Normal view

## Applied multivariate statistical analysis / Richard A. Johnson, Dean W. Wichern.

Editor: Upper Saddle River, N.J. : Prentice Hall, c2007Edición: 6th edDescripción: xviii, 773 p. : il. ; 24 cmISBN: 0131877151Tema(s): Multivariate analysisOtra clasificación: 62-01 (62H15 62H25 62H30) Recursos en línea: Página web del libro
Contenidos:
```1 ASPECTS OF MULTIVARIATE ANALYSIS [1]
1.1 Introduction [1]
1.2 Applications of Multivariate Techniques [3]
1.3 The Organization of Data [5]
Arrays, [5]
Descriptive Statistics, [6]
Graphical Techniques, [11]
1.4 Data Displays and Pictorial Representations [19]
Linking Multiple Two-Dimensional Scatter Plots, [20]
Graphs of Growth Curves, [24]
Stars, [26]
Chernoff Faces, [27]
1.5 Distance [30]
1.6 Final Comments [37]
Exercises [37]
References [47]
2 MATRIX ALGEBRA AND RANDOM VECTORS [49]
2.1 Introduction [49]
2.2 Some Basics of Matrix and Vector Algebra [49]
Vectors, [49]
Matrices, [54]
2.3 Positive Definite Matrices [60]
2.4 A Square-Root Matrix [65]
2.5 Random Vectors and Matrices [66]
2.6 Mean Vectors and Covariance Matrices [68]
Partitioning the Covariance Matrix, [73]
The Mean Vector and Covariance Matrix for Linear Combinations of Random Variables, [75]
Partitioning the Sample Mean Vector and Covariance Matrix, [77]
2.7 Matrix Inequalities and Maximization [78]
Supplement 2A: Vectors and Matrices: Basic Concepts [82]
Vectors, [82]
Matrices, [87]
Exercises [103]
References [110]
3 SAMPLE GEOMETRY AND RANDOM SAMPLING [111]
3.1 Introduction [111]
3.2 The Geometry of the Sample [111]
3.3 Random Samples and the Expected Values of the Sample Mean and Covariance Matrix [119]
3.4 Generalized Variance [123]
Situations in which the Generalized Sample Variance Is Zero, [129]
Generalized Variance Determined by | R | and Its Geometrical Interpretation, [134]
Another Generalization of Variance, [137]
3.5 Sample Mean, Covariance, and Correlation As Matrix Operations [137]
3.6 Sample Values of Linear Combinations of Variables [140]
Exercises [144]
References [148]
4 THE MULTIVARIATE NORMAL DISTRIBUTION [149]
4.1 Introduction [149]
4.2 The Multivariate Normal Density and Its Properties [149]
Additional Properties of the Multivariate Normal Distribution, [156]
4.3 Sampling from a Multivariate Normal Distribution and Maximum Likelihood Estimation [168]
The Multivariate Normal Likelihood, [168]
Maximum Likelihood Estimation of μ and ∑, [170]
Sufficient Statistics, [173]
4.4 The Sampling Distribution of X and S [173]
Properties of the Wishart Distribution, [174]
4.5 Large-Sample Behavior of X and S [175]
4.6 Assessing the Assumption of Normality [177]
Evaluating the Normality of the Univariate Marginal Distributions, [177]
Evaluating Bivariate Normality, [182]
4.7 Detecting Outliers and Cleaning Data [187]
Steps for Detecting Outliers, [189]
4.8 Transformations to Near Normality [192]
Transforming Multivariate Observations, [195]
Exercises [200]
References [208]
5 INFERENCES ABOUT A MEAN VECTOR [210]
5.1 Introduction [210]
5.2 The Plausibility of μ0 Value for a Normal Population Mean [210]
5.3 Hotelling’s T2 and Likelihood Ratio Tests [216]
General Likelihood Ratio Method, [219]
5.4 Confidence Regions and Simultaneous Comparisons of Component Means [220]
Simultaneous Confidence Statements, [223]
A Comparison of Simultaneous Confidence Intervals with One-at-a-Time Intervals, [229]
The Bonferroni Method of Multiple Comparisons, [232]
5.5 Large Sample Inferences about a Population Mean Vector [234]
