1、1.中國居民人均消費模型從總體上考察中國居民收入與消費支出的關系。表中國人均國內生產總值（GDPP）與以居民消費價格指數（1990年為100）所見的人均居民消費支出（CONSP）兩組數據。表2.1中國居民人均消費支出與人均GDP（單位:元/人）年份CONSPGDPP年份CONSPGDPP1978395.8000675.10001990797.10001602.3001979437.0000716.90001991861.40001727.2001980464.1000763.70001992966.60001949.8001981501.9000792.400019931048.6002187

2、.9001982533.5000851.100019941108.7002436.1001983572.8000931.400019951213.1002663.7001984635.60001059.20019961322.8002889.1001985716.00001185.20019971380.9003111.9001986746.50001269.60019981460.6003323.1001987788.30001393.60019991564.4003529.3001988836.40001527.00020001690.8003789.7001989779.70001565

3、.9002.1給出了1990年不變價格測算的1）建立模型，并分析結果輸出結果為：DependentVariable:CONSPMethod:LeastSquaresDate:07/02/08Time.20:13Sample:19782000Includedobservations:23VariableCoefficientStd.ErrorStatisticProbC201.1189114.8840213.512410.0000GDPP0.38618D0.00722263474710.0000R-squared0.992710Meandependentvar905.3304AdjustedRs

4、qua伯d0.992363S.D.dependent惻380.6334S.E.ofregression33,26450Akaikeinfocriterion9.929800Sumsquaredresid23237.06Schwarzcriterion10,02854Loglikelihood-112.1927F-statistic2859544Durbin-Watsonstart0.550636Prob(F-statistic)0.000000對應的模型表達式為：CONSP=201.1070.3862GDPP2（13.51）（53.47）R=0.9927,F=2859.23,DW=0.55從回

5、歸估計的結果可以看出，擬合度較好，截距項和斜率項系數均通過了t檢驗。中國人均消費增加10000元，GDP增力口3862元。2.線性回歸模型估計表2.2給出黑龍江省伊春林區1999年16個林業局的年木材采伐量和相應伐木剩余物數據。利用該數據（1）畫散點圖；（2）進行OLS回歸；（3）預測。表2.2年剩余物yt和年木材采伐量小數據林業局名年木材剩余物ytm3）年木材米伐重xtm烏伊嶺26.1361.4東風23.4948.3iw21.9751.8紅星11.5335.9五營7.1817.8上甘嶺6.8017.0友好18.4355.0翠面11.6932.7烏馬河6.8017.0美溪9.6927.3大豐7

6、.9921.5南岔12.1535.5帶嶺6.8017.0朗鄉17.2050.0桃山9.5030.0雙豐5.5213.8202.87532.00(1)畫散點圖嗑EVievsFileEditObjactiewFreeSmck口衛tionwWindowK*LpOlorkfile:CASE1-(diew|p皿|ObJst|PrintSave|DtHSampleGenerateSeri&w,.Show.GraphEmptyGroup(EditSeriesjRange:116-16obsSample：116-1BobsSeri_esStatisticsGtourStatisticsEstimateEqua

7、tion.=EstimateVAR.先輸入橫軸空量名，再輸入縱軸變量名SeriesList得放點圖2rA1012304050607D8.(2) OLS估計嚅EVievsFileEditOLjectiew?rocQuick口電tion言tfindcvrHelpampl.-Gener4teSeries.Show.GtaphEmptyGroup(EditSeri&s)forkfile:CASE1-(dSeriesStatistics丫淚向|Proc|PrintSaveDetaikRange:11616obsSample:116-1BobsGroutSt&tisticsEstimateEquation

8、.EstimiteVAR.Filter.*固S0I2cresid彈出方程設定對話框EquationEstiaationSpacificationOptionsE*pendtntvsrifcllcwtdbylistare5risscrs皿dFDLtermSjOBmexplicitequationlikeEstimationsettingsMtthod:|is-utScares(KLSandAETI三Sampie116確定得到輸出結果如圖Eie：ws-Equation:WTITLEDTorkfile:CASElCaselI1FileEditObjactViewProc：QuickOptionsWi

