(Johnson & Wichern, 2014)

Applied Multivariate Statistical Analysis, PearsonNew International Edition (6th edition)高清原始电子文件(非Scan版) 附书签(论坛其他版本书签有误)

Johnson, R. A., & Wichern, D. W.(2014). Applied Multivariate StatisticalAnalysis, Pearson New International Edition (6th ed.): Pearson Higher EdUSA.

高清原始电子文件(非Scan版) 附书签(论坛其他版本书签有误)

Edition  6th

ISBN 9781292024943

ISBN 10 1292024941

Published 31/07/2013

Published by Pearson Higher Ed USA

Pages  776

DRAFT

(NOTE: Each chapter begins with an Introduction, andconcludes with Exercises and References.)

I. GETTING STARTED.

1.Aspects of Multivariate Analysis.

Applications of Multivariate Techniques. The Organization of Data. DataDisplays and Pictorial Representations. Distance. Final Comments.

2.Sample Geometry and Random Sampling.

The Geometry of the Sample. Random Samples and the Expected Values of theSample Mean and Covariance Matrix. Generalized Variance. Sample Mean,Covariance, and Correlation as Matrix Operations. Sample Values of LinearCombinations of Variables.

3. Matrix Algebra and Random Vectors.

Some Basics of Matrix and Vector Algebra. Positive Definite Matrices. ASquare-Root Matrix. Random Vectors and Matrices. Mean Vectors and CovarianceMatrices. Matrix Inequalities and Maximization. Supplement 2A Vectors andMatrices: Basic Concepts.  

4. TheMultivariate Normal Distribution.

The Multivariate Normal Density and Its Properties. Sampling from aMultivariate Normal Distribution and Maximum Likelihood Estimation. TheSampling Distribution of `X and S. Large-Sample Behavior of `X and S. Assessing the Assumption of Normality. DetectingOutliners and Data Cleaning. Transformations to Near Normality.

II. INFERENCES ABOUT MULTIVARIATE MEANS AND LINEARMODELS.

5.Inferences About a Mean Vector.

The Plausibility of …m0 as a Value for a Normal Population Mean.Hotelling's T 2 and Likelihood Ratio Tests. Confidence Regions and SimultaneousComparisons of Component Means. Large Sample Inferences about a Population MeanVector. Multivariate Quality Control Charts. Inferences about Mean Vectors WhenSome Observations Are Missing. Difficulties Due To Time Dependence inMultivariate Observations. Supplement 5A Simultaneous Confidence Intervals andEllipses as Shadows of the p-Dimensional Ellipsoids.

6.Comparisons of Several Multivariate Means.

Paired Comparisons and a Repeated Measures Design. Comparing MeanVectors from Two Populations. Comparison of Several Multivariate PopulationMeans (One-Way MANOVA). Simultaneous Confidence Intervals for TreatmentEffects. Two-Way Multivariate Analysis of Variance. Profile Analysis. RepealedMeasures, Designs, and Growth Curves. Perspectives and a Strategy for AnalyzingMultivariate Models.

7.Multivariate Linear Regression Models.

The Classical Linear Regression Model. Least Squares Estimation.Inferences About the Regression Model. Inferences from the Estimated RegressionFunction. Model Checking and Other Aspects of Regression. Multivariate MultipleRegression. The Concept of Linear Regression. Comparing the Two Formulations ofthe Regression Model. Multiple Regression Models with Time Dependant Errors.Supplement 7A The Distribution of the Likelihood Ratio for the MultivariateRegression Model.

III. ANALYSIS OF A COVARIANCE STRUCTURE.

8.Principal Components.

Population Principal Components. Summarizing Sample Variation byPrincipal Components. Graphing the Principal Components. Large-SampleInferences. Monitoring Quality with Principal Components. Supplement 8A TheGeometry 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. StructuralEquation Models. Supplement 9A Some Computational Details for MaximumLikelihood Estimation.

10.Canonical Correlation Analysis

Canonical Variates and Canonical Correlations. Interpreting thePopulation Canonical Variables. The Sample Canonical Variates and SampleCanonical Correlations. Additional Sample Descriptive Measures. Large SampleInferences.

IV. CLASSIFICATION AND GROUPING TECHNIQUES.

11.Discrimination and Classification.

Separation and Classification for Two Populations. Classifications withTwo Multivariate Normal Populations. Evaluating Classification Functions.Fisher's Discriminant Function…ñSeparation of Populations. Classification withSeveral Populations. Fisher's Method for Discriminating among SeveralPopulations. Final Comments.

12.Clustering, Distance Methods and Ordination.

Similarity Measures. Hierarchical Clustering Methods. NonhierarchicalClustering Methods. Multidimensional Scaling. Correspondence Analysis. Biplotsfor Viewing Sample Units and Variables. Procustes Analysis: A Method forComparing Configurations.

Appendix.

Standard Normal Probabilities. Student's t-Distribution Percentage Points. …c2Distribution Percentage Points. F-Distribution Percentage Points. F-Distribution Percentage Points (…a = .10). F-Distribution Percentage Points (…a = .05). F-Distribution Percentage Points (…a = .01).

Data Index.
Subject Index.

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