5.6 Multivariate Quality Control Charts [239]
Charts for Monitoring a Sample of Individual Multivariate Observations for Stability, [241]
Control Regions for Future Individual Observations, [247]
Control Ellipse for Future Observations, [248]
T2-Chart for Future Observations, [248]
Control Charts Based on Subsample Means, [249]
Control Regions for Future Subsample Observations, [251]
5.7 Inferences about Mean Vectors when Some Observations Are Missing [251]
5.8 Difficulties Due to Time Dependence in Multivariate Observations [256]
Supplement 5A: Simultaneous Confidence Intervals and Ellipses as Shadows of the p-Dimensional Ellipsoids [258]
Exercises [261]
References [272]
6 COMPARISONS OF SEVERAL MULTIVARIATE MEANS [273]
6.1 Introduction [273]
6.2 Paired Comparisons and a Repeated Measures Design [273]
Paired Comparisons, [273]
A Repeated Measures Design for Comparing Treatments, [279]
6.3 Comparing Mean Vectors from Two Populations [284]
Assumptions Concerning the Structure of the Data, [284]
Further Assumptions When n2 and n2 Are Small, [285]
Simultaneous Confidence Intervals, [288]
The Two-Sample Situation When ∑1 ≠ ∑2, [291]
An Approximation to the Distribution of T2 for Normal Populations When Sample Sizes Are Not Large, [294]
6.4 Comparing Several Multivariate Population Means (One-Way Manova) [296]
Assumptions about the Structure of the Data for One-Way MANOVA, [296]
A Summary of Univariate A NOVA, [297]
Multivariate Analysis of Variance (MANOVA), [301]
6.5 Simultaneous Confidence Intervals for Treatment Effects [308]
6.6 Testing for Equality of Covariance Matrices [310]
6.7 Two-Way Multivariate Analysis of Variance [312]
Univariate Two-Way Fixed-Effects Model with Interaction, [312]
Multivariate Two-Way Fixed-Effects Model with Interaction, [315]
6.8 Profile Analysis [323]
6.9 Repeated Measures Designs and Growth Curves [328]
6.10 Perspectives and a Strategy for Analyzing
Multivariate Models [332]
Exercises [337]
References [358]
7 MULTIVARIATE LINEAR REGRESSION MODELS [360]
7.1 Introduction [360]
7.2 The Classical Linear Regression Model [360]
7.3 Least Squares Estimation [364]
Sum-of-Squares Decomposition, [366]
Geometry of Least Squares, [367]
Sampling Properties of Classical Least Squares Estimators, [369]
7.4 Inferences About the Regression Model [370]
Inferences Concerning the Regression Parameters, [370]
Likelihood Ratio Tests for the Regression Parameters, [374]
7.5 Inferences from the Estimated Regression Function [378]
Estimating the Regression Function at z0, [378]
Forecasting a New Observation at z0, [379]
7.6 Model Checking and Other Aspects of Regression [381]
Does the Model Fit?, [381]
Leverage and Influence, [384]
Additional Problems in Linear Regression, [384]
T.T Multivariate Multiple Regression [387]
Likelihood Ratio Tests for Regression Parameters, [395]
Other Multivariate Test Statistics, [398]
Predictions from Multivariate Multiple Regressions, [399]
7.8 The Concept of Linear Regression [401]
Prediction of Several Variables, [406]
Partial Correlation Coefficient, [409]
7.9 Comparing the Two Formulations of the Regression Model [410]
Mean Corrected Form of the Regression Model, [410]
Relating the Formulations, [412]
7.10 Multiple Regression Models with Time Dependent Errors [413]
Supplement 7A: The Distribution of the Likelihood Ratio for the Multivariate Multiple Regression Model [418]
Exercises [420]
References [428] ```
```8 PRINCIPAL COMPONENTS [430]
8.1 Introduction [430]
8.2 Population Principal Components [430]
