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Applied multivariate statistical analysis 5th ed

Applied multivariate statistical analysis 5th ed (9회 대출)

자료유형
단행본
개인저자
Johnson, Richard A. (Richard Arnold) , 1937-. Wichern, Dean W.
서명 / 저자사항
Applied multivariate statistical analysis / Richard A. Johnson, Dean W. Wichern.
판사항
5th ed.
발행사항
[Great Britain] :   Pearson Education ,   c2002.  
형태사항
xviii, 767 p. : ill. ; 24 cm + 1 CD-ROM (4 3/4 in.).
ISBN
0131219731 (pbk.)
일반주기
Previous ed.: Prentice Hall, 1998.  
서지주기
Includes bibliographical references and index.
일반주제명
Multivariate analysis.
000 00821camuu2200229u 4500
001 000045196230
005 20051005143714
008 031217s2002 enka 001 0 eng
020 ▼a 0131219731 (pbk.)
040 ▼a Uk ▼c Uk ▼d 244002
082 0 4 ▼a 519.535 ▼2 21
090 ▼a 519.535 ▼b J68a5a
100 1 ▼a Johnson, Richard A. ▼q (Richard Arnold) , ▼d 1937-.
245 1 0 ▼a Applied multivariate statistical analysis / ▼c Richard A. Johnson, Dean W. Wichern.
250 ▼a 5th ed.
260 ▼a [Great Britain] : ▼b Pearson Education , ▼c c2002.
300 ▼a xviii, 767 p. : ▼b ill. ; ▼c 24 cm + ▼e 1 CD-ROM (4 3/4 in.).
500 ▼a Previous ed.: Prentice Hall, 1998.
504 ▼a Includes bibliographical references and index.
650 0 ▼a Multivariate analysis.
700 1 ▼a Wichern, Dean W.

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No. 1 소장처 세종학술정보원/과학기술실/ 청구기호 519.535 J68a5a 등록번호 151183832 도서상태 대출가능 반납예정일 예약 서비스 C

