Applied multivariate statistical analysis /
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| Author / Creator: | Johnson, Richard A. (Richard Arnold), 1937- |
|---|---|
| Imprint: | Englewood Cliffs, N.J. : Prentice-Hall, c1982. |
| Description: | xiii, 594 p. : ill. ; 25 cm. |
| Language: | English |
| Subject: | |
| Format: | Print Book |
| URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/518122 |
Table of Contents:
- I. Getting Started
- 1. Aspects of Multivariate Analysis
- Applications of Multivariate Techniques
- The Organization of Data
- Data Displays and Pictorial Representations
- Distance
- Final Comments
- 2. Matrix Algebra and Random Vectors
- Some Basics of Matrix and Vector Algebra
- Positive Definite Matrices
- A Square-Root Matrix
- Random Vectors and Matrices
- Mean Vectors and Covariance Matrices
- Matrix Inequalities and Maximization
- Supplement 2. A Vectors and Matrices: Basic Concepts
- 3. Sample Geometry and Random Sampling
- The Geometry of the Sample
- Random Samples and the Expected Values of the Sample Mean and Covariance Matrix
- Generalized Variance
- Sample Mean, Covariance, and Correlation as Matrix Operations
- Sample Values of Linear Combinations of Variables
- 4. The Multivariate Normal Distribution
- The Multivariate Normal Density and Its Properties
- Sampling from a Multivariate Normal Distribution and Maximum Likelihood Estimation
- The Sampling Distribution of 'X and S
- Large-Sample Behavior of 'X and S
- Assessing the Assumption of Normality
- Detecting Outliners and Data Cleaning
- Transformations to Near Normality
- II. Inferences About Multivariate Means And Linear Models
- 5. Inferences About a Mean Vector
- The Plausibility of âÇ m0 as a Value for a Normal Population Mean
- Hotelling's T
- 2. and Likelihood Ratio Tests
- Confidence Regions and Simultaneous Comparisons of Component Means
- Large Sample Inferences about a Population Mean Vector
- Multivariate Quality Control Charts
- Inferences about Mean Vectors When Some Observations Are Missing
- Difficulties Due To Time Dependence in Multivariate Observations
- Supplement 5. A Simultaneous Confidence Intervals and Ellipses as Shadows of the p-Dimensional Ellipsoids
- 6. Comparisons of Several Multivariate Means
- Paired Comparisons and a Repeated Measures Design
- Comparing Mean Vectors from Two Populations
- Comparison of Several Multivariate Population Means (One-Way MANOVA)
- Simultaneous Confidence Intervals for Treatment Effects
- Two-Way Multivariate Analysis of Variance
- Profile Analysis
- Repealed Measures, Designs, and Growth Curves
- Perspectives and a Strategy for Analyzing Multivariate Models
- 7. Multivariate Linear Regression Models
- The Classical Linear Regression Model
- Least Squares Estimation
- Inferences About the Regression Model
- Inferences from the Estimated Regression Function
- Model Checking and Other Aspects of Regression
- Multivariate Multiple Regression
- The Concept of Linear Regression
- Comparing the Two Formulations of the Regression Model
- Multiple Regression Models with Time Dependant Errors
- Supplement 7. A The Distribution of the Likelihood Ratio for the Multivariate Regression Model
- III. Analysis Of A Covariance Structure
- 8. Principal Components
- Population Principal Components
- Summarizing Sample Variation by Principal Components
- Graphing the Principal Components
- Large-Sample Inferences
- Monitoring Quality with Principal Components
- Supplement 8. A The Geometry of the Sample Principal Component Approximation
- 9. Factor Analysis and Inference for Structured Covariance Matrices
- The Orthogonal Factor Model
- Methods of Estimation
- Factor Rotation
- Factor Scores
- Perspectives and a Strategy for Factor Analysis
- Structural Equation Models
- Supplement 9. A Some Computational Details for Maximum Likelihood Estimation
- 10. Canonical Correlation Analysis
- Canonical Variates and Canonical Correlations
- Interpreting the Population Canonical Variables
- The Sample Canonical Variates and Sample Canonical Correlations
- Additional Sample Descriptive Measures
- Large Sample Inferences
- IV. Classification And Grouping Techniques
- 11. Discrimination and Classification
- Separation and Classification for Two Populations
- Classifications with Two Multivariate Normal Populations
- Evaluating Classification Functions
- Fisher's Discriminant FunctionâÇ Ã±Separation of Populations
- Classification with Several Populations
- Fisher's Method for Discriminating among Several Populations
- Final Comments
- 12. Clustering, Distance Methods and Ordination
- Similarity Measures
- Hierarchical Clustering Methods
- Nonhierarchical Clustering Methods
- Multidimensional Scaling
- Correspondence Analysis
- Biplots for Viewing Sample Units and Variables
- Procustes Analysis: A Method for Comparing Configurations
- Appendix
- Standard Normal Probabilities
- Student's t-Distribution Percentage Points
- âÇ c2 Distribution Percentage Points
- F-Distribution Percentage Points
- F-Distribution Percentage Points (âÇ a = .10)
- F-Distribution Percentage Points (âÇ a = .05)
- F-Distribution Percentage Points (âÇ a = .01)
- Data Index
- Subject Index