Principal Components Obtained from Standardized Variables, [436]
Principal Components for Covariance Matrices with Special Structures, [439]
8.3 Summarizing Sample Variation by Principal Components [441]
The Number of Principal Components, [444]
Interpretation of the Sample Principal Components, [448]
Standardizing the Sample Principal Components, [449]
8.4 Graphing the Principal Components [454]
8.5 Large Sample Inferences [456]
Large Sample Properties of λi and ei, [456]
Testing for the Equal Correlation Structure, [457]
8.6 Monitoring Quality with Principal Components [459]
Checking a Given Set of Measurements for Stability, [459]
Controlling Future Values, [463]
Supplement 8A: The Geometry of the Sample Principal Component Approximation [466]
The p-Dimensional Geometrical Interpretation, [468]
The n-Dimensional Geometrical Interpretation, [469]
Exercises [470]
References [480]
9 FACTOR ANALYSIS AND INFERENCE FOR STRUCTURED COVARIANCE MATRICES [481]
9.1 Introduction [481]
9.2 The Orthogonal Factor Model [482]
9.3 Methods of Estimation [488]
The Principal Component (and Principal Factor) Method, [488]
A Modified Approach—the Principal Factor Solution, [494]
The Maximum Likelihood Method, [495]
A Large Sample Test for the Number of Common Factors, [501]
9.4 Factor Rotation [504]
Oblique Rotations, [512]
9.5 Factor Scores [513]
The Weighted Least Squares Method, [514]
The Regression Method, [516]
9.6 Perspectives and a Strategy for Factor Analysis [519]
Supplement 9A: Some Computational Details for Maximum Likelihood Estimation [527]
Recommended Computational Scheme, [528]
Maximum Likelihood Estimators of p = LZLz' + ψz [529]
Exercises [530]
References [538]
10 CANONICAL CORRELATION ANALYSIS [539]
10.1 Introduction [539]
10.2 Canonical Variates and Canonical Correlations [539]
10.3 Interpreting the Population Canonical Variables [545]
Identifying the Canonical Variables, [545]
Canonical Correlations as Generalizations of Other Correlation Coefficients, [547]
The First r Canonical Variables as a Summary of Variability, [548]
A Geometrical Interpretation of the Population Canonical Correlation Analysis [549]
10.4 The Sample Canonical Variates and Sample
Canonical Correlations [550]
10.5 Additional Sample Descriptive Measures 558 Matrices of Errors of Approximations, 558 Proportions of Explained Sample Variance, [561]
10.6 Large Sample Inferences [563]
Exercises [567]
References [574]
11 DISCRIMINATION AND CLASSIFICATION [575]
11.1 Introduction [575]
11.2 Separation and Classification for Two Populations [576]
11.3 Classification with Two Multivariate Normal Populations [584]
Classification of Normal Populations When ∑1 = ∑2 = ∑, 584 Scaling, [589]
Fisher’s Approach to Classification with Two Populations, [590]
Is Classification a Good Idea?, [592]
Classification of Normal Populations When ∑1 ≠ ∑2 ,593
11.4 Evaluating Classification Functions [596]
11.5 Classification with Several Populations [606]
The Minimum Expected Cost of Misclassification Method, 606 Classification with Normal Populations, [609]
11.6 Fisher’s Method for Discriminating among Several Populations [621]
Using Fisher’s Discriminants to Classify Objects, [628]
11.7 Logistic Regression and Classification [634]
Introduction, [634]
The Logit Model, [634]
Logistic Regression Analysis, [636]
Classification, [638]
Logistic Regression with Binomial Responses, [640]
11.8 Final Comments [644]
Including Qualitative Variables, [644]
Classification Trees, [644]
