农村居民家庭人均纯收入影响因素分析报告

1、班级：商学院. :学号：指导教师：完成时间：年 月曰农村居民家庭人均纯收入影响因素分析摘要：随着我国工业化与城市化建设的发展，农村问题越来越凸显， 留守问题、看病问题、养老问题等，农民收入问题亦是国家各界人士 十分关注的问题。本文旨在用计量经济学方法简单分析农村居民家庭 人均纯收入的影响因素。关键字：农村居民家庭人均纯收入 财政年度支农支出 农业机械 总动力 农作物播种总面积 乡村就业人数 乡村人口数第一产业总产值正文：一、引言国家“十二五”规划第六章拓宽农民增收渠道中明确提出：加 大引导和扶持力度，提高农民职业技能和创收能力，千方百计拓宽农 民增收渠道，促进农民收入持续较快增长。同时“十二五

2、”规划中明 确提出以下几点：1、稳定粮食播种面积、优化品种结构、提高单产和品质。2、健全农业补贴制度，坚持对种粮农民实行直接补贴，继续实行良 种补贴和农机具购置补贴，完善农资综合补贴动态调整机制。3、推进农业技术集成化、劳动过程机械化、生产经营信息化。结合 这几方面，本文从第一产业总产值、财政年度支农支出、农业机械总 动力等几个方面分析其对农村居民家庭人均纯收入的影响。二、预设模型令农村居民家庭人均纯收入丫（元）为被解释变量，农作物播种 总面积X1（千公顷）、乡村就业人数X2 （万人）、乡村人口数X3（万 人）、第一产业总产值X4 （亿元）、财政年度支农支出X5 （亿元） 农业机械总动力X6（

3、万千瓦）为解释变量，据此建立回归模型。三、数据搜集从中国统计年鉴得到如下数据:年度农村居农作物乡村就乡村人第一产财政年农业机民家庭播种总业人数口数X3业总产度支农械总动人均纯 收入Y （元）面积X1（千公 顷）X2（万 人）（万人）值 X4（亿元）支出X5（亿元）力X6（万千瓦）1990686.3148362.347708841385062221.7628707.71991708.6149585.848026846205342.2243.5529388.61992784149007.148291849965866.6269.0430308.41993921.6147740.7485468534

4、46963.763323.4231816.619941221148240.648802856819572.695399.733802.519951577.7149879.3490258594712135.81430.2236118.0519961926.1152380.6490288508514015.39510.0738546.919972090.1153969.2490398417714441.89560.7742015.619982162155705.7490218315314817.63626.0245207.7119992210.3156372.8489828203814770.03

5、677.4648996.1220002253.4156299.8489348083714944.72766.8952573.6120012366.4155707.9486747956315781.27917.9655172.120022475.6154635.5481217824116537.021102.757929.8520032622.2152415475067685117381.71134.8660386.52420042936.4153552.469717570521412.71693.7964027.953120053254.9155487.4625874544224201792.

6、468397.875200635871521494534673160240402161.3572522.120074140.4153463.4436871496286273404.776589.6920084760.6156265.4346170399337024544.0182190.4720095153.2158613.4250668938352266720.4187496.1520105919160674.8414186711340533.68129.5892410.4四、建立模型1、散点图分析2、单因素或多变量间关系分析 Group: U树TITLE D Workfi le: UIST

7、ITTLECXU ntitl ed| 口 I B 1 Z3ViewObjectj Rrir| Name F寸FFt Wmtu SpecIrCorrelation MatrixYX1X2X3X4X5X6Y1.QODDOOO.80D987-O.004B91-D.S6D23SD.59B239D.923B20D. 975356X109009371 000000-3.520B63-3.6823273.7830560.&35B661772623X2X).884B910 物 6631.0000000.918402-3.891367-1S60B63-1864062K3-0 S5D238刀 6823?70 91

8、34021 DDOOOO据 M5376682731-3 961893X40 99B2390.7S3B560.891367-3.H53761 0000001&274SS0.972 翊M50 &23B200 695666-0 95DB63-D 88271D笠斑81 ooooooD B71277XS0 5793560.772623-3.964052-3.991D933.&T2&2416712771.000000L=Ulf由散点图分析和变量间关系分析可以看出被解释变量农村居民家庭人均纯收入丫（元）与解释变量农作物播种总面积X1、 乡 村 就业人数X2、乡村人口数X3、第一产业总产值X4、财政年度支农支

