Title:
Applied multivariate statistical analysis
Personal Author:
Edition:
3rd ed.
Publication Information:
New York : Prentice Hall, 1992
ISBN:
9780130418074
Subject Term:
Added Author:
Available:*
Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
|---|---|---|---|---|---|
Searching... | 30000000154124 | QA278 J63 1992 | Open Access Book | Book | Searching... |
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Summary
Summary
An updated edition, this book provides explanations of the results needed to understand output from the standard multivariate analysis computer packages and prepares readers to make proper interpretations, select appropriate techniques and understand their strengths and weaknesses.
Author Notes
Richard A. Johnson is the curator of the Sports Museum of New England & the author of "Young at Heart: The Story of Johnny Kelly." He lives in Boston, Massachusetts.
(Bowker Author Biography)
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 |
