Graphical Exploratory Data Analysis (Springer Texts in Statistics). Questo articolo non è disponibile.Graphical Exploratory Data Analysis (Springer Texts in Statistics)

Portraying data graphically certainly contributes toward a clearer and more penetrative understanding of data and also makes sophisticated statistical data analyses more marketable. This realization has emerged from many years of experience in teaching students, in research, and especially from engaging in statistical consulting work in a variety of subject fields. Consequently, we were somewhat surprised to discover that a comprehen­ sive, yet simple presentation of graphical exploratory techniques for the data analyst was not available. Generally books on the subject were either too incomplete, stopping at a histogram or pie chart, or were too technical and specialized and not linked to readily available computer programs. Many of these graphical techniques have furthermore only recently appeared in statis­ tical journals and are thus not easily accessible to the statistically unsophis­ ticated data analyst. This book, therefore, attempts to give a sound overview of most of the well-known and widely used methods of analyzing and portraying data graph­ ically. Throughout the book the emphasis is on exploratory techniques. Real­ izing the futility of presenting these methods without the necessary computer programs to actually perform them, we endeavored to provide working com­ puter programs in almost every case. Graphic representations are illustrated throughout by making use of real-life 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.

1 The Role of Graphics in Data Exploration.- 1. Introduction.- 2. Historical Background.- 3. Content of the Book.- 4. Central Data Sets.- 5. Different Types of Data.- 6. Computer Programs.- 2 Graphics for Univariate and Bivariate Data.- 1. Introduction.- 2. Graphics for Univariate Data.- 3. Stem-and-Leaf Plots.- 4. Graphics for Bivariate Data.- 5. Graphical Perception.- 3 Graphics for Selecting a Probability Model.- 1. Introduction.- 2. Discrete Models.- 3. Continuous Models.- 4. General.- 4 Visual Representation of Multivariate Data.- 1. Introduction.- 2. “Scatterplots” in More Than Two Dimensions.- 3. Profiles.- 4. Star Representations.- 5. Glyphs.- 6. Boxes.- 7. Andrews’ Curves.- 8. Chernoff Faces.- 9. General.- 5 Cluster Analysis.- 1. Introduction.- 2. The Probability Approach.- 3. Measures of Distance and Similarity.- 4. Hierarchical Cluster Analysis.- 5. Computer Programs for Hierarchical Cluster Analysis.- 6. Digraphs.- 7. Spanning Trees.- 8. Cluster Analysis of Variables.- 9. Application of Cluster Analysis to Fitness/Cholesterol Data.- 10. Other Graphical Techniques of Cluster Analysis.- 11. General.- 6 Multidimensional Scaling.- 1. Introduction.- 2. The Biplot.- 3. Principal Component Analysis.- 4. Correspondence Analysis.- 5. Classical (Metric) Scaling.- 6. Non-Metric Scaling.- 7. Three-Way Multidimensional Scaling (INDSCAL).- 8. Guttman’s Techniques.- 9. Facet Theory.- 10. Partial Order Scalogram Analysis.- 11. General.- 7 Graphical Representations in Regression Analysis.- 1. Introduction.- 2. The Scatterplot.- 3. Residual Plots.- 4. Mallows’ Q-Statistic.- 5. Confidence and Forecast Bands.- 6. The Ridge Trace.- 7. General.- 8 CHAID and XAID: Exploratory Techniques for Analyzing Extensive Data Sets.- 1. Introduction.- 2. CHAID—An Exploratory Technique for Analyzing Categorical Data.- 3. Applying a CHAID Analysis.- 4. XAID—An Exploratory Technique for Analyzing a Quantitative Dependent Variable with Categorical Predictors.- 5. Application of XAID Analysis.- 6. General.- 9 Control Charts.- 1. Introduction.- 2. Process Capability.- 3. Control Charts for Items with Quantitative Characteristics.- 4. Control Charts for Dichotomous Measurements (P-Chart).- 5. Cumulative Sum Charts.- 6. Cumulative Sine Charts.- 7. General.- 10 Time Series Representations.- 1. Representations in the Time Domain.- 2. Representations in the Frequency Domain.- 11 Further Useful Graphics.- 1. Graphics for the Two-Sample Problem.- 2. Graphical Techniques in Analysis of Variance.- 3. Four-Fold Circular Display of 2 x 2 Contingency Tables.- References.- Inde.