Statistical Analysis and Data Display

Name: Statistical Analysis and Data Display | An Intermediate Course with Examples in R
Brand: Springer
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An Intermediate Course with Examples in R

Richard M. Heiberger Burt Holland(Author)

Springer (Publisher)

2nd Edition

Published on 23. December 2015

XXXI, 898 pages

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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. 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. Classical concepts and techniques are illustrated with a variety of case studies using both newer graphical tools and traditional tabular displays.New graphical material includes:an expanded chapter on graphicsa section on graphing Likert Scale Data to build on the importance of rating scales in fields from population studies to psychometricsa discussion on design of graphics that will work for readers with color-deficient visionan expanded discussion on the design of multi-panel graphicsexpanded and new sections in the discrete bivariate statistics capter on the use of mosaic plots for contingency tables including the n×2×2 tables for which the Mantel-Haenszel-Cochran test is appropriatean interactive (using the shiny package) presentation of the graphics for the normal and t-tables that is introduced early and used in many chapters

Reviews / Votes

"It is a thorough and self-contained book. Keeping with the spirit with earlier edition, the second edition is well organized and structured and builds on statistical knowledge and its appreciation in a logical and concise manner. The book provides a host of useful topics and techniques for students in the niche of statistical analysis and data display. The book can adopted as textbook for an intermediate level statistics course." (S. E. Ahmed, Technometrics, Vol. 58 (3), August, 2016)

