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2019-12-13 18:13 來源：京東作者：京東

海外優(yōu)秀數學類教材系列叢書：應用多元統(tǒng)計分析方法（影印版）（附光盤）

內容簡介: 《應用多元統(tǒng)計分析方法-影印版》設有大量的例題與練習題，實用面豐富，統(tǒng)計思維清晰?！稇枚嘣y(tǒng)計分析方法-影印版》適用于高等院校統(tǒng)計學專業(yè)和理工科各專業(yè)本科生和研究生作為雙語教材使用。應用多元回歸分析方法，樣本相關，多元數據點圖，特征值和特征向量，復合分析原理，因子分析，判別分析，邏輯斯諦回歸方法，聚類分析，均值向量和方差-協(xié)方差矩陣，方差多元分析，預測模型和多元回歸?！稇枚嘣y(tǒng)計分析方法-影印版》統(tǒng)計內容覆蓋面廣于國內的概率統(tǒng)計教材，內容安排頗有新意，例如，在處理回歸分析時，強調了從建模的觀點與需要來考慮。

目錄:1.APPLIED MULTIVARIATE METHODS 1.1 An Overview of Multivariate Methods 1 Variable-and Individual-Directed Techniques 2 Creating New Variables 2 Principal Components Analysis 3 Factor Analysis 3 Discriminant Analysis 4 Canonical Discriminant Analysis 5 Logistic Regression 5 Cluster Analysis 5 Multivariate Analysis of Variance 6 Canonical Variates Analysis 7 Canonical Correlation Analysis 7 Where to Find the Preceding Topics 7 1.2 Two Examples 8 Independence of Experimental Units 11 1.3 Types of Variables U 1.4 Data Matrices and Vectors 12 Variable Notation 13 Data Matrix 13 Data Vectors 13 Data Subscripts 14 1.5 The Multivariate Normal Distribution 15 Some Definitions 15 Summarizing Multivariate Distributions 16 Mean Vectors and Variance-Covariance Matrices 16 Correlations and Correlation Matrices 17 The Multivariate Normal Probability Density Function 19 Bivariate Normal Distributions 19 1.6 Statistical Computing 22 Cautions About Computer Usage 22 Missing Values 22 Replacing Missing Values by Zeros 23 Replacing Missing Values by Averages 23 Removing Rows of the Data Matrix 23 Sampling Strategies 24 Data Entry Errors and Data Verification 24 1.7 Multivariate Outliers 25 Locating Outliers 25 Dealing with Outliers 25 Outliers May Be Influential 26 1.8 Multivariate Summary Statistics 26 1.9 Standardized Data and/or Z Scores 27 Exercises 28 2.SAMPLE CORRELATIONS 2.1 Statistical Tests and Confidence Intervals 35 Are the Correlations Large Enough to Be Useful? 36 Confidence Intervals by the Chart Method 36 Confidence Intervals by Fishers Approximation 38 Confidence Intervals by Rubens Approximation 39 Variable Groupings Based on Correlations 40 Relationship to Factor Analysis 46 2.2 Summary 46 Exercises 47 3.MULTIVARIATE DATA PLOTS 3.1 Three-Dimensional Data Plots 55 3.2 Plots of Higher Dimensional Data 59 Chernoff Faces 61 Star Plots and Sun-Ray Plots 63 Andrews Plots 65 Side-by-Side Scatter Plots 66 3.3 Plotting to Check for Multivariate Normality 67 Summary 73 Exercises 73 4.EIGENVALUES AND EIGENVECTORS 4.1 Trace and Determinant 77 Examples 78 4.2 Eigenvalues 78 4.3 Eigenvectors 79 Positive Definite and Positive Semidefinite Matrices 80 4.4 Geometric Descriptions (p = 2) 82 Vectors 82 Bivariate Normal Distributions 83 4.5 Geometric Descriptions (p = 3) 87 Vectors 87 Trivariate Normal Distributions 87 4.6 Geometric Descriptions (p > 3) 90 Summary 91 Exercises 91 5.PRINCIPAL COMPONENTS ANALYSIS 5.1 Reasons for Using Principal Components Analysis 93 Data Screening 93 Clustering 95 Discriminant Analysis 95 Regression 95 5.2 Objectives of Principal Components Analysis 96 5.3 Principal Components Analysis on the Variance-Covariance Matrix 96 Principal Component Scores 98 Component Loading Vectors 98 5.4 Estimation of Principal Components 99 Estimation of Principal Component Scores 99 5.5 Determining the Number of Principal Components 99 Method 1 100 Method 2 100 5.6 Caveats 107 5.7 PCA on the Correlation Matrix P 109 Principal Component Scores 110 Component Correlation Vectors 110 Sample Correlation Matrix 110 Determining the Number of Principal Components 110 5.8 Testing for Independence of the Original Variables 111 5.9 Structural Relationships 111 5.10 Statistical Computing Packages 112 SASR PRINCOMP Procedure 112 Principal Components Analysis Using Factor Analysis Programs 118 PCA with SPSSs FACTOR Procedure 124 Summary 142 Exercises 142 6. FACTOR ANALYSIS 6.1 Objectives of Factor Analysis 147 6.2 Caveats 148 6.3 Some History of Factor Analysis 148 6.4 The Factor Analysis Model 150 Assumptions 150 Matrix Form of the Factor Analysis Model 151 Definitions of Factor Analysis Terminology 151 6.5 Factor Analysis Equations 151 Nonuniqueness of the Factors 152 6.6 Solving the Factor Analysis Equations 153 …… 7.DISCRIMINANT ANALYSIS 8.LOGISTIC REGRESSION METHODS 9.CLUSTER ANALYSIS 10.MEAN VECTORS AND VARIANCE-COVARIANCE MATRICES 11.MULTIVARIATE ANALYSIS OF VARIANCE 12.PREDICTION MODELS AND MULTIVARIATE REGRESSION APPENDIX A:MATRIX RESULTS APPENDIX B:WORK ATTITUDES SURVEY APPENDIX C:FAMILY CONTROL STUDY REFERENCES INDEX

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