Springer, 1986. — 321 p. — ISBN: 1461293715, 9781461293712, e-ISBN: 9781461249504.
With 180 Graphical Representations.
This book, therefore, attempts to give a sound overview of most of the wellknown and widely used methods of analyzing and portraying data graph ically. Throughout the book the emphasis is on exploratory techniques. Realizing the futility of presenting these methods without the necessary computer programs to actually perform them, we endeavored to provide working computer programs in almost every case. Graphic representations are illustrated throughout by making use of reallife data. Two such data sets are frequently used throughout the text. In realizing the aims set out above we avoided intricate theoretical derivations and explanations but we nevertheless are convinced that this book will be of inestimable value even to a trained statistician.
Contents:
Preface.
The Role of Graphics in Data Exploration.
Introduction.
Historical Background.
Content of the Book.
Central Data Sets.
Different Types of Data.
Computer Programs.
Graphics for Univariate and Bivariate Data.
Introduction.
Graphics for Univariate Data.
Stem-and-Leaf Plots.
Graphics for Bivariate Data.
Graphical Perception.
Graphics for Selecting a Probability Model.
Introduction.
Discrete Models.
Continuous Models.
General.
Visual Representation of Multivariate Data.
Introduction.
"Scatterplots" in More Than Two Dimensions.
Profiles.
Star Representations.
Glyphs.
Boxes.
Andrews' Curves.
Chernoff Faces.
General.
Cluster Analysis.
Introduction.
The Probability Approach.
Measures of Distance and Similarity.
Hierarchical Cluster Analysis.
Computer Programs for Hierarchical Cluster Analysis.
Digraphs.
Spanning Trees.
Cluster Analysis of Variables.
Application of Cluster Analysis to Fitness/Cholesterol Data.
Other Graphical Techniques of Cluster Analysis.
General.
Multidimensional Scaling.
Introduction.
The Biplot.
Principal Component Analysis.
Correspondence Analysis.
Classical (Metric) Scaling.
Non-Metric Scaling.
Three-Way Multidimensional Scaling (INDSCAL).
Guttman's Techniques.
Facet Theory.
Partial Order Scalogram Analysis.
General.
Graphical Representations in Regression Analysis.
Introduction.
The Scatterplot.
Residual Plots.
Mallows' Ck-Statistic.
Confidence and Forecast Bands.
The Ridge Trace.
General.
CHAID and XAID: Exploratory Techniques for Analyzing Extensive Data Sets.
Introduction.
CHAID-An Exploratory Technique for Analyzing Categorical Data.
Applying a CHAID Analysis.
XAID-An Exploratory Technique for Analyzing a Quantitative.
Dependent Variable with Categorical Predictors.
Application ofXAID Analysis.
General.
Control Charts.
Introduction.
Process Capability.
Control Charts for Items with Quantitative Characteristics.
Control Charts for Dichotomous Measurements (P-Chart).
Cumulative Sum Charts.
Cumulative Sine Charts.
General.
Time Series Representations.
Representations in the Time Domain.
Representations in the Frequency Domain.
Further Useful Graphics.
Graphics for the Two-Sample Problem.
Graphical Techniques in Analysis of Variance.
Four-Fold Circular Display of 2 x 2 Contingency Tables.
References.
Index.