Visual Statistics Seeing Data with Dynamic Interactive Graphics

ISBN-10: 0471681601

ISBN-13: 9780471681601

Edition: 2006

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Description: Visual statistics accomplishes the goal of bringing the most complex and advanced statistical methods within the reach of those with little statistical training by using animated graphics of the data. This text shows how to make dynamic visualizations tht are fully interactive and respond instantly to the user's nudges and prods. The graphics are created from relevant mathematical statistics and the interactive presentation of dynamic graphics promotes perceptual and cognitive understanding of the data's story.

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Book details

List price: $142.00
Copyright year: 2006
Publisher: John Wiley & Sons, Incorporated
Publication date: 8/4/2006
Binding: Hardcover
Pages: 364
Size: 6.50" wide x 9.50" long x 0.75" tall
Weight: 1.496
Language: English

The late FORREST W. YOUNG, PhD, was Professor Emeritus of Quantitative Psychology at The University of North Carolina at Chapel Hill. As a result of a ten-year research project, Dr. Young and his students created ViSta: The Visual Statistics System. He acted as a professional consultant for the SAS Institute, Inc., Statistical Sciences, Inc., and Bell Telephone Laboratories. He authored three books and numerous journal articles. He received his PhD in psychometrics from the University of Southern California, Los Angeles.PEDRO M. VALERO-MORA, PhD, is Professor of Data Processing at the University of Valencia in Spain. He is the author of several research papers. He received his PhD in methodology in the behavioral sciences from the University of Valencia in Spain.MICHAEL FRIENDLY, PhD, is Professor in the Department of Psychology at York University in Toronto, Ontario, Canada. He received his PhD in psychometrics and cognitive psychology from Princeton University. He is the author of two books and numerous research papers.

Introduction
Introduction
Visual Statistics
Dynamic Interactive Graphics
An Analogy
Why Use Dynamic Graphics?
The Four Respects
Three Examples
Nonrandom Numbers
Automobile Efficiency
Fidelity and Marriage
History of Statistical Graphics
1600-1699: Measurement and Theory
1700-1799: New Graphic Forms and Data
1800-1899: Modern Graphics and the Golden Age
1900-1950: The Dark Ages of Statistical Graphics-The Golden Age of Mathematical Statistics
1950-1975: Rebirth of Statistical Graphics
1975-2000: Statistical Graphics Comes of Age
About Software
XLisp-Stat
Commercial Systems
Noncommercial Systems
ViSta
About Data
Essential Characteristics
Datatypes
Datatype Examples
About This Book
What This Book Is-and Isn't
Organization
Who Our Audience Is-and Isn't
Comics
Thumb-Powered Dynamic Graphics
Visual Statistics and the Graphical User Interface
Visual Statistics and the Scientific Method
A Paradigm for Seeing Data
About Statistical Data Analysis: Visual or Otherwise
Examples
Random Numbers
Medical Diagnosis
Fidelity and Marriage
See Data-The Process
Interfaces and Environments
Objects
User Interfaces for Seeing Data
Character-Based Statistical Interface Objects
Command Line
Calculator
Program Editor
Report Generator
Graphics-Based Statistical Interfaces
Datasheets
Variable Window
Desktop
Workmap
Selector
Plots
Look of Plots
Feel of Plots
Impact of Plot Look and Feel
Spreadplots
Layout
Coordination
SpreadPlots
Look of Spreadplots
Feel of Spreadplots
Look and Feel of Statistical Data Analysis
Environments for Seeing Data
Sessions and Projects
The Next Reality
The Fantasy
The Reality
Reality Check
Tools and Techniques
Types of Controls
Buttons
Palettes
Menus and Menu Items
Dialog Boxes
Sliders
Control Panels
The Plot Itself
Hyperlinking
Datasheets
Plots
Activating Plot Objects
Manipulating Plot Objects
Manipulating Plot Dimensions
Adding Graphical Elements
Seeing Data-Objects
Seeing Frequency Data
Data
Automobile Efficiency
Berkeley Admissions Data
Tables of Frequency data
Working at the Categories Level
Working at the Variables Level
Frequency Plots
Mosaic Displays
Dynamic Mosaic Displays
Visual Fitting of Log-Linear Models
Log-Linear Spreadplot
Specifying Log-Linear Models and the Model Builder Window
Evaluating the Global Fit of Models and Their History
Visualizing Fitted and Residual Values with Mosaic Displays
Interpreting the Parameters of the Model
Conclusions
Seeing Univariate Data
Introduction
Data: Automobile Efficiency
Looking at the Numbers
What Can Unidimensional Methods Reveal?
Univariate Plots
Dotplots
Boxplots
Cumulative Distribution Plots
Histograms and Frequency Polygons
Ordered Series Plots
Namelists
Visualization for Exploring Univariate Data
What Do We See in MPG?
Seeing Bivariate Data
Introduction
Plots About Relationships
Chapter Preview
Data: Automobile Efficiency
What the Data Seem to Say
Bivariate Plots
Scatterplots
Distribution Comparison Plots
Parallel-Coordinates Plots and Parallel Boxplots
Multiple Bivariate Plots
Scatterplot Plot Matrix
Quantile Plot Matrix
Numerical Plot-matrix
BoxPlot Plot Matrix
Bivariate Visualization Methods
Visual Exploration
Two Bivariate Data Visualizations
Using These Visualizations
Visual Transformation: Box-Cox
The Transformation Visualization
Using Transformation Visualization
The Box-Cox Power Transformation
Visual Fitting: Simple Regression
Conclusions
Seeing Multivariate Data
Data: Medical Diagnosis
Three Families of Multivariate Plots
Parallel-Axes Plots
Parallel-Coordinates Plot
Parallel-Comparisons Plot
Parallel Univariate Plots
Orthogonal-Axes Plots
Spinplot
Orbitplot
BiPlot
Wiggle-Worm (Multivariable Comparison) Plot
Paired-Axes Plots
Spinplot Plot Matrix
Parallel-Coordinates Plot Matrix
Multivariate Visualization
Variable Visualization
Principal Components Analysis
Fit Visualization
Principal Components Visualization
One More Step - Discriminant Analysis
Summary
What Did We See? Clusters!
How Did We See It?
How Do We Interpret It? With Diagnostic Groups!
Conclusion
Seeing Missing Values
Introduction
Data: Sleep in Mammals
Missing Data Visualization Tools
Missing Values Bar Charts
Histograms and Bar Charts
Boxplots
Scatterplots
Visualizing Imputed Values
Marking the Imputed Values
Single Imputation
Multiple Imputation
Summary of Imputation
Missing Data Patterns
Patterns and Number of Cases
The Mechanisms Leading to Missing Data
Visualizing Dynamically the Patterns of Missing Data
Conclusions
References
Author Index
Subject Index
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