應(yīng)用線(xiàn)性回歸模型

內(nèi)容概要

本書(shū)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(xiàn)性回歸：一元預(yù)測(cè)函數(shù)的線(xiàn)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(zhuān)題，簡(jiǎn)單線(xiàn)性回歸分析中的矩陣方法；第二部分：多元線(xiàn)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(xiàn)性回歸模型Ⅰ：模型選擇及有效性，建立線(xiàn)性回歸模型Ⅱ：診斷，建立線(xiàn)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(xiàn)性回歸：非線(xiàn)性回歸和神經(jīng)網(wǎng)絡(luò)方法。本書(shū)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(pán)數(shù)據(jù)豐富。本書(shū)適用于高等院校統(tǒng)計(jì)學(xué)專(zhuān)業(yè)和理工科各專(zhuān)業(yè)本科生和研究生作為教材使用。

書(shū)籍目錄

PARTONE　SIMPLELINEARREGRESSION.　Chapter1　LinearRegressionwithOnePredictorVariable　　1.1RelationsbetweenVariables　　1.2RegressionModelsandTheirUses　　1.3SimpleLinearRegressionModelwithDistributionofErrorTermsUnspecified　　1.4DataforRegressionAnalysis　　1.5OverviewofStepsinRegressionAnalysis　　1.6EstimationofRegressionFunction　　1.7EstimationofErrorTermsVarianceσ2　　1.8NormalErrorRegressionModel　Chapter2　InferencesinRegressionandCorrelationAnalysis　　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　Chapter3　DiagnosticsandRemedialMeasures　　3.1DiagnosticsforPredictorVariable　　3.2Residuals　　3.3DiagnosticsforResiduals　　3.4OverviewofTestsInvolvingResiduals　　3.5CorrelationTestforNormality　　3.6TestsforConstancyofError　　3.7FTestforLackofFit　　3.8OverviewofRemedialMeasures　　3.9Transformations　　3.10ExplorationofShapeofRegressionFunction　　3.11CaseExample--PlutoniumMeasurement　Chapter4　SimultaneousInferencesandOtherTopicsinRegressionAnalysis　　4.1JointEstimationofβ0andβ1　　4.2SimultaneousEstimationofMeanResponses　　4.3SimultaneousPredictionIntervalsforNewObservations　　4.4RegressionthroughOrigin　　4.5EffectsofMeasurementErrors　　4.6InversePredictions　　4.7ChoiceofXLevels　Chapter5　MatrixApproachtoSimpleLinearRegressionAnalysis　　5.1Matrices　　5.2MatrixAdditionandSubtraction　　5.3MatrixMultiplication　　5.4SpecialTypesofMatrices　　5.5LinearDependenceandRankofMatrix　　5.6InverseofaMatrix　　5.7SomeBasicResultsforMatrices　　5.8RandomVectorsandMatrices　　5.9SimpleLinearRegressionModelinMatrixTerms　　5.10LeastSquaresEstimation　　5.11FittedValuesandResiduals　　5.12AnalysisofVarianceResults　　5.13InferencesinRegressionAnalysisPARTTWO　MULTIPLELINEARREGRESSION　Chapter6MultipleRegressionI　Chapter7　MultipleRegressionII　Chapter8　RegressionModelsforQuantitativeandQualitativePredictors　Chapter9　BuildingtheRegressionModelI:ModelSelectionandValidation　Chapter10　BuildingtheRegressionModelII:Diagnostics　Chapter11　BuildingtheRegressionModelIII:RemedialMeasures　Chapter12　AutocorrelationinTimeSeriesDataPARTTHREENONLINEARREGRESSION　Chapter13　IntroductiontoNonlinearRegressionandNeuralNetworks　Chapter14　LogisticRegression,PoissonRegression,andGeneralizedLinearModelsAppendixA　SomeBasicResultsinProbabilityandStatisticsAppendixB　TablesAppendixC　DataSetsAppendixD　SelectedBibliographyIndex

章節(jié)摘錄

　　The correlation test for normality described in Chapter 3 carries forward directly to multipldregression.Tbe expected values of the ordered residuals under normality are calculatedaccording to（3.6），and the coefIicient of correlation between the residuals and the expectedvalues under normality is then obtained.Table B.6 is employed to assess whether or nolthe magnitude of the correlation coeIIicient supports the reasonableness of the normalityassumption.　　The Brown-Forsythe test statistic（3.9）for assessing the constancy ofthe error variance canbe used readily in multiple regression when the error variance increases or decreases withone of the predictor variables.To conduct the Brown-Forsythe teSt.we divide the data seinto two groups，as for simple linear regression，where one group consists of cases whenthe level of the predictor variable is relatively low and the other group consists of case where the level of the predictor variable is relatively hiRh.The Brown-Forsy the test the proceeds as for simple linear regression.The Breusch.Pagan test（3.1 1）for constancy of the error variance in multiple regression icarded out exactly the same as for simple linear regression when the error variance increaseor decreases with one of the predictor variables. 2. Research and Analysis （including site visit）　 A. Base Plan PreparationB. Site Inventory （Data Collection） and Analysis （Evaluation）C. Client InterviewD. Program Development

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