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Content

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
10.2 Example-Height and Weight
10.2.1 Study Objectives
10.2.2 Data Description
10.2.3 Data Problems
10.2.4 Three Variants on the Analysis
10.3 Equivalence of Linear Independent X-Variables (such as Contrasts) for Regression
10.4 Polynomial Contrasts and Orthogonal Polynomials
10.4.1 Specification and Interpretation of Interaction Terms
10.5 Analysis Using a Concomitant Variable (Analysis of Covariance-ANCOVA)
10.6 Example-Hot Dog Data
10.6.1 Study Objectives
10.6.2 Data Description
10.6.3 One-Way ANOVA
10.6.4 Concomitant Explanatory Variable-ANCOVA
10.6.5 Tests of Equality of Regression Lines
10.7 ancovaplot Function
10.8 Exercises
11 Multiple Regression-Regression Diagnostics
11.1 Example-Rent Data
11.1.1 Study Objectives
11.1.2 Data Description
11.1.3 Rent Levels
11.1.4 Alfalfa Rent Relative to Other Rent
11.2 Checks on Model Assumptions
11.2.1 Scatterplot Matrix
11.2.2 Residual Plots
11.3 Case Statistics
11.3.1 Leverage
11.3.2 Deleted Standard Deviation
11.3.3 Standardized and Studentized Deleted Residuals
11.3.4 Cook's Distance
11.3.5 DFFITS
11.3.6 DFBETAS
11.3.7 Residuals vs Leverage
11.3.8 Calculation of Regression Diagnostics
11.4 Exercises
12 Two-Way Analysis of Variance
12.1 Example-Display Panel Data
12.1.1 Study Objectives
12.1.2 Data Description
12.1.3 Analysis Goals
12.2 Statistical Model
12.3 Main Effects and Interactions
12.4 Two-Way Interaction Plot
12.5 Sums of Squares in the Two-Way ANOVA Table
12.6 Treatment and Blocking Factors
12.7 Fixed and Random Effects
12.8 Randomized Complete Block Designs
12.9 Example-The Blood Plasma Data
12.9.1 Study Objectives
12.9.2 Data Description
12.9.3 Analysis
12.10 Random Effects Models and Mixed Models
12.11 Example-Display Panel Data-Continued
12.12 Studentized Range Distribution
12.13 Introduction to Nesting
12.13.1 Example-Workstation Data
12.13.2 Data Description
12.13.3 Analysis Goals
12.14 Example-The Rhizobium Data
12.14.1 Study Objectives
12.14.2 Data Description
12.14.3 First Rhizobium Experiment: Alfalfa Plants
12.14.4 Second Rhizobium Experiment: Clover Plants
12.14.5 Initial Plots
12.14.6 Alfalfa Analysis
12.14.7 Clover Analysis
12.15 Models Without Interaction
12.16 Example-Animal Feed Data
12.16.1 Study Objectives
12.16.2 Analysis
12.17 Exercises
12.A Appendix: Computation for the Analysis of Variance
13 Design of Experiments-Factorial Designs
13.1 A Three-Way ANOVA-Muscle Data
13.2 Latin Square Designs
13.2.1 Example-Latin Square
13.3 Simple Effects for Interaction Analyses
13.3.1 Example-The filmcoat Data
13.3.2 Study Objectives
13.3.3 Data Description
13.3.4 Data Analysis
13.4 Nested Factorial Experiment
13.4.1 Example-Gunload Data
13.4.2 Example-Turkey Data (Continued)
13.5 Specification of Model Formulas
13.5.1 Crossing of Two Factors
13.5.2 Example-Dummy Variables for Crossed Factors Nested Within Another Factor-Turkey Data (Continued Again)
13.6 Sequential and Conditional Tests
13.6.1 SAS Terminology for Conditional Sums of Squares
13.6.2 Example-Application to Clover Data
13.6.3 Example-Application to Body Fat Data
13.7 Exercises
13.A Appendix: Orientation for Boxplots
14 Design of Experiments-Complex Designs
14.1 Confounding
14.2 Split Plot Designs
14.3 Example-Yates Oat Data
14.3.1 Alternate Specification
14.3.2 Polynomial Effects for Nitrogen
14.4 Introduction to Fractional Factorial Designs
14.4.1 Example-28-2 Design
14.4.2 Example-25-1 Design
14.5 Introduction to Crossover Designs
14.5.1 Example-Two Latin Squares
14.6 ANCOVA with Blocks: Example-Apple Tree Data
14.6.1 Study Objectives
14.6.2 Data Description
14.6.3 Data Analysis
14.6.4 Model 1: yield '176 block + pre * treat
14.6.5 Model 2: yield.block '176 pre.block * treat
14.6.6 Model 3: yield.block '176 pre.block
14.6.7 Model 4: yield.block '176 treat
14.6.8 Model 5: yield.block '176 pre.block + treat
14.6.9 Model 6: yield.block.pre '176 treat
14.6.10 Multiple Comparisons
14.7 Example-testscore
14.7.1 Study Objectives
14.7.2 Data Description
14.7.3 Analysis-Plots
14.7.4 Analysis-ANOVA
14.7.5 Summary of ANOVA
14.8 The Tukey One Degree of Freedom for Nonadditivity
14.8.1 Example-Crash Data-Study Objectives
14.8.2 Data Description
14.8.3 Data Analysis
14.8.4 Theory
14.9 Exercises
15 Bivariate Statistics-Discrete Data
15.1 Two-Dimensional Contingency Tables-Chi-Square Analysis
15.1.1 Example-Drunkenness Data
15.1.2 Chi-Square Analysis
