海外优秀数学类教材系列丛书 应用线性回归模型 第4版 影印版
作者： (美)库特纳等著
出版时间： 2005年版
内容简介
《应用线性回归模型（第4版影印版）》从McGrawHill出版公司引进，共分三部分，内容包括：第一部分：简单线性回归：一元预测函数的线性回归，回归影响和相关分析，诊断及补救措施，即时推断和回归分析的其它几个专题，简单线性回归分析中的矩阵方法；第二部分：多元线性回归：多元回归Ⅰ，多元回归2，定性回归模型和定量预测，建立线性回归模型Ⅰ：模型选择及有效性，建立线性回归模型Ⅱ：诊断，建立线性回归模型Ⅲ：补救措施，时间序列数据中的自相关；第三部分：非线性回归：非线性回归和神经网络方法。《应用线性回归模型（第4版影印版）》篇幅适中，例子多涉及各个应用领域，在介绍统计思想方面比较突出，光盘数据丰富。《应用线性回归模型（第4版影印版）》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教材使用。
目录
PARTONESIMPLELINEARREGRESSION.
Chapter1LinearRegressionwithOnePredictorVariable
1.1RelationsbetweenVariables
1.2RegressionModelsandTheirUses
1.3SimpleLinearRegressionModelwithDistributionofErrorTermsUnspecified
1.4DataforRegressionAnalysis
1.5OverviewofStepsinRegressionAnalysis
1.6EstimationofRegressionFunction
1.7EstimationofErrorTermsVarianceσ2
1.8NormalErrorRegressionModel
Chapter2InferencesinRegressionandCorrelationAnalysis
2.1InferencesConcerning/β1
2.2InferencesConcerning/β0
2.3SomeConsiderationsonMakingInferencesConcerning/50andβ1
2.4IntervalEstimationofE{Yh}
2.5PredictionofNewObservation
2.6ConfidenceBandforRegressionLine
2.7AnalysisofVarianceApproach
2.8GeneralLinearTestApproach
2.9DescriptiveMeasuresofLinearAssociationbetweenXandY
2.10ConsiderationsinApplyingRegressionAnalysis
2.11NormalCorrelationModels
Chapter3DiagnosticsandRemedialMeasures
3.1DiagnosticsforPredictorVariable
3.2Residuals
3.3DiagnosticsforResiduals
3.4OverviewofTestsInvolvingResiduals
3.5CorrelationTestforNormality
3.6TestsforConstancyofError
3.7FTestforLackofFit
3.8OverviewofRemedialMeasures
3.9Transformations
3.10ExplorationofShapeofRegressionFunction
3.11CaseExample--PlutoniumMeasurement
Chapter4SimultaneousInferencesandOtherTopicsinRegressionAnalysis
4.1JointEstimationofβ0andβ1
4.2SimultaneousEstimationofMeanResponses
4.3SimultaneousPredictionIntervalsforNewObservations
4.4RegressionthroughOrigin
4.5EffectsofMeasurementErrors
4.6InversePredictions
4.7ChoiceofXLevels
Chapter5MatrixApproachtoSimpleLinearRegressionAnalysis
5.1Matrices
5.2MatrixAdditionandSubtraction
5.3MatrixMultiplication
5.4SpecialTypesofMatrices
5.5LinearDependenceandRankofMatrix
5.6InverseofaMatrix
5.7SomeBasicResultsforMatrices
5.8RandomVectorsandMatrices
5.9SimpleLinearRegressionModelinMatrixTerms
5.10LeastSquaresEstimation
5.11FittedValuesandResiduals
5.12AnalysisofVarianceResults
5.13InferencesinRegressionAnalysis
PARTTWOMULTIPLELINEARREGRESSION
Chapter6MultipleRegressionI
6.1MultipleRegressionModels
6.2GeneralLinearRegressionModelinMatrixTerms
6.3EstimationofRegressionCoefficients
6.4FittedValuesandResiduals
6.5AnalysisofVarianceResults
6.6InferencesaboutRegressionParameters
6.7EstimationofMeanResponseandPredictionofNewObservation
6.8DiagnosticsandRemedialMeasures
6.9AnExample--MultipleRegressionwithTwoPredictorVariables
Chapter7MultipleRegressionII
7.1ExtraSumsofSquares
7.2UsesofExtraSumsofSquaresinTestsforRegressionCoefficients
7.3SummaryofTestsConcerningRegressionCoefficients..
