia's Mean-Mean Scatterplot -- 7.2.2.1 Construction of the Mean-Mean Scatterplot -- 7.2.2.2 Interpretation of the Mean-Mean Scatterplot -- 7.2.3 Extensions of the Mean-Mean Display to Arbitrary Contrasts -- 7.2.3.1 Scaling -- 7.2.3.2 Contrasts -- 7.2.3.3 Labeling -- 7.2.3.4 q Multipliers -- 7.2.4 Display of an Orthogonal Basis Set of Contrasts -- 7.2.5 Hsu and Peruggia's Pulmonary Example -- 7.3 Exercises -- 8 Linear Regression by Least Squares -- 8.1 Introduction. ; 8.2 Example-Body Fat Data -- 8.2.1 Study Objectives -- 8.2.2 Data Description -- 8.2.3 Data Input -- 8.2.4 One-X Analysis -- 8.3 Simple Linear Regression -- 8.3.1 Algebra -- 8.3.2 Normal Distribution Theory -- 8.3.3 Calculations -- 8.3.4 Residual Mean Square in Regression Printout -- 8.3.5 New Observations -- 8.4 Diagnostics -- 8.5 ECDF of Centered Fitted Values and Residuals -- 8.6 Graphics -- 8.7 Exercises -- 9 Multiple Regression-More Than One Predictor -- 9.1 Regression with Two Predictors-Least-Squares Geometry -- 9.2 Multiple Regression-Two-X Analysis -- 9.3 Multiple Regression-Algebra -- 9.3.1 The Hat Matrix and Leverage -- 9.3.2 Geometry of Multiple Regression -- 9.4 Programming -- 9.4.1 Model Specification -- 9.4.2 Printout Idiosyncrasies -- 9.5 Example-Albuquerque Home Price Data -- 9.5.1 Study Objectives -- 9.5.2 Data Description -- 9.5.3 Data Input -- 9.6 Partial F-Tests -- 9.7 Polynomial Models -- 9.8 Models Without a Constant Term -- 9.9 Prediction -- 9.10 Example-Longley Data -- 9.10.1 Study Objectives -- 9.10.2 Data Description -- 9.10.3 Discussion -- 9.11 Collinearity -- 9.12 Variable Selection -- 9.12.1 Manual Use of the Stepwise Philosophy -- 9.12.2 Automated Stepwise Regression -- 9.12.3 Automated Stepwise Modeling of the Longley Data -- 9.13 Residual Plots -- 9.13.1 Partial Residuals -- 9.13.2 Partial Residual Plots -- 9.13.3 Partial Correlation -- 9.13.4 Added Variable Plots -- 9.13.5 Interpretation of Residual Plots -- 9.13.5.1 Response Variable Against Each of the Predictors -- 9.13.5.2 Residuals Against Each of the Predictors -- 9.13.5.3 Partial Residuals -- 9.13.5.4 Partial Residual Plots -- 9.13.5.5 Added Variable Plots -- 9.14 Example-U.S. Air Pollution Data -- 9.15 Exercises -- 9.A Appendix: Computation for Regression Analysis -- 10 Multiple Regression-Dummy Variables, Contrasts, and Analysis of Covariance. ; 10.1 Dummy (Indicator) Variables.
• Sommario/riassunto: 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.
• Titolo autorizzato: Statistical Analysis and Data Display
• ISBN: 1-4939-2122-3
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910300246303321
• Lo trovi qui: Univ. Federico II
• Serie: Springer Texts in Statistics, . 1431-875X