9、 出X5、农业机械总动力X6呈线性关系，因此该回归模型设为：Y=B+B 1X1+B 2&+B 3X3+8 4X4+8 5X5+8 6X6+ 口3、模型预模拟用Eviews做OLS回归分析得: Equation: UNTITLED Workfile: UNTITLEDUntitled| = | 回 | S3 |vie叫叩11亦时司月哙司 EsummtE | 5tats |Dependent Variable: YMethod: Least Squaresate: 06/03/12 Time: 21:58Sample: 1990 2010Included observations: 20Varia

10、bleCoefficientStd. Errort-StatisticProb.C-6067.3551991.962-3.0459340.0094X10.0202910.0114631.7701310 1001-0 0803230.066175-1.2213430.24360.071S620.0439411.630B670 12690 0936500.01S051S.2155320 00000.0029620.0430460.0638200.94620.0367970.0153282 4006210.0320R-squared0.998843Mean dependent var2626.790

11、Adjusted R-squared0.9963083.D. dependent var14B1.92OS.E. of regression60.94368Akaike info criteri&r11.32716Sumi squared rsid48291 64Schwarz criterion11 67566Log likelihood-106.2716F-statistic1869.907Durbin-Watson stat2.060182Prob(F-statisti：)C.000000Y=-6067.355+0.02029X1-0.08082X2+0.07165X3+0.09355X

12、4+0.002962X5+0.03680X6(-3.04593) (1.77013)(-1.2213 )(1.6307)(6.2155)(0.06882)(2.4006)R 2=0.99882=0.9983F=1869.907D.W.=2.0602五、模型检验1、计量经济学意义检验 （1）多重共线性检验与解决求相关矩阵得到: Group: UNTTLED Workfile: UMTTTUDXUrrthlad| 回S3 |Vi&xjFr匹 QbjectJ grhtn F=如 打ebI* S卜己匕| 5姑白| Sp丘匚 |coirralatlan MatrixYXIX2X3皿15X6Y1 0000

13、000.900937-3 664B91-3.&bD2393.9902393.&23D203.57935GX1O.80D9671.00DDOO-0.62BB63-0.682327D.783B560.6S5B66D.772B23X2-0 994B91-3 52BG631 0000003.910402-3.891367-3.&5DB63-3.064052X3-Q.J5D238-Q.6823270.91B4021.QODDOO-D.945376-D.S 32791-D.581B93X4O.990E3907830560.091367-3.94537S1.0000003.3274330.9 茂 9241X

14、50.923S200.695666-0L96DB63-D.882791D.9274S31.0QDDOOD.871277X60 打33580 宓 623-0 064052-3 931093。9莅也I 871771 000000nrJ卜发现模型存在多重共线性。接下来运用逐步回归法对模型进行修正:将各个解释变量分别加入模型，进行一元回归：作Y与X1的回归，结果如下：Dependent Variable: Y Method: Least Squares ate: 06/03/12 Time: 22:16Sample: 1990 2010Included observations: 21Variable

15、CoefficientStJ. Error t-StatisticProb.C4S679.0G854&.810-5.6038320.0000X10.3334620.06S7175.904943o.oaooR-squared0.&S3408Mean dependent var2559.348Adjusted R.-squared0,635167S.D. dependent var147.614S.E. of re-gression891 89EDAkaike info criterion16一51497Sum squared resi-d15114052S-chwarz criterion1S.

16、61445Log likelihood-171 4072F-statist g35.81954urbin-Watso-n stat0.319004ProtjfF-statistic)0.000009作Y与X2的回归，结果如下：rn Equation: EQ12 WarkFle: UWTITLED-Urititled = | 回 | 3 View I Proc I Object I PrintlName Freeze EEtimatE Forecast! Stats Resids IDependent Variable: Y Method: Least Squares ate: 06/03/12

17、 Time: 22:17 Sample: 1990 2010 Included observations: 21VariableCoeffi-cientStd. Error t-Statisti-cProb.C28798.133124.S7&.2157680.0000X2-0.55 S6520.056206-8.4056 6 80.0000R-squared0.788112Mean dependent var269.84SAdjusted R.-squared0.776961S.D. depen(F-statistic)o.oaoooo作Y与X3的回归，结果如下:Q Equation; EQ1

18、3 WorkFile; UNTITLE&UntitledPrintNameFreeze IEstinna teForesca戒| StatsResids IDependent Variable: Y Method: Least Squares Date: OEJO3/12 Time: 22:17 S-ample: 1990 2010 Included obserYations: 20VariableCoefficientStd. Error t-Statisti-cProb.C20673.S61398 一 56614.782180.0000X3-0.22S9740.017613-12.9412

19、40.0000R.-squared0.902952Mean dependent var2526J90Adjusted R-squared0.897561S.D. depencient var1481.S20S.E. ofregressio-n474 3063Akaike infio criterion15.26 B22Sum squared res id4049379.Schwarz criterion15.35579Log likelihood-150 5622F-statistic167.4757Durbin-Wats-on statO.16460SProb(F-statisti-c)0.

