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1、???????????????????????精品資料推薦???????????????????基于GLM（廣義線性模型）的數(shù)據(jù)分析SAS里的GLM應(yīng)用在實(shí)際中比較廣泛，對(duì)數(shù)據(jù)的分析具有比較強(qiáng)的普適性。趨勢(shì)面回歸分析（TrendAnalysis）是以多元回歸分析為理論基礎(chǔ)的一種預(yù)測(cè)與統(tǒng)計(jì)技術(shù)。它用空間坐標(biāo)法進(jìn)行多項(xiàng)式回歸，從中估計(jì)出最佳的回歸模型，因此也被稱為趨勢(shì)面分析，當(dāng)不知道手中的數(shù)據(jù)呈線性還是非線性相關(guān)時(shí)，可以采用趨勢(shì)面數(shù)據(jù)分析方法，以便找出擬合數(shù)據(jù)的最佳統(tǒng)計(jì)預(yù)測(cè)模型。本文運(yùn)用GLM對(duì)一定的數(shù)據(jù)進(jìn)行GLM分析。一、數(shù)據(jù)與要
2、求此處選取15名吧不同程度的煙民的每日飲酒（啤酒）量與心電圖指標(biāo)（zb）的對(duì)應(yīng)數(shù)據(jù)。然后設(shè)法建立zb與日抽煙量（X）/支和日飲酒量（y）/升之間的關(guān)系。序號(hào)組別日抽煙量（x）/支日飲酒量（y）/升心電圖指標(biāo)（zb）113010280212511260313513330414014400514514410622012270721811210822512280922513300102231329011340144101234515420133481642514350184501535519470二、運(yùn)用GLM過(guò)程進(jìn)行趨勢(shì)面分析1.趨
3、勢(shì)分析的GLM程序databeer;inputobsnxyzb;cards;0130102800225112601???????????????????????精品資料推薦???????????????????033513330044014400054514410062012270071811210082512280092513300102313290114014410124515420134816425145018450155519470;procglm;modelzb=xy/p;procglm;modelzb=xyx*xx*
4、yy*y/p;procglm;modelzb=xyx*x*xx*x*yx*y*yy*y*y/p;procglm;modelzb=xyx*x*xx*x*yx*y*yy*y*yx*x*x*xx*x*x*yx*x*y*yx*y*y*yy*y*y*y/p;run;2.四種分析模型結(jié)果（1）一階趨勢(shì)模型DependentVariable:zb源變量自由度平方和均值F值概率值SumofSourceDFSquaresMeanSquareFValuePr>FModel290615.2099345307.60497127.19<.0001Erro
5、r124274.79007356.23251CorrectedTotal1494890.00000R-SquareCoeffVarRootMSEzbMean0.9549505.43922818.87412347.000---------------------------------------------------------------------------------------------------------------------------------SourceDFTypeISSMeanSquareFVal
6、uePr>Fx189541.5655889541.56558251.36<.0001y11073.644351073.644353.010.1081---------------------------------------------------------------------------------------------------------------------------------SourceDFTypeIIISSMeanSquareFValuePr>F2???????????????????????精品資
7、料推薦???????????????????x114652.2435114652.2435141.13<.0001y11073.644351073.644353.010.1081---------------------------------------------------------------------------------------------------------------------------------StandardParameterEstimateErrortValuePr>
8、t
9、Interce
10、pt64.0499938033.065399191.940.0766x5.383855650.839475676.41<.0001y6.941998693.998720781.740.1081ObservationObservedPredictedResidua
