应用多元统计分析方法（附光盘1片）(影印版)

《应用多元统计分析方法-影印版》设有大量的例题与练习题，实用面丰富，统计思维清晰。《应用多元统计分析方法-影印版》适用于高等院校统计学专业和理工科各专业本科生和研究生作为双语教材使用。应用多元回归分析方法，样本相关，多元数据点图，特征值和特征向量，复合分析原理，因子分析，判别分析，逻辑斯谛回归方法，聚类分析，均值向量和方差-协方差矩阵，方差多元分析，预测模型和多元回归。《应用多元统计分析方法-影印版》统计内容覆盖面广于国内的概率统计教材，内容安排颇有新意，例如，在处理回归分析时，强调了从建模的观点与需要来考虑。

目录
- 1. APPLIED MULTIVARIATE METHODS
  - 1.1 An Overview of Multivariate Methods
    - Variable-and Individual-Directed Techniques
    - Creating New Variables
    - Principal Components Analysis
    - Factor Analysis
    - Discriminant Analysis
    - Canonical Discriminant Analysis
    - Logistic Regression
    - Cluster Analysis
    - Multivariate Analysis of Variance
    - Canonical Variates Analysis
    - Canonical Correlation Analysis
    - Where to Find the Preceding Topics
  - 1.2 Two Examples
    - Independence of Experimental Units
  - 1.3 Types of Variables
  - 1.4 Data Matrices and Vectors
    - Variable Notation
    - Data Matrix
    - Data Vectors
    - Data Subscripts
  - 1.5 The Multivariate Normal Distribution
    - Some Definitions
    - Summarizing Multivariate Distributions
    - Mean Vectors and Variance-Covariance Matrices
    - Correlations and Correlation Matrices
    - The Multivariate Normal Probability Density Function
    - Bivariate Normal Distributions
  - 1.6 Statistical Computing
    - Cautions About Computer Usage
    - Missing Values
    - Replacing Missing Values by Zeros
    - Replacing Missing Values by Averages
    - Removing Rows of the Data Matrix
    - Sampling Strategies
    - Data Entry Errors and Data Verification
  - 1.7 Multivariate Outliers Locating Outliers Dealing with Outliers Outliers May Be Influential
  - 1.8 Multivariate Summary Statistics
  - 1.9 Standardized Data and/or Z Scores
    - Exercises
- 2. SAMPLE CORRELATIONS
  - 2.1 Statistical Tests and Confidence Intervals
    - Are the Correlations Large Enough to Be Useful?
    - Confidence Intervals by the Chart Method
    - Confidence Intervals by Fisher's Approximation
    - Confidence Intervals by Ruben's Approximation
    - Variable Groupings Based on Correlations
    - Relationship to Factor Analysis
  - 2.2 Summary
    - Exercises
- 3. MULTIVARIATE DATA PLOTS
  - 3.1 Three-Dimensional Data Plots
  - 3.2 Plots of Higher Dimensional Data
    - Chernoff Faces
    - Star Plots and Sun-Ray Plots
    - Andrews' Plots
    - Side-by-Side Scatter Plots
  - 3.3 Plotting to Check for Multivariate Normality
    - Summary
    - Exercises
- 4. EIGENVALUES AND EIGENVECTORS
  - 4.1 Trace and Determinant
    - Examples
  - 4.2 Eigenvalues
  - 4.3 Eigenvectors
    - Positive Definite and Positive Semidefinite Matrices
  - 4.4 Geometric Descriptions (p = 2)
    - Vectors
    - Bivariate Normal Distributions
  - 4.5 Geometric Descriptions (p = 3)
    - Vectors
    - Trivariate Normal Distributions
  - 4.6 Geometric Descriptions (p > 3)
    - Summary
    - Exercises
- 5. PRINCIPAL COMPONENTS ANALYSIS
  - 5.1 Reasons for Using Principal Components Analysis
    - Data Screening
    - Clustering
    - Discriminant Analysis
    - Regression
  - 5.2 Objectives of Principal Components Analysis
  - 5.3 Principal Components Analysis on the Variance-Covariance Matrix Σ
    - Principal Component Scores
    - Component Loading Vectors
  - 5.4 Estimation of Principal Components
    - Estimation of Principal Component Scores
  - 5.5 Determining the Number of Principal Components
    - Method 1
    - Method 2
  - 5.6 Caveats
  - 5.7 PCA on the Correlation Matrix P
    - Principal Component Scores
    - Component Correlation Vectors
    - Sample Correlation Matrix