9、ndowKelp岫網|ProcObiectPrintNameFreezeEstimdliEForwuagt:%dt51Re51dsDependentVariable:YMethod:LeastSquaresDate:0B/28AJ8Time:18:20Sample:116Includedobservations:16VariableCoefficientStd.Errort-StstisticProbC-D.76292Q1220966062485605421X0.4042000.03337712.1126600000RSquared0.912890Meandependentvar12.6793

10、7AdjustedR-squared0,906668S.D,dependentvar6.665466S.Eofregression2.036319Akaikeinfocriterion4.37E633Sumsquaredresid5805231Schwarzcriterion4473207Loglikelihood-33.01306F-statistic146.7166DurbinWatsonstat1481946Prob(F-statistic0.000000由輸出結果可以看出，對應的回歸表達式為：?t=-0.76290.4043xt(-0.625)(12.11)R2=0.9129,F=14

11、6.7166,DW=1.48(3) x=20條件下模型的樣本外預測方法首先修改工作文件范圍感lEVie區ileEditQtjectVisw孰ideOptionsWindowHtlpF二Torkfile：CASE1-(d:ftdata2easel,vfl)匕)口RanStSample.DisplayFilter:*M吧悔應置口用白山print(5ar/eD地川計卜(5ho畫片tuh152re丘ie3Gerir5annpleStructure/ResizeCurrentFige.AppendtoCurrent?ige.ContractCurrentFage.RshapeCurrentFageC*p

12、y/ExtractfromCurrintFW告eSirtCurrentPagt.將工作文件范圍從116改為117IXDat電d-regularfrequencyVIWorkfilestructyrttypeFrequencySturtEndorkfilestructureCancel確定后將工作文件的范圍改為包括17個觀測值，然后修改樣本范圍EievsFileEditObjectViewProc英dckOgtioueWindowHelplorkfile:CASE1-(dtKttXdata2casel.rf1)目回除忡五國Jobje田Print5aveDeta依+/-5h口/|51白：e|Ger

13、ir5七m|DlgRanSanSetSajnple.bStructiore/EesizeCnirrentFags,.Append七。CwrentPage.ContractCurrentPage.ReshapeCurrentFgeCcpy/Et:tractfromCurrentSirtCurrentFage.,.將樣本范圍從116改為117Samplerangepirs(crsuiplfiobjecttocpy)1LTIFcndilion(optional)打開x的數據文件，利用Edit+/-給x的第17個觀測值賦值為20畫c0resid0y需-Eipiatian:UBTITLEDTorkfil

14、e:CASE1:CasellFileEditOtijectViewFreeQuickOptionsWindowHelp反叵密i*口ProujobjedPriiitRNam巳|FieezeE就im曰3日就911日國J叵esich|DependentVariable:YMethod:LeastSquaresDate:06/29/08Time:18:17Sample(adjusted):116Includedobservations:16afteradjuVariableCoefficient-0.76292B0.404280R-squaredAdjustedR-squaredS.E.ofregre

15、ssionSumsquaredresidLoglikelihoodDurbin-Watsonstat0.9128900.90666E2.03631958,05231-33.0130E1.431946Fath=c：Vdocijmants皿dEettingEVzhuyisimydocumentsDB=noneWF=將Forecastsample選擇區把預測范圍從117改為1717,即只預測x=20時的y的值。4GGroup:UNTITLEDTorkfile：CASE1:CaseI口回儂口均畫廊nfc.NaffieF旭詠elDeFaultv|5訊卜刖$應任北+卜新計*obsYYFYFSE17NA7.

16、3226682.145072AAIl由上圖可以知道，當x=20時，y的預測值是7.32,yf的分布標準差是2.145。3.表2.3列出了中國19782000年的參政收入Y和國內生產總值GDP的統計資料。做出散點圖，建立財政收入隨國內生產總值變化的一元線性回歸方程。表2.3年份財政收入YGDP年份財政收入YGDP19781132.2603624.10019902937.10018547.9019791146.3804038.20019913149.48021617.8019801159.9304517.80019923483.37026638.1019811175.7904862.4001993

17、4348.95034634.4019821212.3305294.70019945218.10046759.4019831366.9505934.50019956242.20058478.1019841642.8607171.00019967407.99067884.6019852004.8208964.40019978651.14074462.6019862122.01010202.2019989875.95078345.2019872199.35011962.50199911444.0882067.5019882357.24014928.30200013395.2389403.601989

