Visual Statistics: Seeing Data with Dynamic Interactive Graphics

✍ Scribed by Forrest W. Young, Pedro M. Valero-Mora, Michael Friendly

• Publisher: Wiley-Interscience
• Year: 2006
• Tongue: English
• Leaves: 396
• Edition: 1
• Category: Library

✦ Synopsis

A visually intuitive approach to statistical data analysis

Visual Statistics brings the most complex and advanced statistical methods within reach of those with little statistical training by using animated graphics of the data. Using ViSta: The Visual Statistics System-developed by Forrest Young and Pedro Valero-Mora and available free of charge on the Internet-students can easily create fully interactive visualizations from relevant mathematical statistics, promoting perceptual and cognitive understanding of the data's story. An emphasis is placed on a paradigm for understanding data that is visual, intuitive, geometric, and active, rather than one that relies on convoluted logic, heavy mathematics, systems of algebraic equations, or passive acceptance of results.

A companion Web site complements the book by further demonstrating the concept of creating interactive and dynamic graphics. The book provides users with the opportunity to view the graphics in a dynamic way by illustrating how to analyze statistical data and explore the concepts of visual statistics.

Visual Statistics addresses and features the following topics:
* Why use dynamic graphics?
* A history of statistical graphics
* Visual statistics and the graphical user interface
* Visual statistics and the scientific method
* Character-based statistical interface objects
* Graphics-based statistical interfaces
* Visualization for exploring univariate data

This is an excellent textbook for undergraduate courses in data analysis and regression, for students majoring or minoring in statistics, mathematics, science, engineering, and computer science, as well as for graduate-level courses in mathematics. The book is also ideal as a reference/self-study guide for engineers, scientists, and mathematicians.

With contributions by highly regarded professionals in the field, Visual Statistics not only improves a student's understanding of statistics, but also builds confidence to overcome problems that may have previously been intimidating.