Neural Networks, [647]
Selection of Variables, [648]
Testing for Group Differences, [648]
Graphics, [649]
Practical Considerations Regarding Multivariate Normality, [649]
Exercises [650]
References [669]
12 CLUSTERING, DISTANCE METHODS, AND ORDINATION [671]
12.1 Introduction [671]
12.2 Similarity Measures [673]
Distances and Similarity Coefficients for Pairs of Items, [673]
Similarities and Association Measures for Pairs of Variables, [677]
Concluding Comments on Similarity, [678]
12.3 Hierarchical Clustering Methods [680]
Ward’s Hierarchical Clustering Method, [692]
Final Comments—Hierarchical Procedures, [695]
12.4 Nonhierarchical Clustering Methods [696]
K-means Method, [696]
Final Comments—Nonhierarchical Procedures, [701]
12.5 Clustering Based on Statistical Models [703]
12.6 Multidimensional Scaling [706]
The Basic Algorithm, [708]
12.7 Correspondence Analysis [716]
Algebraic Development of Correspondence Analysis, 718 Inertia, [725]
Interpretation in Two Dimensions, [726]
12.8 Biplots for Viewing Sampling Units and Variables [726]
Constructing Biplots, [727]
12.9 Procrustes Analysis: A Method
for Comparing Configurations [732]
Constructing the Procrustes Measure of Agreement, [733]
Supplement 12A: Data Mining [740]
Introduction, [740]
The Data Mining Process, [741]
Model Assessment, [742]
Exercises [747]
References [755]
APPENDIX [757]
DATA INDEX [764]
SUBJECT INDEX [767] --```
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Incluye referencias bibliográficas (p. 755-756) e índices.

1 ASPECTS OF MULTIVARIATE ANALYSIS [1] --
1.1 Introduction [1] --
1.2 Applications of Multivariate Techniques [3] --
1.3 The Organization of Data [5] --
Arrays, [5] --
Descriptive Statistics, [6] --
Graphical Techniques, [11] --
1.4 Data Displays and Pictorial Representations [19] --
Linking Multiple Two-Dimensional Scatter Plots, [20] --
Graphs of Growth Curves, [24] --
Stars, [26] --
Chernoff Faces, [27] --
1.5 Distance [30] --
1.6 Final Comments [37] --
Exercises [37] --
References [47] --
2 MATRIX ALGEBRA AND RANDOM VECTORS [49] --
2.1 Introduction [49] --
2.2 Some Basics of Matrix and Vector Algebra [49] --
Vectors, [49] --
Matrices, [54] --
2.3 Positive Definite Matrices [60] --
2.4 A Square-Root Matrix [65] --
2.5 Random Vectors and Matrices [66] --
2.6 Mean Vectors and Covariance Matrices [68] --
Partitioning the Covariance Matrix, [73] --
The Mean Vector and Covariance Matrix for Linear Combinations of Random Variables, [75] --
Partitioning the Sample Mean Vector and Covariance Matrix, [77] --
2.7 Matrix Inequalities and Maximization [78] --
Supplement 2A: Vectors and Matrices: Basic Concepts [82] --
Vectors, [82] --
Matrices, [87] --
Exercises [103] --
References [110] --
3 SAMPLE GEOMETRY AND RANDOM SAMPLING [111] --
3.1 Introduction [111] --
3.2 The Geometry of the Sample [111] --
3.3 Random Samples and the Expected Values of the Sample Mean and Covariance Matrix [119] --
3.4 Generalized Variance [123] --
Situations in which the Generalized Sample Variance Is Zero, [129] --
Generalized Variance Determined by | R | and Its Geometrical Interpretation, [134] --
Another Generalization of Variance, [137] --
3.5 Sample Mean, Covariance, and Correlation As Matrix Operations [137] --
3.6 Sample Values of Linear Combinations of Variables [140] --
Exercises [144] --
References [148] --
4 THE MULTIVARIATE NORMAL DISTRIBUTION [149] --
4.1 Introduction [149] --
4.2 The Multivariate Normal Density and Its Properties [149] --
