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Graphical Exploratory Data Analysis

Author: S H C Toit; A G W Steyn; R H Stumpf
Publisher: New York, NY : Springer New York, 1986.
Series: Springer texts in statistics.
Edition/Format:   eBook : Bibliographic data : EnglishView all editions and formats
Summary:
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  Read more...
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Details

Genre/Form: Electronic books
Additional Physical Format: Printed edition:
Material Type: Bibliographic data, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: S H C Toit; A G W Steyn; R H Stumpf
ISBN: 9781461249504 1461249503 9781461293712 1461293715
OCLC Number: 840279850
Description: 1 online resource (IX, 314 pages).
Contents: 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.
Series Title: Springer texts in statistics.
Responsibility: by S.H.C. Toit, A.G.W. Steyn, R.H. Stumpf.

Abstract:

Portraying data graphically certainly contributes toward a clearer and more penetrative understanding of data and also makes sophisticated statistical data analyses more marketable. This book,  Read more...

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