✦ Table of Contents

Cover
Title
Contents
Part 1 Introduction
1 Introduction
1.1 Visual Statistics
1.2 Dynamic Interactive Graphies
1.2.1 An Analogy
1.2.2 Why Use Dynamic Graphies?
1.2.3 The Four Respects
1.3 Three Examples
1.3.1 Nonrandom Numbers
1.3.2 Automobile Efficiency
1.3.3 Fidelity and Marriage
1.4 History of Statistical Graphics
1.4.1 1600–1699: Measurement and Theory
1.4.2 1700–1799: New Graphic Forms and Data
1.4.3 1800–1899: Modern Graphics and the Golden Age
1.4.4 1900–1950: The Dark Ages of Statistical Graphics. The Golden Age of Mathematical Statistics
1.4.5 1950–1975: Rebirth of Statistical Graphics
1.4.6 1975-2000: Statistical Graphics Comes of Age
1.5 About Software
1.5.1 XLisp-Stat
1.5.2 Commercial Systems
1.5.3 Noncommercial Systems
1.5.4 ViSta
1.6 About Data
1.6.1 Essential Characteristics
1.6.2 Datatypes
1.6.3 Datatype Examples
1.7 About This Book
1.7.1 What This Book Is and Isn't
1.7.2 Organization
1.7.3 Who Our Audience Is and Isn't
1.7.4 Comics
1.7.5 Thumb-Powered Dynamic Graphics
1.8 Visual Statistics and the Graphical User Interface
1.9 Visual Statistics and the Scientific Method
1.9.1 A Paradigm for Seeing Data
1.9.2 About Statistical Data Analysis: Visual or Otherwise
2 Examples
2.1 Random Numbers
2.2 Medical Diagnosis
2.3 Fidelity and Marriage
Part II See Data The Process
3 Interfaces and Environments
3.1 Objects
3.2 User Interfaces for Seeing Data
3.3 Character-Based Statistical Interface Objects
3.3.1 Command Line
3.3.2 Calculator
3.3.3 Program Editor
3.3.4 Report Generator
3.4 Graphics-Based Statistical Interfaces
3.4.1 Datasheets
3.4.2 Variable Window
3.4.3 Desktop
3.4.4 Workmap
3.4.5 Selector
3.5 Plots
3.5.1 Look of Plots
3.5.2 Feel of Plots
3.5.3 Impact of Plot Look and Feel
3.6 Spreadplots
3.6.1 Layout
3.6.2 Coordination
3.6.3 SpreadPlots
3.6.4 Look of Spreadplots
3.'6.5 Feel of Spreadplots
3.6.6 Look and Feel of Statistical Data Analysis
3.7 Environments for Seeing Data
3.8 Sessions and Projects
3.9 The Next Reality
3.9.1 The Fantasy
3.9.2 The Reality
3.9.3 Reality Check
4 Tools and Techniques
4.1 Types of Controls
4.1.1 Buttons
4.1.2 Palettes
4.1.3 Menus and Menu Items
4.1.4 Dialog Boxes
4.1.5 Sliders
4.1.6 Control Panels
4.1.7 The Plot Itself
4.1.8 Hyperlinking
4.2 Datasheets
4.3 Plots
4.3.1 Activating Plot Objects
4.3.2 Manipulating Plot Objects
4.3.3 Manipulating Plot Dimensions
4.3.4 Adding Graphical Elements
Part III Seeing Data Objects
5 Seeing Frequency Data
5.1 Data
5.1.1 Automobile Efficiency:
5.1.2 Berkeley Admissions Data
5.1.3 Tables of Frequency data
5.1.4 Working at the Categories Level
5.1.5 Working at the Variables Level
5.2 Frequency Plots
5.2.1 Mosaic Displays
5.2.2 Dynamic Mosaic Displays
5.3 Visual Fitting of Log-Linear Models
5.3.1 Log-Linear Spreadplot
5.3.2 Specifying Log-Linear Models and the Model Builder Window
5.3.3 Evaluating the Global Fit of Models and Their History
5.3.4 Visualizing Fitted and Residual Values with Mosaic Displays
5.3.5 Interpreting the Parameters of the Model
5.4 Conclusions
6 Seeing Univariate Data
6.1 Introduction
6.2 Data: Automobile Efficiency
6.2.1 Looking at the Numbers
6.2.2 What Can Unidimensional Methods Reveal?
6.3 Univariate Plots
6.3.1 Dotplots
6.3.2 Boxplots
6.3.3 Cumulative Distribution Plots
6.3.4 Histograms and Frequency Polygons
6.3.5 Ordered Series Plots
6.3.6 Namelists
6.4 Visualization for Exploring Univariate Data
6.5 What Do We See in MPG?
7 Seeing Bivariate Data
7.1 Introduction
7.1.1 Plots About Relationships
7.1.2 Chapter Preview
7.2 Data: Automobile Efficiency
7.2.1 What the Data Seem to Say
7.3 Bivariate Plots
7.3.1 Scatterplots
7.3.2 Distribution Comparison Plots
7.3.3 Parallel-Coordinates Plots and Parallel Boxplots
7.4 Multiple Bivariate Plots
7.4.1 Scatterplot Plot Matrix
7.4.2 Quantile Plot Matrix
7.4.3 Numerical Plot-matrix
7.4.4 BoxPlot Plot Matrix
7.5 Bivariate Visualization Methods
7.6 Visual Exploration
7.6.1 Two Bivariate Data Visualizations
7.6.2 Using These Visualizations
7.7 Visual Transformation: Box–Cox
7.7.1 The Transformation Visualization
7.7.2 Using Transformation Visualization
7.7.3 The Box–Cox Power Transformation
7.8 Visual Fitting: Simple Regression
7.9 Conclusions
8 Seeing Multivariate Data
8.1 Data: Medical Diagnosis
8.2 Three Families of Multivariate Plots
8.3 Parallel-Axes Plots
8.3.1 Parallel-Coordinates Plot
8.3.2 Parallel-Comparisons Plot
8.3.3 Parallel Univariate Plots
8.4 Orthogonal-Axes Plots
8.4.1 Spinplot
8.4.2 Orbitplot
8.4.3 BiPlot
8.4.4 Wiggle-Worm (Multivariable Comparison) Plot
8.5 Paired-Axes Plots
8.5.1 Spinplot Plot Matrix
8.5.2 Parallel-Coordinates Plot Matrix
8.6 Multivariate Visualization
8.6.1 Variable Visualization
8.6.2 Principal Components Analysis
8.6.3 Fit Visualization
8.6.4 Principal Components Visualization
8.6.5 One More Step - Discriminant Analysis
8.7 Summary
8.7.1 What Did We See? Clusters!
8.7.2 How Did We See It?
8.7.3 How Do We Interpret It? With Diagnostic Groups!
8.8 Conclusion
9 Seeing Missing Values
9.1 Introduction
9.2 Data: Sleep in Mammals
9.3 Missing Data Visualization Tools
9.3.1 Missing Values Bar Charts
9.3.2 Histograms and Bar Charts
9.3.3 Boxplots
9.3.4 Scatterplots
9.4 Visualizing Imputed Values
9.4.1 Marking the Imputed Values
9.4.2 Single Imputation
9.4.3 Multiple Imputation
9.4.4 Summary of Imputation
9.5 Missing Data Patterns
9.5.1 Patterns and Number of Cases
9.5.2 The Mechanisms Leading to Missing Data
9.5.3 Visualizing Dynamically the Patterns of Missing Data
9.6 Conclusions
References
Author Index
Subject Index