15.2 Two-Dimensional Contingency Tables-Fisher's Exact Test
15.2.1 Example-Do Juvenile Delinquents Eschew Wearing Eyeglasses?
15.3 Simpson's Paradox
15.4 Relative Risk and Odds Ratios
15.4.1 Glasses (Again)
15.4.2 Large Sample Approximations
15.4.2.1 Odds Ratio
15.4.2.2 Relative Risk
15.4.3 Example-Treating Cardiac Arrest with Therapeutic Hypothermia
15.5 Retrospective and Prospective Studies
15.6 Mantel-Haenszel Test
15.7 Example-Salk Polio Vaccine
15.8 Example-Adverse Experiences
15.9 Ordered Categorical Scales, Including Rating Scales
15.9.1 Display of Professional Challenges Dataset
15.9.2 Single-Panel Displays
15.9.3 Multiple-Panel Displays
15.9.3.1 One Question with Multiple Subsets of the Sample
15.9.3.2 One or More Subpopulations with Multiple Questions
15.9.3.3 Common Structure at Multiple Times-Population Pyramids
15.10 Exercises
16 Nonparametrics
16.1 Introduction
16.2 Sign Test for the Location of a Single Population
16.3 Comparing the Locations of Paired Populations
16.3.1 Sign Test
16.3.2 Wilcoxon Signed-Ranks Test
16.4 Mann-Whitney Test for Two Independent Samples
16.5 Kruskal-Wallis Test for Comparing the Locations of at Least Three Populations
16.6 Exercises
17 Logistic Regression
17.1 Example-The Space Shuttle Challenger Disaster
17.1.1 Study Objectives
17.1.2 Data Description
17.1.3 Graphical Display
17.1.4 Numerical Display
17.2 Estimation
17.3 Example-Budworm Data
17.4 Example-Lymph Nodes
17.4.1 Data
17.4.2 Data Analysis
17.4.3 Additional Techniques
17.4.4 Diagnostics
17.5 Numerical Printout
17.6 Graphics
17.6.1 Conditioned Scatterplots
17.6.2 Common Scaling in Comparable Plots
17.6.3 Functions of Predicted Values
17.7 Model Specification
17.7.1 Fitting Models When the Response Is Dichotomous
17.7.1.1 R and S-Plus
17.7.1.2 SAS
17.7.2 Fitting Models When the Response Is a SampleProportion
17.7.2.1 R and S-Plus
17.7.2.2 SAS
17.8 LogXact
17.9 Exercises
18 Time Series Analysis
18.1 Introduction
18.2 The ARIMA Approach to Time Series Modeling
18.2.1 AutoRegression (AR)
18.2.2 Moving Average (MA)
18.2.3 Differencing
18.2.4 Autoregressive Integrated Moving Average (ARIMA)
18.3 Autocorrelation
18.3.1 Autocorrelation Function (ACF)
18.3.2 Partial Autocorrelation Function (PACF)
18.4 Analysis Steps
18.5 Some Algebraic Development, Including Forecasting
18.5.1 The General ARIMA Model
18.5.2 Special Case-The AR(1) Model
18.5.3 Special Case-The MA(1) Model
18.6 Graphical Displays for Time Series Analysis
18.7 Models with Seasonal Components
18.7.1 Multiplicative Seasonal ARIMA Models
18.7.2 Example-co2 ARIMA(0,1,1)(0,1,1)12 Model
18.7.3 Determining the Seasonal AR and MA Parameters
18.8 Example of a Seasonal Model-The Monthly co2 Data
18.8.1 Identification of the Model
18.8.2 Parameter Estimation and Diagnostic Checking
18.8.3 Forecasting
18.9 Exercises
18.A Appendix: Construction of Time Series Graphs
18.A.1 Characteristics of This Presentationof the Time Series Plot
18.A.2 Characteristics of This Presentation of the Sample ACF and PACF Plots
18.A.3 Construction of Graphical Displays
18.A.4 Functions in the HH package for R
A R
A.1 Installing R-Initial Installation
A.1.1 Packages Needed for This Book-Macintoshand Linux
A.1.2 Packages and Other Software Needed for This Book-Windows
A.1.2.1 RExcel
A.1.2.2 RExcel Users Need to Install Rcmdr as Administrator
A.1.2.3 Packages Needed for This Book-Windows
A.1.2.4 `_12`1~2`$12=-1 Rtools
A.1.2.5 Windows Potential Complications: Internet, Firewall, and Proxy
A.1.3 Installation Problems-Any Operating System
A.1.4 `_12`1~2`$12=-1 XLConnect: All Operating Systems
A.2 Installing R-Updating
A.3 Using R
A.3.1 Starting the R Console
A.3.2 Making the Functions in the HH Package Available to the Current Session
A.3.3 Access HH Datasets
A.3.4 Learning the R Language
A.3.5 Duplicating All HH Examples
A.3.5.1 Linux and Macintosh
A.3.5.2 Windows.
A.3.6 Learning the Functions in R
A.3.7 Learning the lattice Functions in R
A.3.8 Graphs in an Interactive Session
A.4 S/R Language Style
A.5 Getting Help While Learning and Using R
A.6 R Inexplicable Error Messages-Some Debugging Hints
B HH
B.1 Contents of the HH Package
B.2 R Scripts for all Figures and Tables in the Book
B.2.1 Macintosh
B.2.2 Linux
B.2.3 Windows
B.3 Functions in the HH Package
B.4 HH and S+
C Rcmdr: R Commander
D RExcel: Embedding R inside Excel on Windows
D.1 Installing RExcel for Windows
D.1.1 Install R
D.1.2 Install Two R Packages Needed by RExcel
D.1.3 Install RExcel and Related Software
