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应用多元统计分析方法（附光盘1片）(影印版)

作者：
Dallas E. Johnson
定价：
43.30元
ISBN：
978-7-04-016545-6
版面字数：
650.000千字
开本：
16开
全书页数：
567页
装帧形式：
平装
重点项目：
暂无
出版时间：
2005-06-08
读者对象：
高等教育
一级分类：
数学与统计学类
二级分类：
统计学专业课
三级分类：
多元统计分析

《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。

  • 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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