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목차


CONTENTS
PREFACE = xv
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 = 25
  Chernoff Faces = 28
 1.5 Distance = 30
 1.6 Final Comments = 38
 Exercises = 38
 References = 48
2 MATRIX ALGEBRA AND RANDOM VECTORS = 50
 2.1 Introduction = 50
 2.2 Some Basics of Matrix and Vector Algebra = 50
  Vectors = 50
  Matrices = 55
 2.3 Positive Definite Matrices = 61
 2.4 A Square-Root Matrix = 66
 2.5 Random Vectors and Matrices = 67
 2.6 Mean Vectors and Covariance Matrices = 68
  Partitioning the Covariance Matrix = 74
  The Mean Vector and Covariance Matrix for Linear Combinations of Random Variables = 76
  Partitioning the Sample Mean Vector and Covariance Matrix = 78
 2.7 Matrix Inequalities and Maximization = 79
 Supplement 2A : Vectors and Matrices : Basic Concepts = 84
  Vectors = 84
  Matrices = 89
 Exercises = 104
 References = 111
3 SAMPLE GEOMETRY AND RANDOM SAMPLING = 112
 3.1 Introduction = 112
 3.2 The Geometry of the Sample = 112
 3.3 Random Samples and the Expected Values of the Sample Mean and Covariance Matrix = 120
 3.4 Generalized Variance = 124
  Situations in which the Generalized Sample Variance Is Zero = 130
  Generalized Variance Determined by |R| and Its Geometrical Interpretation = 136
  Another Generalization of Variance = 138
 3.5 Sample Mean, Covariance, and Correlation As Matrix Operations = 139
 3.6 Sample Values of Linear Combinations of Variables = 141
 Exercises = 145
 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 $$\bar X$$ and S = 173
  Properties of the Wishart Distribution = 174
 4.5 Large-Sample Behavior of $$\bar X$$ and S = 175
 4.6 Assessing the Assumption of Normality = 177
  Evaluating the Normality of the Univariate Marginal Distributions = 178
  Evaluating Bivariate Normality = 183
 4.7 Detecting Outliers and Cleaning Data = 189
  Steps for Detecting Outliers = 190
 4.8 Transformations To Near Normality = 194
  Transforming Multivariate Observations = 198
 Exercises = 202
 References = 209
5 INFERENCES ABOUT A MEAN VECTOR = 210
 5.1 Introduction = 210
 5.2 The Plausibility of $$μ_0$$ as a 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 = 252
 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 = 260
 References = 270
6 COMPARISONS OF SEVERAL MULTIVARIATE MEANS = 272
 6.1 Introduction = 272
 6.2 Paired Comparisons and a Repeated Measures Design = 272
  Paired Comparisons = 272
  A Repeated Measures Design for Comparing Treatments = 278
 6.3 Comparing Mean Vectors from Two Populations = 283
  Assumptions Concerning the Structure of the Data = 283
  Further Assumptions when n₁ and n₂ Are Small = 284
  Simultaneous Confidence Intervals = 287
  The Two-Sample Situation when ∑₁≠∑₂ = 290
 6.4 Comparing Several Multivariate Population Means(One-Way Manova) = 293
  Assumptions about the Structure of the Data for One-way MANOVA = 293
  A Summary of Univariate ANOVA = 293
  Multivariate Analysis of Variance(MANOVA) = 298
 6.5 Simultaneous Confidence Intervals for Treatment Effects = 305
 6.6 Two-Way Multivariate Analysis of Variance = 307
  Univariate Two-Way Fixed-Effects Model with Interaction = 307
  Multivariate Two-Way Fixed-Effects Model with Interaction = 309
 6.7 Profile Analysis = 318
 6.8 Repeated Measures Designs and Growth Curves = 323
 6.9 Perspectives and a Strategy for Analyzing Multivariate Models = 327
 Exercises = 332
 References = 352
7 MULTIVARIATE LINEAR REGRESSION MODELS = 354
 7.1 Introduction = 354
 7.2 The Classical Linear Regression Model = 354
 7.3 Least Squares Estimation = 358
  Sum-of-Squares Decomposition = 360
  Geometry of Least Squares = 361
  Sampling Properties of Classical Least Squares Estimators = 363
 7.4 Inferences About the Regression Model = 365
  Inferences Concerning the Regression Parameters = 365
  Likelihood Ratio Tests for the Regression Parameters = 370
 7.5 Inferences from the Estimated Regression Function = 374
  Estimating the Regression Function at $$z_0$$ = 374
  Forecasting a New Observation at $$z_0$$ = 375
 7.6 Model Checking and Other Aspects of Regression = 377
  Does the Model Fit? = 377
  Leverage and Influence = 380
  Additional Problems in Linear Regression = 380
 7.7 Multivariate Multiple Regression = 383
  Likelihood Ratio Tests for Regression Parameters = 392
  Other Multivariate Test Statistics = 395
  Predictions from Multivariate Multiple Regressions = 395
 7.8 The Concept of Linear Regression = 398
  Prediction of Several Variables = 403
  Partial Correlation Coefficient = 406
 7.9 Comparing the Two Formulations of the Regression Model = 407
  Mean Corrected Form of the Regression Model = 407
  Relating the Formulations = 409
 7.10 Multiple Regression Models with Time Dependent Errors = 410
 Supplement 7A : The Distribution of the Likelihood Ratio for the Multivariate Multiple Regression Model = 415
 Exercises = 417
 References = 424
8 PRINCIPAL COMPONENTS = 426
 8.1 Introduction = 426
 8.2 Population Principal Components = 426
  Principal Components Obtained from Standardized Variables = 432
