應(yīng)用線性回歸模型

定　價(jià)：￥60.00

作　者：	美Michael H.Kutner等著
出版社：	高等教育出版社
叢編項(xiàng)：	海外優(yōu)秀數(shù)學(xué)類教材系列叢書
標(biāo)　簽：	暫缺

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ISBN：	9787040163803	出版時(shí)間：	2005-02-01	包裝：	平裝
開本：	24cm＋光盤1片	頁(yè)數(shù)：	701	字?jǐn)?shù)：

內(nèi)容簡(jiǎn)介

　　《應(yīng)用線性回歸模型（第4版影印版）》從McGrawHill出版公司引進(jìn)，共分三部分，內(nèi)容包括：第一部分：簡(jiǎn)單線性回歸：一元預(yù)測(cè)函數(shù)的線性回歸，回歸影響和相關(guān)分析，診斷及補(bǔ)救措施，即時(shí)推斷和回歸分析的其它幾個(gè)專題，簡(jiǎn)單線性回歸分析中的矩陣方法；第二部分：多元線性回歸：多元回歸Ⅰ，多元回歸2，定性回歸模型和定量預(yù)測(cè)，建立線性回歸模型Ⅰ：模型選擇及有效性，建立線性回歸模型Ⅱ：診斷，建立線性回歸模型Ⅲ：補(bǔ)救措施，時(shí)間序列數(shù)據(jù)中的自相關(guān)；第三部分：非線性回歸：非線性回歸和神經(jīng)網(wǎng)絡(luò)方法?！稇?yīng)用線性回歸模型（第4版影印版）》篇幅適中，例子多涉及各個(gè)應(yīng)用領(lǐng)域，在介紹統(tǒng)計(jì)思想方面比較突出，光盤數(shù)據(jù)豐富。《應(yīng)用線性回歸模型（第4版影印版）》適用于高等院校統(tǒng)計(jì)學(xué)專業(yè)和理工科各專業(yè)本科生和研究生作為教材使用。

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圖書目錄

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...