    - Determining the Number of Principal Components
  - 5.8 Testing for Independence of the Original Variables
  - 5.9 Structural Relationships
  - 5.10 Statistical Computing Packages
    - SASR PRINCOMP Procedure
    - Principal Components Analysis Using Factor Analysis
    - Programs
    - PCA with SPSS's FACTOR Procedure
    - Summary
    - Exercises
- 6. FACTOR ANALYSIS
  - 6.1 Objectives of Factor Analysis
  - 6.2 Caveats
  - 6.3 Some History of Factor Analysis
  - 6.4 The Factor Analysis Model
    - Assumptions
    - Matrix Form of the Factor Analysis Model
    - Definitions of Factor Analysis Terminology
  - 6.5 Factor Analysis Equations
    - Nonuniqueness of the Factors
  - 6.6 Solving the Factor Analysis Equations
  - 6.7 Choosing the Appropriate Number of Factors
    - Subjective Criteria
    - Objective Criteria
  - 6.8 Computer Solutions of the Factor Analysis Equations
    - Principal Factor Method on R
    - Principal Factor Method with Iteration
  - 6.9 Rotating Factors
    - Examples (m = 2)
    - Rotation Methods
    - The Varimax Rotation Method
  - 6.10 Oblique Rotation Methods
  - 6.11 Factor Scores
    - Bartlett's Method or the Weighted Least-Squares Method
    - Thompson's Method or the Regression Method
    - Ad Hoc Methods
    - Summary
    - Exercises
- 7. DISCRIMINANT ANALYSIS
  - 7.1 Discrimination for Two Multivariate Normal Populations
    - A Likelihood Rule
    - The Linear Discriminant Function Rule
    - A Mahalanobis Distance Rule
    - A Posterior Probability Rule
    - Sample Discriminant Rules
    - Estimating Probabilities of Misclassification
    - Resubstitution Estimates
    - Estimates from Holdout Data
    - Cross-Validation Estimates
  - 7.2 Cost Functions and Prior Probabilities (Two Populations)
  - 7.3 A General Discriminant Rule (Two Populations)
    - A Cost Function
    - Prior Probabilities
    - Average Cost of Misclassification
    - A Bayes Rule
    - Classification Functions
    - Unequal Covariance Matrices
    - Tricking Computing Packages
  - 7.4 Discriminant Rules (More than Two Populations)
    - Basic Discrimination
  - 7.5 Variable Selection Procedures
    - Forward Selection Procedure
    - Backward Elimination Procedure
    - Stepwise Selection Procedure
    - Recommendations
    - Caveats
  - 7.6 Canonical Discriminant Functions
    - The First Canonical Function
    - A Second Canonical Function
    - Determining the Dimensionality of the Canonical Space
    - Discriminant Analysis with Categorical Predictor Variables
  - 7.7 Nearest Neighbor Discriminant Analysis
  - 7.8 Classification Trees
    - Summary
    - Exercises
- 8. LOGISTIC REGRESSION METHODS
  - 8.1 Logistic Regression Model
  - 8.2 The Logit Transformation
    - Model Fitting
  - 8.3 Variable Selection Methods
  - 8.4 Logistic Discriminant Analysis (More Than Two Populations)
    - Logistic Regression Models
    - Model Fitting
    - Another SAS LOGISTIC Analysis
    - Exercises
- 9. CLUSTER ANALYSIS
  - 9.1 Measures of Similarity and Dissimilarity
    - Ruler Distance
    - Standardized Ruler Distance
    - A Mahalanobis Distance
    - Dissimilarity Measures
  - 9.2 Graphical Aids in Clustering
    - Scatter Plots
    - Using Principal Components
    - Andrews' Plots
    - Other Methods
  - 9.3 Clustering Methods
    - Nonhierarchical Clustering Methods
    - Hierarchical Clustering
    - Nearest Neighbor Method
    - A Hierarchical Tree Diagram
    - Other Hierarchical Clustering Methods
    - Comparisons of Clustering Methods
    - Verification of Clustering Methods
    - How Many Clusters?