18、2664.90016909.201）做散點圖:得到散點圖如下:14000120001000080004000-2000-6000-20000400006000080000100000GDPSpecificat!onOptions2）進行回歸分析:EntiationEsti&ationEquailqhspecificatlqdDependentvariablefolio-wedbylistofregressors皿4PDLterms,OR皿explicitequationlike輸出結果如下：DependentVariable:YMethod：LeastSquaresDate:07/0208Ti

19、me:20:48Sample:19782000Includedobservations:23VariableCoefficientStd.ErrorStatisticProb.C556.6477220.89432.5199730.0199GDP0.11980700D52732272298O.DODOR-squared0.960913Meandependentvar4188.627AdjustedR-squared0.950057S.D.dependentvar3613700S.E.ofregression731.2086Akaikeinfocriterion1B11022Sumsquaredr

20、esid11227986Schwarzcriterion16,20895Loglikelihood83.2675F-statistic616.3336Durbin-Watsonstat0347372Prob(F-statistic)0.000000對應的表達式是：Y=556.60.12GDP2(2.52)(22.72)R=0.96,F=516.3從上面的結果可以看出，模型的你擬合度較高，各個系數均通過了t檢驗財政收入增加10000元，GDP增加1200元。4.表2.4給出了某國19901996年間的CPI指數與S&P500指數。(1)以CPI指數為橫軸，S&P500指數為縱軸作圖；(2)做回歸

21、模型，并解釋結果。表2.4年份CPI指數S&P500旨數年份CPI指數S&P500旨數1990130.7000334.59001994148.2000460.33001991136.20003764000541.64001992140.3000415.74001996159.6000670.83001993144.5000451.4100口1307F&th=c：docuimentsandsettingVzhuyisimydocumentsB=rvoneHF=untitiedSeriesListOK得散點圖如下:6444T51(51140_io3JILD上山如2）做回歸

22、估計:EquationEstiaationSpecificationOptionsEquticnspecifietiImpendentiallefollowedtylistofregressorsandFBLterms,ORgexplicittquaticiklike確定取消得到如下結果：DependentVariable:SERD1Method:LeastSquaresDate:07AJ3/08Time:11:11Sample:19901996Includedobservations:7VariableCoefficientStdErrort-StatisticProb.C*1137.826

23、177.9488-6.394122O.DOUCPI11,08361122655590216B20.0003R-squared0.942123Meandependentvar46438B6AdjustedR-squared0.930548S.D.dependentvar112.3728S.E.ofregression29,61448Akaikeinfqcriterion9.049960Sumsquaredresid4385.086Schwarzcriterion9.833906Loglikelihood-3247276F-statistic8139039Durbin-Watson5tHi1.18

24、7041Prob(F-statistic)0.000279對應的回歸表達式為：S&P=-1137.8311.08CPI(-6.39)(9.02)回歸結果顯示，CPI指數與S&P指數正相關，斜率表示當CPI指數變化1個點，會使S&P指數變化11.08個點；截距表示當CPI指數為0是，S&P指數為-1137.83,此數據沒有明顯的經濟意義。5.表2.5給出了美國30所知名學校的MBA學生1994年基本年薪(ASP),GPA分數(從14共四個等級)，GMAT分數，以及每年學費(X)的數據。(1)用雙變量回歸模型分析GPA分數是否對ASP有影響？(2)用合適的回歸模型分析GMAT分數是否與ASP有關？

25、(3)每年的學費與ASP有關嗎？如果兩變量之間正相關，是否意味著進到最高費用的商業學校是有利的？(4)高學費的商業學校意味著高質量的MBA成績嗎？為什么表2.5學校ASP/美元GPA分數GMAT分數X/美元Harvard102630.03.400000650.000023894.00Stanford100800.03.300000665.000021189.00Columbian100480.03.300000640.000021400.00Dartmouth95410.003.400000660.000021225.00Wharton89930.003.400000650.000021050

26、.00Northwestern84640.003.300000640.000020634.00Chicago83210.003.300000650.000021656.00MIT80500.003.500000650.000021690.00Virginia74280.003.200000643.000017839.00UCLA74010.003.500000640.000014496.00Berkeley71970.003.200000647.000014361.00Cornell71970.003.200000630.000020400.00NUY70660.003.200000630.0

