Multivariate Statistical Analysis: A Conceptual Introduction / Edition 2

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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.
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Editorial Reviews

Booknews
A particularly accessible introductory text for students (or researchers) without a highly technical mathematical background, first published in 1982, and expanded in 1986 under the title Statistical analysis: an interdisciplinary introduction to univariate and multivariate methods. The present edition is essentially a reissue of the 1982 text, but with an additional chapter on multidimensional scaling from the 1986 edition. Published by Radius Press, PO Box 1271, FDR Sta., New York, NY 10150. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Product Details

  • ISBN-13: 9780942154917
  • Publisher: Radius Press
  • Publication date: 7/1/1991
  • Edition description: New Edition
  • Edition number: 2
  • Pages: 303
  • Sales rank: 434,813
  • Product dimensions: 6.16 (w) x 9.02 (h) x 0.72 (d)

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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Sort by: Showing all of 2 Customer Reviews
  • Posted May 8, 2011

    more from this reviewer

    Statisics Basics Made Clear

    This is an excellent treatment of some of the most basic concepts and tests in statistics. The author writes quite clearly and takes the reader through the basic conepts behind descriptive staistics, z scores, linear regression, ANOVA, and more. Kachigan goes through just enough math and calculations to get you to the concepts and to feel confident about using and interpreting the tests. This book also makes an excellent review if you need to brush up. I recommend this book for anyone feeling less than confident with their mathematical skills or a bit overwhelmed with statistics.

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  • Anonymous

    Posted January 21, 2005

    Excellent Introduction to the subject

    This is the best introduction to the subject of statistical analysis that I have found. It starts out simple, but covers enough depth to make this the only statistics book that I use. All of my other stat books are relegated to looking up obscure details.

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