D.1.4 Install Rcmdr to Work with RExcel
D.1.5 Additional Information on Installing RExcel
D.2 Using RExcel
D.2.1 Automatic Recalculation of an R Function
D.2.2 Transferring Data To/From R and Excel
D.2.3 Control of a lattice Plot from an Excel/Rcmdr Menu
E Shiny: Web-Based Access to R Functions
E.1 NTplot
E.2 bivariateNormal
E.3 bivariateNormalScatterplot
E.4 PopulationPyramid
F R Packages
F.1 What Is a Package?
F.2 Installing and Loading R Packages
F.3 Where Are the Packages on Your Computer?
F.4 Structure of an R Package
F.5 Writing and Building Your Own Package
F.6 Building Your Own Package with Windows
G Computational Precision and Floating-Point Arithmetic
G.1 Examples
G.2 Floating Point Numbers in the IEEE 754 Floating-PointStandard
G.3 Multiple Precision Floating Point
G.4 Binary Format
G.5 Round to Even
G.6 Base-10, 2-Digit Arithmetic
G.7 Why Is .9 Not Recognized to Be the Same as (.3 + .6)?
G.8 Why Is (2)2 Not Recognized to Be the Same as 2?
G.9 zapsmall to Round Small Values to Zero for Display
G.10 Apparent Violation of Elementary Factoring
G.11 Variance Calculations
G.12 Variance Calculations at the Precision Boundary
G.13 Can the Answer to the Calculation be Represented?
G.14 Explicit Loops
H Other Statistical Software
I Mathematics Preliminaries
I.1 Algebra Review
I.1.1 Line
I.1.2 Parabola
I.1.3 Ellipse
I.1.4 Simultaneous Equations
I.1.5 Exponential and Logarithm Functions
I.1.6 Asymptote
I.2 Elementary Differential Calculus
I.3 An Application of Differential Calculus
I.4 Topics in Matrix Algebra
I.4.1 Elementary Operations
I.4.2 Linear Independence
I.4.3 Rank
I.4.4 Quadratic Forms
I.4.5 Orthogonal Transformations
I.4.6 Orthogonal Basis
I.4.7 Matrix Factorization-QR
I.4.8 Modified Gram-Schmidt (MGS) Algorithm
I.4.9 Matrix Factorization-Cholesky
I.4.10 Orthogonal Polynomials
I.4.11 Projection Matrices
I.4.12 Geometry of Matrices
I.4.13 Eigenvalues and Eigenvectors
I.4.14 Singular Value Decomposition
I.4.15 Generalized Inverse
I.4.16 Solving Linear Equations
I.4.16.1 n = m = rank(X)
I.4.16.2 n & m = rank(X)
I.4.16.3 m & p = rank(X)
I.5 Combinations and Permutations
I.5.1 Factorial
I.5.2 Permutations
I.5.3 Combinations
I.6 Exercises
J Probability Distributions
J.1 Continuous Central Distributions
J.1.1 Beta
J.1.2 Cauchy
J.1.3 Chi-Square
J.1.4 Exponential
J.1.5 F
J.1.6 Gamma
J.1.7 Log Normal
J.1.8 Logistic
J.1.9 Normal
J.1.10 Studentized Range Distribution
J.1.11 (Student's) T
J.1.12 Uniform
J.1.13 Weibull
J.2 Noncentral Continuous Probability Distributions
J.2.1 Chi-Square: Noncentral
J.2.2 T: Noncentral
J.2.3 F: Noncentral
J.3 Discrete Distributions
J.3.1 Discrete Uniform
J.3.2 Binomial
J.3.3 Geometric
J.3.4 Hypergeometric
J.3.5 Negative Binomial
J.3.6 Poisson
J.3.7 Signed Rank
J.3.8 Wilcoxon
J.4 Multivariate Distributions
J.4.1 Multinomial
J.4.2 Multivariate Normal
K Working Style
K.1 Text Editor
K.1.1 Requirements for an Editor
K.1.2 Choice of Editor
K.2 Types of interaction with R
K.3 Script File
K.4 Directory Structure
K.4.1 Directory Structure of This Book
K.4.2 Directory Structure for Users of This Book
K.4.3 Other User Directories
L Writing Style
L.1 Typographic Style
L.2 Graphical Presentation Style
L.2.1 Resolution
L.2.2 Aspect Ratio
L.2.3 Other Features
L.3 English Writing Style
L.4 Programming Style and Common Errors
L.5 Presentation of Results
M Accessing R Through a Powerful Editor-With Emacs and ESS as the Example
M.1 Emacs Features
M.1.1 Text Editing
M.1.2 File Comparison
M.1.3 Buffers
M.1.4 Shell Mode
M.1.5 Controlling Other Programs
M.2 ESS
M.2.1 Syntactic Indentation and Color/Font-Based Source Code Highlighting
M.2.2 Partial Code Evaluation
M.2.3 Object Name Completion
M.2.4 Process Interaction
M.2.5 Interacting with Statistical Programs on Remote Computers
M.2.6 Transcript Editing and Reuse
M.2.7 Help File Editing (R)
M.3 Learning Emacs
M.3.1 GUI (Graphical User Interface)
M.3.2 Keyboard Interface
M.4 Nuisances with Windows and Emacs
M.5 Requirements
N LaTeX
N.1 Organization Using LaTeX
N.2 Setting Equations
N.3 Coordination with R
N.4 Global Changes: Specification of Fonts
O Word Processors and Spreadsheets
O.1 Microsoft Word
O.1.1 Editing Requirements
O.1.2 SWord
O.2 Microsoft Excel
O.2.1 Database Management
O.2.2 Organizing Calculations
O.2.3 Excel as a Statistical Calculator
References
Index of Datasets
Index

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Statistical Analysis and Data Display

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