Multivariate Statistical Analysis: A Conceptual Introduction / Edition 2

Multivariate Statistical Analysis: A Conceptual Introduction / Edition 2

by Sam Kash Kachigan
     
 

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ISBN-10: 0942154916

ISBN-13: 9780942154917

Pub. Date: 07/01/1991

Publisher: Radius Press

This classic book provides the much needed conceptual explanations of advanced computer-based multivariate data analysis techniques: correlation and regression analysis, factor analysis, discrimination analysis, cluster analysis, multi-dimensional scaling, perceptual mapping, and more. It closes the gap between spiraling technology and its intelligent application,

Overview

This classic book provides the much needed conceptual explanations of advanced computer-based multivariate data analysis techniques: correlation and regression analysis, factor analysis, discrimination analysis, cluster analysis, multi-dimensional scaling, perceptual mapping, and more. It closes the gap between spiraling technology and its intelligent application, fulfilling the potential of both. 303 pp. Pub: 6/91.

Product Details

ISBN-13:
9780942154917
Publisher:
Radius Press
Publication date:
07/01/1991
Edition description:
New Edition
Pages:
303
Product dimensions:
6.16(w) x 9.02(h) x 0.72(d)

Related Subjects

Table of Contents

Chapter 1. FUNDAMENTAL CONCEPTS

1. Introduction
2. The nature of statistical analysis
3. Objects, variables, and scales
4. Frequency distributions
5. Central tendency
6. Variation
7. Association
8. Concluding comments

Chapter 2. PROBABILITY TOPICS

1. Introduction
2. Conceptualizations of probability
3. Probability experiments
4. Random variables
5. Sample spaces
6. Probability distributions
7. Composite outcomes
8. Conditional probability
9. Multiplication rule
10. Addition rule
11. Independent outcomes
12. Expected value of a random variable
13. Sampling distributions
14. Parameter estimation
15. Hypothesis testing
16. Concluding comments

Chapter 3. CORRELATION ANALYSIS

1. Introduction
2. Patterns of association
3. Gross indicators of correlation
4. The correlation coefficient r
5. Calculation of r
6. Other bivariate correlation coefficients
7. The interpretation of correlation
8. Applications of correlation analysis
9. Multivariate correlation analysis
10. The correlation matrix
11. Multiple correlation
12. Partial correlation
13. Serial correlation
14. Canonical correlation
15. Concluding comments

Chapter 4. REGRESSION ANALYSIS

1. Introduction
2. Overview of regression analysis
3. The regression line
4. The regression model
5. Accuracy of prediction
6. Significance test of the slope
7. Analysis of residual errors
8. Multiple regression
9. Importance of the predictor variables
10. Selection of predictor variables
11. Applications of regression analysis
12. Collinearity problem
13. Dummy variables
14. Autoregression
15. Regression to the mean
16. Self-fulfilling prophecy
17. Concluding comments

Chapter 5. ANALYSIS OF VARIANCE

1. Introduction
2. Overview of analysis of variance
3. The F distribution
4. One-way analysis of variance
5. Two-factor designs
6. Interaction
7. Three-factor designs
8. Other designs
9. Experimental vs. in-tact groups
10. Concluding comments

Chapter 6. DISCRIMINANT ANALYSIS

1. Introduction
2. Overview of discriminant analysis
3. The discriminant function
4. Understanding the discriminant function
5. Evaluation of the discriminant function
6. Accuracy of classification
7. Importance of the predictors
8. Discriminant vs. regression analysis
9. Concluding comments

Chapter 7. FACTOR ANALYSIS

1. Introduction
2. Overview of factor analysis
3. Applications of factor analysis
4. The input data matrix
5. The correlation matrix
6. The factor 'matrix
7. Number of factors extracted
8. Rotation of factors
9. The naming of factors
10. Summary presentation and interpretation
11. Criticisms of factor analysis
12. Concluding comments

Chapter 8. CLUSTER ANALYSIS

1. Introduction
2. Overview of cluster analysis
3. Measures of similarity
4. Cluster formation
5. Cluster comparisons
6. Hierarchical clustering
7. Concluding comments

Chapter 9. MULTIDIMENSIONAL SCALING

1. Introduction
2. One- and two-dimensional representation
3. Three-dimensional representation
4. Multidimensional representation
5. Perceptual maps
6. Stress
7. Concluding comments

APPENDIX

Statistical Tables

I. Random Digits
II. Random Normal Deviates
III. Normal Distribution
IV. Student's t Distribution
V. F Distribution
VI. Chi-Squared Distribution
VII. Correlation Coefficient (critical values)

Suggested Reading
Index

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