    - Beale's F-Type Statistic
    - A Pseudo Hotelling's T2 Test
    - The Cubic Clustering Criterion
    - Clustering Order
    - Estimating the Number of Clusters
    - Principal Components Plots
    - Clustering with SPSS
    - SAS's FASTCLUS Procedure
  - 9.4 Multidimensional Scaling
    - Exercises
- 10. MEAN VECTORS AND VARIANCE-COVARIANCE MATRICES
  - 10.1 Inference Procedures for Variance-Covariance Matrices
    - A Test for a Specific Variance-Covariance Matrix
    - A Test for SphericityA Test for Compound Symmetry
    - A Test for the Huynh-Feldt Conditions
    - A Test for Independence
    - A Test for Independence of Subsets of Variables
    - A Test for the Equality of Several Variance-Covariance
    - Matrices
  - 10.2 Inference Procedures for a Mean Vector
    - Hotelling's T2 Statistic
    - Hypothesis Test for μ
    - Confidence Region for μ
    - A More General Result
    - Special Case—A Test of Symmetry
    - A Test for Linear Trend
    - Fitting a Line to Repeated Measures
    - Multivariate Quality Control
  - 10.3 Two Sample Procedures
    - Repeated Measures Experiments
  - 10.4 Profile Analyses
  - 10.5 Additional Two-Group Analyses
    - Paired Samples
    - Unequal Variance-Covariance Matrices
    - Large Sample Sizes
    - Small Sample Sizes
    - Summary
    - Exercises
- 11. MULTIVARIATE ANALYSIS OF VARIANCE
  - 11.1 MANOVA
    - MANOVA Assumptions
    - Test Statistics
    - Test Comparisons
    - Why Do We Use MANOVAs?
    - A Conservative Approach to Multiple Comparisons
  - 11.2 Dimensionality of the Alternative Hypothesis
  - 11.3 Canonical Variates Analysis
    - The First Canonical Variate
    - The Second Canonical Variate
    - Other Canonical Variates
  - 11.4 Confidence Regions for Canonical Variates
    - Summary
    - Exercises
- 12. PREDICTION MODELS AND MULTIVARIATE REGRESSION
  - 12.1 Multiple Regression
  - 12.2 Canonical Correlation Analysis
    - Two Sets of Variables
    - The First Canonical Correlation
    - The Second Canonical Correlation
    - Number of Canonical Correlations
    - Estimates
    - Hypothesis Tests on the Canonical Correlations
    - Interpreting Canonical Functions
    - Canonical Correlation Analysis with SPSS
  - 12.3 Factor Analysis and Regression
    - Summary
    - Exercises
- APPENDIX A: MATRIX RESULTS
  - A.1 Basic Definitions and Rules of Matrix Algebra
  - A.2 Quadratic Forms
  - A.3 Eigenvalues and Eigenvectors
  - A.4 Distances and Angles
  - A.5 Miscellaneous Results
- APPENDIX B: WORK ATTITUDES SURVEY
  - B.1 Data File Structure
  - B.2 SPSS Data Entry Commands
  - B.3 SAS Data Entry Commands
- APPENDIX C: FAMILY CONTROL STUDY
- REFERENCES
- Index

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应用多元统计分析方法（附光盘1片）(影印版)

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