Additional Properties of the Multivariate Normal Distribution, [156] --
4.3 Sampling from a Multivariate Normal Distribution and Maximum Likelihood Estimation [168] --
The Multivariate Normal Likelihood, [168] --
Maximum Likelihood Estimation of μ and ∑, [170] --
Sufficient Statistics, [173] --
4.4 The Sampling Distribution of X and S [173] --
Properties of the Wishart Distribution, [174] --
4.5 Large-Sample Behavior of X and S [175] --
4.6 Assessing the Assumption of Normality [177] --
Evaluating the Normality of the Univariate Marginal Distributions, [177] --
Evaluating Bivariate Normality, [182] --
4.7 Detecting Outliers and Cleaning Data [187] --
Steps for Detecting Outliers, [189] --
4.8 Transformations to Near Normality [192] --
Transforming Multivariate Observations, [195] --
Exercises [200] --
References [208] --
5 INFERENCES ABOUT A MEAN VECTOR [210] --
5.1 Introduction [210] --
5.2 The Plausibility of μ0 Value for a Normal Population Mean [210] --
5.3 Hotelling’s T2 and Likelihood Ratio Tests [216] --
General Likelihood Ratio Method, [219] --
5.4 Confidence Regions and Simultaneous Comparisons of Component Means [220] --
Simultaneous Confidence Statements, [223] --
A Comparison of Simultaneous Confidence Intervals with One-at-a-Time Intervals, [229] --
The Bonferroni Method of Multiple Comparisons, [232] --
5.5 Large Sample Inferences about a Population Mean Vector [234] --
5.6 Multivariate Quality Control Charts [239] --
Charts for Monitoring a Sample of Individual Multivariate Observations for Stability, [241] --
Control Regions for Future Individual Observations, [247] --
Control Ellipse for Future Observations, [248] --
T2-Chart for Future Observations, [248] --
Control Charts Based on Subsample Means, [249] --
Control Regions for Future Subsample Observations, [251] --
5.7 Inferences about Mean Vectors when Some Observations Are Missing [251] --
5.8 Difficulties Due to Time Dependence in Multivariate Observations [256] --
Supplement 5A: Simultaneous Confidence Intervals and Ellipses as Shadows of the p-Dimensional Ellipsoids [258] --
Exercises [261] --
References [272] --
6 COMPARISONS OF SEVERAL MULTIVARIATE MEANS [273] --
6.1 Introduction [273] --
6.2 Paired Comparisons and a Repeated Measures Design [273] --
Paired Comparisons, [273] --
A Repeated Measures Design for Comparing Treatments, [279] --
6.3 Comparing Mean Vectors from Two Populations [284] --
Assumptions Concerning the Structure of the Data, [284] --
Further Assumptions When n2 and n2 Are Small, [285] --
Simultaneous Confidence Intervals, [288] --
The Two-Sample Situation When ∑1 ≠ ∑2, [291] --
An Approximation to the Distribution of T2 for Normal Populations When Sample Sizes Are Not Large, [294] --
6.4 Comparing Several Multivariate Population Means (One-Way Manova) [296] --
Assumptions about the Structure of the Data for One-Way MANOVA, [296] --
A Summary of Univariate A NOVA, [297] --
Multivariate Analysis of Variance (MANOVA), [301] --
6.5 Simultaneous Confidence Intervals for Treatment Effects [308] --
6.6 Testing for Equality of Covariance Matrices [310] --
6.7 Two-Way Multivariate Analysis of Variance [312] --
Univariate Two-Way Fixed-Effects Model with Interaction, [312] --
Multivariate Two-Way Fixed-Effects Model with Interaction, [315] --
6.8 Profile Analysis [323] --
6.9 Repeated Measures Designs and Growth Curves [328] --
6.10 Perspectives and a Strategy for Analyzing --
Multivariate Models [332] --
Exercises [337] --
References [358] --