7.4CoefficientsofPartialDeterminationTwoPredictorVariables
7.5StandardizedMultipleRegressionModel
7.6MulticollinearityandItsEffects
Chapter8RegressionModelsforQuantitativeandQualitativePredictors
8.1PolynomialRegressionModels
8.2InteractionRegressionModels
8.3QualitativePredictors
8.4SomeConsiderationsinUsingIndicatorVariables
8.5ModelingInteractionsbetweenQuantitativeandQualitativePredictors
8.6MoreComplexModels
8.7ComparisonofTwoorMoreRegressionFunctions
Chapter9BuildingtheRegressionModelI:ModelSelectionandValidation
9.1OverviewofModel-BuildingProcess
9.2SurgicalUnitExample
9.3CriteriaforModelSelection
9.4AutomaticSearchProceduresforModelSelection
9.5SomeFinalCommentsonAutomaticModelSelectionProcedures
9.6ModelValidation
Chapter10BuildingtheRegressionModelII:Diagnostics
10.1ModelAdequacyforaPredictorVariable---Added-VariablePlots
10.2IdentifyingOutlyingYObservations--StudentizedDeletedResiduals
10.3IdentifyingOutlyingXObservations--HatMatrixLeverageValues
10.4IdentifyingInfluentialCases--DFFITS,Cook'sDistance,andDFBETASMeasures
10.5MulticollinearityDiagnosticsVarianceInflationFactor
10.6SurgicalUnitExample---Continued
Chapter11BuildingtheRegressionModelIII:RemedialMeasures
11.1UnequalErrorVariancesRemedialMeasures--WeightedLeastSquares
11.2MulticollinearityRemedialMeasures--RidgeRegression
11.3RemedialMeasuresforInfluentialCases--RobustRegression
11.4NonparametricRegression:LowessMethodandRegressionTrees
11.5RemedialMeasuresforEvaluatingPrecisioninNonstandardSituations--Bootstrapping
11.6CaseExample--MNDOTTrafficEstimation
Chapter12AutocorrelationinTimeSeriesData
12.1ProblemsofAutocorrelation
12.2First-OrderAutoregressiveErrorModel
12.3Durbin-WatsonTestforAutocorrelation
12.4RemedialMeasuresforAutocorrelation
12.5ForecastingwithAutocorrelatedErrorTerms
PARTTHREENONLINEARREGRESSION
Chapter13IntroductiontoNonlinearRegressionandNeuralNetworks
13.1LinearandNonlinearRegressionModels
13.2LeastSquaresEstimationinNonlinearRegression
13.3ModelBuildingandDiagnostics
13.4InferencesaboutNonlinearRegressionParameters
13.5LearningCurveExample
13.6IntroductiontoNeuralNetworkModeling
Chapter14LogisticRegression,PoissonRegression,andGeneralizedLinearModels
14.1RegressionModelswithBinaryResponseVariable
14.2SigmoidalResponseFunctionsforBinaryResponses
14.3SimpleLogisticRegression
14.4MultipleLogisticRegression
14.5InferencesaboutRegressionParameters
14.6AutomaticModelSelectionMethods
14.7TestsforGoodnessofFit
14.8LogisticRegressionDiagnostics
14.9InferencesaboutMeanResponse
14.10PredictionofaNewObservation
14.11PolytomousLogisticRegressionforNominalResponse
14.12PolytomousLogisticRegressionforOrdinalResponse
14.13PoissonRegression
14.14GeneralizedLinearModels
AppendixASomeBasicResultsinProbabilityandStatistics
AppendixBTables
AppendixCDataSets
AppendixDSelectedBibliography
Index...
