应用回归分析例82SAS程序

本文格式为Word版，下载可任意编辑——应用回归分析例82SAS程序例8.21,建立数据集dataa;

inputyearyx1x2x3x4x5x6x7x8x9x10x11x12x13@@;cards;

199711355.5313.8816074.14227.0311511.41559.83

2036.9574.40725.762820.9651173.816630.710894.29978.9319981167013.3216100232.7911863.675422171539.3774430105360017194.031155910737.8

19991239313.6416000251.9812539.245672356580.1476632515730017419.791242612109.78

20001355613.841630027213101.486572427667.8883431865970018352.21285013146

202314808.0214.7216395.87303.2915554.25760.682696.3787.96914.373383.0166103.9920964.1215163.416067.61

20231654015.516700326.6117084.68503050.4877.971033.1537917250023445.5618236.619251.59

202319105.7518.3516959.98350.1521366.68983.583371.2945.271133.563881.3186208.1127702.622233.624108.01202322033.0921.2317587.33414.626830.991291.343928.91041.121334.74804.8296681.9937026.1728291.131975.72202325002.623.5018135.29493.234375.191450.544544.701239.981421.085177.86106884.7940210.2435324377712625.2918476.57585.5341245.191742.965033.201511.781560.035345.05123676.4846574.7041914.9046893.36202332815.5326.9218631.82692.4047651.632068.175412.601759.291765.005824.98136117.2553918.0748928.8056560.87202334957.6128.0219043.06802.9947824.422170.925098.001926.011854.606028.05142355.7359890.3950305.8060460.29202337146.5129.7318948.96852.6955283.462393.465960.901832.371944.776385.01164397.7858574.0757218.2069405.40;run;

procprintdata=a;run;

2进行相关分析

proccorrdata=anoprob;varyx1-x13;run;Pearson相关系数,N=13yy1.00000x0.919231x0.928743x0.939086x0.949564x0.959866x0.968657x0.979299x0.989744x0.998993x0.9196707x0.9x10.992311.000000.993480.973310.992560.991560.989740.978960.992680.991870.990640.9x20.987430.993481.000000.965250.983230.984260.985140.981830.990620.990390.979850.9x30.990860.973310.965251.000000.987420.992100.960940.987240.981380.970390.987130.9x40.995640.992560.983230.987421.000000.997080.988270.985570.989840.985960.994730.9x50.998660.991560.984260.992100.997081.000000.984560.990700.995180.987980.996000.9x60.986570.989740.985140.960940.988270.984561.000000.970970.989240.991480.988910.9x70.992990.978960.981830.987240.985570.990700.970971.000000.988860.975440.982250.9x80.997440.992680.990620.981380.989840.995180.989240.988861.000000.994240.994080.9x90.989930.991870.990390.970390.985960.987980.991480.975440.994241.000000.987690.9x10x11x12x130.996770.990640.979850.987130.994730.996000.988910.982250.994080.987691.000000.90.995650.993010.991410.985080.990560.995110.978730.991400.994740.989710.987841.00.997710.993840.985620.988930.999440.998310.989090.987580.993350.989380.996420.90.997830.989340.979160.994310.997250.999050.982540.987710.992560.985650.997230.9Pearson相关系数,N=13y195615x0.9197721x0.9197833x193010.993840.98934x291410.985620.97916x385080.988930.99431x490560.999440.99725x595110.998310.99905x678730.989090.98254x791400.987580.98771x894740.993350.99256x989710.989380.98565x10x11x12x1387840.996420.9972300000.993130.9922893131.000000.9983992280.998391.00000可见数据存在多重共线性3,逐步回归法进行回归procregdata=a;

modely=x1-x13/selection=stepwise;run;

方差分析源模型误差自由度57平方和均方F值Pr>F10381852832076370576234.78F1.320.28895.060.05926.280.04067.690.027620.200.00283.950.0873Intercept-511.85733446.0617043852x3x4x7x8x10

4.991860.093153.114487.650640.047182.218440.037171.122931.702320.023741686212092062561846726621314724,偏最小二乘法建立回归方程

procstandarddata=aout=out1mean=0std=1;varyx1-x13;run;

procplsdata=out1nfac=3details;modely=x1-x13/solution;run;ModelEffectWeightsNumx1berofExtractedFactors1x2x3x4x5x6x7x8x9x10x11x12x13InnerRegressionCoefficients0.20.20.20.20.20.20.20.20.20.20.20.20.20.27769755765778786753771783762781778784784864225621954952215565969573266-0.-0.0.5-0.0.1-0.0.40.1-0.0.10.0-0.0.10.11342368312097144374509096342677043038784599966535493015996488943339860684795-0.0.0-0.-0.-0.0.30.30.60.00.3-0.-0.-0.0.14125407306481173095154009630549132281181879041378449455700572226223533500878923如上表,我们得到结果tk与y*的回归方程

ParameterEstimates聽yIntercept-.0000000000x1x2x30.00267843960.02283348430.1182505437ParameterEstimates聽x4x5x6x7x8x9x10x11x12x13

回归方程:

y-.00994990980.0705926287