  Principal Components for Covariance Matrices with Special Structures = 435
 8.3 Summarizing Sample Variation by Principal Components = 437
  The Number of Principal Components = 440
  Interpretation of the Sample Principal Components = 444
  Standardizing the Sample Principal Components = 445
 8.4 Graphing the Principal Components = 450
 8.5 Large Sample Inferences = 452
  Large Sample Properties of $$\hat \lambda_i$$ and $$\hat e_i$$ = 452
  Testing for the Equal Correlation Structure = 453
 8.6 Monitoring Quality with Principal Components = 455
  Checking a Given Set of Measurements for Stability = 455
  Controlling Future Values = 459
 Supplement 8A : The Geometry of the Sample Principal Component Approximation = 462
  The p-Dimensional Geometrical Interpretation = 464
  The n-Dimensional Geometrical Interpretation = 465
 Exercises = 466
 References = 475
9 FACTOR ANALYSIS AND INFERENCE FOR STRUCTURED COVARIANCE MATRICES = 477
 9.1 Introduction = 477
 9.2 The Orthogonal Factor Model = 478
 9.3 Methods of Estimation = 484
  The Principal Component(and Principal Factor) Method = 484
  A Modified Approach - the Principal Factor Solution = 490
  The Maximum Likelihood Method = 492
  A Large Sample Test for the Number of Common Factors = 498
 9.4 Factor Rotation = 501
  Oblique Rotations = 509
 9.5 Factor Scores = 510
  The Weighted Least Squares Method = 511
  The Regression Method = 513
 9.6 Perspectives and a Strategy for Factor Analysis = 517
 9.7 Structural Equation Models = 524
  The LISREL Model = 525
  Construction of a Path Diagram = 525
  Covariance Structure = 526
  Estimation = 527
  Model-Fitting Strategy = 529
 Supplement 9A : Some Computational Details for Maximum Likelihood Estimation = 530
  Recommended Computational Scheme = 531
  Maximum Likelihood Estimators of ρ=$$L_z$$$$L'_z$$+$$ψ_z$$ = 532
 Exercises = 533
 References = 541
10 CANONICAL CORRELATION ANALYSIS = 543
 10.1 Introduction = 543
 10.2 Canonical Variates and Canonical Correlations = 543
 10.3 Interpreting the Population Canonical Variables = 551
  Identifying the Canonical Variables = 551
  Canonical Correlations as Generalizations of other Correlation Coefficients = 553
  The First r Canonical Variables as a Summary of Variability = 554
  A Geometrical Interpretation of the Population Canonical Correlation Analysis = 555
 10.4 The Sample Canonical Variates and Sample Canonical Correlations = 556
 10.5 Additional Sample Descriptive Measures = 564
  Matrices of Errors of Approximations = 564
  Proportions of Explained Sample Variance = 567
 10.6 Large Sample Inferences = 569
 Exercises = 573
 References = 580
11 DISCRIMINATION AND CLASSIFICATION = 581
 11.1 Introduction = 581
 11.2 Separation and Classification for Two Populations = 582
 11.3 Classification with Two Multivariate Normal Populations = 590
  Classification of Normal Populations When ∑₁=∑₂=∑ = 590
  Scaling = 595
  Classification of Normal Populations When ∑₁≠∑₂ = 596
 11.4 Evaluating Classification Functions = 598
 11.5 Fisher's Discriminant Function - Separation of Populations = 609
 11.6 Classification with Several Populations = 612
  The Minimum Expected Cost of Misclassification Method = 613
  Classification with Normal Populations = 616
 11.7 Fisher's Method for Discriminating among Several Populations = 628
  Using Fisher's Discriminants to Classify Objects = 635
 11.8 Final Comments = 641
  Including Qualitative Variables = 641
  Classification Trees = 641
  Neural Networks = 644
  Selection of Variables = 645
  Testing for Group Differences = 645
  Graphics = 646
  Practical Considerations Regarding Multivariate Normality = 646
 Exercises = 647
 References = 666
12 CLUSTERING, DISTANCE METHODS, AND ORDINATION = 668
 12.1 Introduction = 668
 12.2 Similarity Measures = 670
  Distances and Similarity Coefficients for Pairs of Items = 670
  Similarities and Association Measures for Pairs of Variables = 676
  Concluding Comments on Similarity = 677
 12.3 Hierarchical Clustering Methods = 679
  Single Linkage = 681
  Complete Linkage = 685
  Average Linkage = 689
  Ward's Hierarchical Clustering Method = 690
  Final Comments - Hierarchical Procedures = 693
 12.4 Nonhierarchical Clustering Methods = 694
  K-means Method = 694
  Final Comments - Nonhierarchical Procedures = 698
 12.5 Multidimensional Scaling = 700
  The Basic Algorithm = 700
 12.6 Correspondence Analysis = 709
  Algebraic Development of Correspondence Analysis = 711
  Inertia = 718
  Interpretation in Two Dimensions = 719
  Final Comments = 719
 12.7 Biplots for Viewing Sampling Units and Variables = 719
  Constructing Biplots = 720
 12.8 Procrustes Analysis : A Method for Comparing Configurations = 723
  Constructing the Procrustes Measure of Agreement = 724
 Supplement 12A : Data Mining = 731
  Introduction = 731
  The Data Mining Process = 732
  Model Assessment = 733
 Exercises = 738
 References = 745
APPENDIX = 748
DATA INDEX = 758
SUBJECT INDEX = 761


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