7 MULTIVARIATE LINEAR REGRESSION MODELS [360] --
7.1 Introduction [360] --
7.2 The Classical Linear Regression Model [360] --
7.3 Least Squares Estimation [364] --
Sum-of-Squares Decomposition, [366] --
Geometry of Least Squares, [367] --
Sampling Properties of Classical Least Squares Estimators, [369] --
7.4 Inferences About the Regression Model [370] --
Inferences Concerning the Regression Parameters, [370] --
Likelihood Ratio Tests for the Regression Parameters, [374] --
7.5 Inferences from the Estimated Regression Function [378] --
Estimating the Regression Function at z0, [378] --
Forecasting a New Observation at z0, [379] --
7.6 Model Checking and Other Aspects of Regression [381] --
Does the Model Fit?, [381] --
Leverage and Influence, [384] --
Additional Problems in Linear Regression, [384] --
T.T Multivariate Multiple Regression [387] --
Likelihood Ratio Tests for Regression Parameters, [395] --
Other Multivariate Test Statistics, [398] --
Predictions from Multivariate Multiple Regressions, [399] --
7.8 The Concept of Linear Regression [401] --
Prediction of Several Variables, [406] --
Partial Correlation Coefficient, [409] --
7.9 Comparing the Two Formulations of the Regression Model [410] --
Mean Corrected Form of the Regression Model, [410] --
Relating the Formulations, [412] --
7.10 Multiple Regression Models with Time Dependent Errors [413] --
Supplement 7A: The Distribution of the Likelihood Ratio for the Multivariate Multiple Regression Model [418] --
Exercises [420] --
References [428] --

8 PRINCIPAL COMPONENTS [430] --
8.1 Introduction [430] --
8.2 Population Principal Components [430] --
Principal Components Obtained from Standardized Variables, [436] --
Principal Components for Covariance Matrices with Special Structures, [439] --
8.3 Summarizing Sample Variation by Principal Components [441] --
The Number of Principal Components, [444] --
Interpretation of the Sample Principal Components, [448] --
Standardizing the Sample Principal Components, [449] --
8.4 Graphing the Principal Components [454] --
8.5 Large Sample Inferences [456] --
Large Sample Properties of λi and ei, [456] --
Testing for the Equal Correlation Structure, [457] --
8.6 Monitoring Quality with Principal Components [459] --
Checking a Given Set of Measurements for Stability, [459] --
Controlling Future Values, [463] --
Supplement 8A: The Geometry of the Sample Principal Component Approximation [466] --
The p-Dimensional Geometrical Interpretation, [468] --
The n-Dimensional Geometrical Interpretation, [469] --
Exercises [470] --
References [480] --
9 FACTOR ANALYSIS AND INFERENCE FOR STRUCTURED COVARIANCE MATRICES [481] --
9.1 Introduction [481] --
9.2 The Orthogonal Factor Model [482] --
9.3 Methods of Estimation [488] --
The Principal Component (and Principal Factor) Method, [488] --
A Modified Approach—the Principal Factor Solution, [494] --
The Maximum Likelihood Method, [495] --
A Large Sample Test for the Number of Common Factors, [501] --
9.4 Factor Rotation [504] --
Oblique Rotations, [512] --
9.5 Factor Scores [513] --
The Weighted Least Squares Method, [514] --
The Regression Method, [516] --
9.6 Perspectives and a Strategy for Factor Analysis [519] --
Supplement 9A: Some Computational Details for Maximum Likelihood Estimation [527] --
Recommended Computational Scheme, [528] --
Maximum Likelihood Estimators of p = LZLz' + ψz [529] --
Exercises [530] --
References [538] --
10 CANONICAL CORRELATION ANALYSIS [539] --
10.1 Introduction [539] --
