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1、SIXTHEDITIONAppliedMultivariateStatisticalAnalysisRICHARDA.JOHNSONUniversityofWisconsin—MadisonDEANW.WICHERNTexasA&MUniversityPearsonEducationInternationalContentsPREFACExv1ASPECTSOFMULTIVARIATEANALYSIS11.1Introduction11.2ApplicationsofMultivariateTechniques31.3TheOrganizationofData5Arrays,
2、5DescriptiveStatistics,6GraphicalTechniques,111.4DataDisplaysandPictorialRepresentations19LinkingMultipleTwo-DimensionalScatterPlots,20GraphsofGrowthCurves,24Stars,26ChernoffFaces,271.5Distance301.6FinalComments37Exercises37References472MATRIXALGEBRAANDRANDOMVECTORS492.1Introduction492.2Som
3、eBasicsofMatrixandVectorAlgebra49Vectors,49Matrices,542.3PositiveDefiniteMatrices60**2.4ASquare-RootMatrix652.5RandomVectorsandMatrices662.6MeanVectorsandCovarianceMatrices68PartitioningtheCovarianceMatrix,73TheMeanVectorandCovarianceMatrixforLinearCombinationsofRandomVariables,75Partitioni
4、ngtheSampleMeanVectorandCovarianceMatrix,772.7MatrixInequalitiesandMaximization78viiviiiContentsSupplement2A:VectorsandMatrices:BasicConcepts82Vectors,82Matrices,87Exercises103References1103SAMPLEGEOMETRYANDRANDOMSAMPLING1113.1Introduction1113.2TheGeometryoftheSample1113.3RandomSamplesandth
5、eExpectedValuesoftheSampleMeanandCovarianceMatrix1193.4GeneralizedVariance123SituationsinwhichtheGeneralizedSampleVarianceIsZero,129GeneralizedVarianceDeterminedbyRandItsGeometricalInterpretation,134AnotherGeneralizationofVariance,1373.5SampleMean,Covariance,andCorrelationAsMatrixOperatio
6、ns1373.6SampleValuesofLinearCombinationsofVariables140Exercises144References1484THEMULTIVARIATENORMALDISTRIBUTION1494.1Introduction1494.2TheMultivariateNormalDensityandItsProperties149AdditionalPropertiesoftheMultivariateNormalDistribution,1564.3SamplingfromaMultivariateNormalDistributionan
7、dMaximumLikelihoodEstimation168TheMultivariateNormalLikelihood,168MaximumLikelihoodEstimationoffiandX,170SufficientStatistics,1734.4TheSamplingDistributionofXandS173PropertiesoftheWishartDistribution,1744.5Large-SampleBehaviorofXandS1754.6AssessingtheAss