27、00020276.00Duke70490.003.300000623.000021910.00CarnegieMellon59890.003.200000635.000020600.00NorthCarolina69880.003.200000621.000010132.00Michigan67820.003.200000630.000020960.00Texas61890.003.300000625.00008580.000Indiana58520.003.200000615.000014036.00Purdue54720.003.200000581.00009556.000CaseWest

28、ern57200.003.100000591.000017600.00Georgetown69830.003.200000619.000019584.00MichiganState41820.003.200000590.000016057.00PennState49120.003.200000580.000011400.00SouthernMethodist60910.003.100000600.000018034.00Tulane44080.003.100000600.000019550.00Illinois47130.003.200000616.000012628.00Lowa41620.

29、003.200000590.00009361.000Minnesota48250.003.200000600.000012618.00Washington44140.003.300000617.000011436.00上述數據是個截面數據，建立數據文件過程如下:然后輸入數據即可。(1)以ASP為因變量，GPA為自變量進行回歸分析。結果如下:DependentVariable:SER01Method:LeastSquaresDate07X)3/00Time:13:D2Sample:130Includedobservations:30VariableCoefficientStdErrort-Sta

30、tisticProbC-273722.685759.31-3,1917900.0035SER02105117626347.09398972300004R-squared0.362447Meandependentvar68260.00Adjustedsquared0.339677SDdependentvar18187.78SE.ofregression1477944Akaikeinfocriterion22.10420Sumsquaredresid6.12E-HJ9Schwarzcriterion2219762Loglikelihood-329.5630FStatistic15.91789Dur

31、bin-Watsonstat1006276Prob(F-statistic)0000432從回歸結果可以看出，GPA分數的系數是顯著的，對ASP有正的影響。(2)以ASP為因變量，GMAT為自變量做回歸分析，結果如下:VariableCoefficientStdErrort-StatisticProbC-332306.S47572.09-6.9B53320.0000SER03641.6598ZB.15036842622200000R-squared0.717175Meandependentvar65260.00AdjustedR-squared0.707074S.D.dependentvar1

32、8187.78S.E.ofregression9843701Akaikeinfocriterion21.29139Sumsquaredresid2.71E瓶Schwarzcriteinon2138480Loglikelihood-317.3709F-statistic7100122Durbin-Watsonstat1.128809Prdb(F-statistiic)0000D00從回歸結果可以看出，GMAT分數與ASP是顯著正相關的(3)以ASP為因變量，X為自變量進行回歸分析，結果如下:DependentVariable:SER01Method：LeastSquaresDate:07/03/

33、08Time:13:09Sample:130Includedcbservatiors：30VariableCoefficientSid.Errort-StatistiicProb.CSER0423126.322.6334839780.86323644460.55160147742520.02520.0001R-squared0448748Meandependentvar68260.00AdjustedR-squared0429061S.D.dependentvar10107.73S.Eofregression13742.78Akaikeinfocriterion21.95376Sumsquar

34、edresid5.29E-KJ9Schwarzcriterion2205217Loglikelihood-327.3813F-statistic22.79348Durbin-Watsonstat1.142178Prob(F-statistic)0000051從回歸結果可以看出，每年的學費與ASP顯著正相關。學費高，ASP就高;但學費僅解釋了ASP變化的一部分，明顯還有其他因素影響著ASP。(4)以GPA為因變量，X為自變量進行回歸分析，結果如下:VariableCoefficientStdErrort-StatisticProb.C3J475790.07255943.379360.0000SE

35、R04617E-D6J09E-0615079520U28R-squared0.075112Meandependentvar3253333AdjustedR-squared0042080SD.dependentvar0104166SE.ofregression0,1101951Akaikeinfocriterion1.664311Sumsquaredresid0.291032Schwarzcriterion-1.570897Loglikelihood26.9E466Fstatistic2.273920Durbin-Watsonistat1.702755Prob(F-statistic)0142768從回歸結果可以看出，盡管高學費的商業學校與高質量的MBA成績略有正相關性，但學費對GPA分數的影響是不顯著的，所以學費并不是影響