应用多元统计分析方法

图书标准信息

• 作者 [美]约翰逊 著
• 出版社 高等教育出版社
• 出版时间 2005-06
• 版次 1
• ISBN 9787040165456
• 定价 43.30元
• 装帧 平装
• 开本 16开
• 纸张 胶版纸
• 页数 567页
• 字数 650千字
• 正文语种 简体中文

【内容简介】

《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。

【目录】

1.APPLIEDMULTIVARIATEMETHODS
1.1AnOverviewofMultivariateMethods1
Variable-andIndividual-DirectedTechniques2
CreatingNewVariables2
PrincipalComponentsAnalysis3
FactorAnalysis3
DiscriminantAnalysis4
CanonicalDiscriminantAnalysis5
LogisticRegression5
ClusterAnalysis5
MultivariateAnalysisofVariance6
CanonicalVariatesAnalysis7
CanonicalCorrelationAnalysis7
WheretoFindthePrecedingTopics7
1.2TwoExamples8
IndependenceofExperimentalUnits11
1.3TypesofVariablesU
1.4DataMatricesandVectors12
VariableNotation13
DataMatrix13
DataVectors13
DataSubscripts14
1.5TheMultivariateNormalDistribution15
SomeDefinitions15
SummarizingMultivariateDistributions16
MeanVectorsandVariance-CovarianceMatrices16
CorrelationsandCorrelationMatrices17
TheMultivariateNormalProbabilityDensityFunction19
BivariateNormalDistributions19
1.6StatisticalComputing22
CautionsAboutComputerUsage22
MissingValues22
ReplacingMissingValuesbyZeros23
ReplacingMissingValuesbyAverages23
RemovingRowsoftheDataMatrix23
SamplingStrategies24
DataEntryErrorsandDataVerification24
1.7MultivariateOutliers25
LocatingOutliers25
DealingwithOutliers25
OutliersMayBeInfluential26
1.8MultivariateSummaryStatistics26
1.9StandardizedDataand/orZScores27
Exercises28

2.SAMPLECORRELATIONS
2.1StatisticalTestsandConfidenceIntervals35
AretheCorrelationsLargeEnoughtoBeUseful?36
ConfidenceIntervalsbytheChartMethod36
ConfidenceIntervalsbyFishersApproximation38
ConfidenceIntervalsbyRubensApproximation39
VariableGroupingsBasedonCorrelations40
RelationshiptoFactorAnalysis46
2.2Summary46
Exercises47

3.MULTIVARIATEDATAPLOTS
3.1Three-DimensionalDataPlots55
3.2PlotsofHigherDimensionalData59
ChernoffFaces61
StarPlotsandSun-RayPlots63
AndrewsPlots65
Side-by-SideScatterPlots66
3.3PlottingtoCheckforMultivariateNormality67
Summary73
Exercises73

4.EIGENVALUESANDEIGENVECTORS
4.1TraceandDeterminant77
Examples78
4.2Eigenvalues78
4.3Eigenvectors79
PositiveDefiniteandPositiveSemidefiniteMatrices80
4.4GeometricDescriptions(p=2)82
Vectors82
BivariateNormalDistributions83
4.5GeometricDescriptions(p=3)87
Vectors87
TrivariateNormalDistributions87
4.6GeometricDescriptions(p>3)90
Summary91
Exercises91

5.PRINCIPALCOMPONENTSANALYSIS
5.1ReasonsforUsingPrincipalComponentsAnalysis93
DataScreening93
Clustering95
DiscriminantAnalysis95
Regression95
5.2ObjectivesofPrincipalComponentsAnalysis96
5.3PrincipalComponentsAnalysisontheVariance-Covariance
Matrix96
PrincipalComponentScores98
ComponentLoadingVectors98
5.4EstimationofPrincipalComponents99
EstimationofPrincipalComponentScores99
5.5DeterminingtheNumberofPrincipalComponents99
Method1100
Method2100
5.6Caveats107
5.7PCAontheCorrelationMatrixP109
PrincipalComponentScores110
ComponentCorrelationVectors110
SampleCorrelationMatrix110
DeterminingtheNumberofPrincipalComponents110
5.8TestingforIndependenceoftheOriginalVariables111
5.9StructuralRelationships111
5.10StatisticalComputingPackages112
SASRPRINCOMPProcedure112
PrincipalComponentsAnalysisUsingFactorAnalysis
Programs118
PCAwithSPSSsFACTORProcedure124
Summary142
Exercises142

6.FACTORANALYSIS
6.1ObjectivesofFactorAnalysis147
6.2Caveats148
6.3SomeHistoryofFactorAnalysis148
6.4TheFactorAnalysisModel150
Assumptions150
MatrixFormoftheFactorAnalysisModel151
DefinitionsofFactorAnalysisTerminology151
6.5FactorAnalysisEquations151
NonuniquenessoftheFactors152
6.6SolvingtheFactorAnalysisEquations153
……
7.DISCRIMINANTANALYSIS
8.LOGISTICREGRESSIONMETHODS
9.CLUSTERANALYSIS
10.MEANVECTORSANDVARIANCE-COVARIANCEMATRICES
11.MULTIVARIATEANALYSISOFVARIANCE
12.PREDICTIONMODELSANDMULTIVARIATEREGRESSION
APPENDIXA:MATRIXRESULTS
APPENDIXB:WORKATTITUDESSURVEY
APPENDIXC:FAMILYCONTROLSTUDY
REFERENCES
INDEX