10.2 Canonical Variates and Canonical Correlations [539] --
10.3 Interpreting the Population Canonical Variables [545] --
Identifying the Canonical Variables, [545] --
Canonical Correlations as Generalizations of Other Correlation Coefficients, [547] --
The First r Canonical Variables as a Summary of Variability, [548] --
A Geometrical Interpretation of the Population Canonical Correlation Analysis [549] --
10.4 The Sample Canonical Variates and Sample --
Canonical Correlations [550] --
10.5 Additional Sample Descriptive Measures 558 Matrices of Errors of Approximations, 558 Proportions of Explained Sample Variance, [561] --
10.6 Large Sample Inferences [563] --
Exercises [567] --
References [574] --
11 DISCRIMINATION AND CLASSIFICATION [575] --
11.1 Introduction [575] --
11.2 Separation and Classification for Two Populations [576] --
11.3 Classification with Two Multivariate Normal Populations [584] --
Classification of Normal Populations When ∑1 = ∑2 = ∑, 584 Scaling, [589] --
Fisher’s Approach to Classification with Two Populations, [590] --
Is Classification a Good Idea?, [592] --
Classification of Normal Populations When ∑1 ≠ ∑2 ,593 --
11.4 Evaluating Classification Functions [596] --
11.5 Classification with Several Populations [606] --
The Minimum Expected Cost of Misclassification Method, 606 Classification with Normal Populations, [609] --
11.6 Fisher’s Method for Discriminating among Several Populations [621] --
Using Fisher’s Discriminants to Classify Objects, [628] --
11.7 Logistic Regression and Classification [634] --
Introduction, [634] --
The Logit Model, [634] --
Logistic Regression Analysis, [636] --
Classification, [638] --
Logistic Regression with Binomial Responses, [640] --
11.8 Final Comments [644] --
Including Qualitative Variables, [644] --
Classification Trees, [644] --
Neural Networks, [647] --
Selection of Variables, [648] --
Testing for Group Differences, [648] --
Graphics, [649] --
Practical Considerations Regarding Multivariate Normality, [649] --
Exercises [650] --
References [669] --
12 CLUSTERING, DISTANCE METHODS, AND ORDINATION [671] --
12.1 Introduction [671] --
12.2 Similarity Measures [673] --
Distances and Similarity Coefficients for Pairs of Items, [673] --
Similarities and Association Measures for Pairs of Variables, [677] --
Concluding Comments on Similarity, [678] --
12.3 Hierarchical Clustering Methods [680] --
Single Linkage, [682] --
Complete Linkage, [685] --
Average Linkage, [690] --
Ward’s Hierarchical Clustering Method, [692] --
Final Comments—Hierarchical Procedures, [695] --
12.4 Nonhierarchical Clustering Methods [696] --
K-means Method, [696] --
Final Comments—Nonhierarchical Procedures, [701] --
12.5 Clustering Based on Statistical Models [703] --
12.6 Multidimensional Scaling [706] --
The Basic Algorithm, [708] --
12.7 Correspondence Analysis [716] --
Algebraic Development of Correspondence Analysis, 718 Inertia, [725] --
Interpretation in Two Dimensions, [726] --
Final Comments, [726] --
12.8 Biplots for Viewing Sampling Units and Variables [726] --
Constructing Biplots, [727] --
12.9 Procrustes Analysis: A Method --
for Comparing Configurations [732] --
Constructing the Procrustes Measure of Agreement, [733] --
Supplement 12A: Data Mining [740] --
Introduction, [740] --
The Data Mining Process, [741] --
Model Assessment, [742] --
Exercises [747] --
References [755] --
APPENDIX [757] --
DATA INDEX [764] --
SUBJECT INDEX [767] --

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