20、000000作Y与X4的回归，结果如下: Equation; EQ14 Wo-rkfile; UhlTFTLE&XUntitled = | 回 | 蹈 Vi巳w|Fggbj*t| Print Name Fr巳巳日吁| Ewtirmtel F口匚匚日耽 S1ats|N雨ds|Dependent Variable: Method: Least SquaresDate: OB/03/12 Time: 22:17Sample; 1990 2010Include-d observations; 21VariableCoefficientStd. Error t-Statisti-cProb.C-63.0

21、098842.22730-1.4921600.1521X40 1474330.0020810.838980.0000R-squared0.99622SMean dependent var25.59.848A-djusted R-squared0.996030S.D. dependent war1476.614S.E. of regression93.04413Akaike infio criterion11.99442Sum squared res id164487.0Schwarz criterion12.09390Log lilkelihood-123.9414F-statistic501

22、8.1B1Durbin-Wats-on stat1.241548Pro b(F-stati Stic0.000000作Y与X5的回归，结果如下:View| Frcxz| Object PrintlNarne Freeze Estiirate Forecast Stats| Rjesidsepen-dent Variable: Y Method: Least Squares ate: 06/03/12 Time: 22:18Sample: 1990 2010Included observations: 21VariableCoefficientSt-d. Error t-StatisticPro

23、b.C1475.635163.15909.0441510.0000X50.621砌0 05906610.,523270.0000R-squarei0.853552Mean depenient 何2559.848Adjusted R-squared0 845844S.D. dependent varU76.&14S.E. of regression575 7576Akaike infio criterion15.65349Sum squared resid6386258.Schwarz criterion15.75297Lo-g likelihood-162.3&16F-statistic110

24、.7391urbin-Watson stat0.216775Pro b(F-stati Stic)0.000000E3作Y与X6的回归，结果如下:Dependent Variable: YMethcxj： Least Squares ate: D&/03/12 Time: 22:18S-ample: 1990 2010Included observations: 21VariableCoefficientStd. Error t-StatiSticPro-b.C-1319 718191.8911-B.8774360.0000X60.07180S0.00333721.5155B0.0000R-s

25、quaredQ.9 网 574Mean dependent var2559.843Adjusted R-squared0.958459S.D. dependent 切1476.E14S.E. of regression300.8115Akaike info criterion14.34124Sum squared res id171K64Schwarz criterion14.44072Log likeliho-txi-148.5830F-statistic462.9200Durbin-Wats-or stat0.305528Prob(F-stati Stic)0.000000IO Eclja

26、Cir,; EQ16 WorkfiIe: UNTTLEDUntitled!=i而讪 Poc| dlbj&t| Prin t| Name I Freeze Estiniaite | F口resca st Stats | Reisjd s|依据可决系数最大的原则选取X6作为进入回归模型的第一个解释变量，再依次将其余变量分别代入回归得:作Y与X6、X1的回归，结果如下：底Proc| PbjEctl Print| NaEe|FrEEze| EstiEate|FerEca5iz|5tats|ResiddDependent Variable: Y Method: Least Squares Date: 0

27、6/09/12 Time: 12:50 Sample: 1990 2010 Include-d abseivati&ns: 21VariableCoefficientStd. Errort-StatisticProb.C7329.7664329.490-1.529860.1077X60 0660540 00526812.538360.0000X10.0412150 029&6J1.3894670.1E16R-squared0.964353Mean -dependent var2559.848Adjusted R-squared0-960437S.D. dependent var1476.E14

28、S.E. of regression29370*7Akaike info criterion14.33459Sum squared re aid1552724.Schwarz criterion14 43361Log likeihood-U7 5132F-statistic24JJ622Durbin-Watson stat0.298684.Pro b(F-stati Stic)0.000000作Y与X6、X2的回归，结果如下: Equation; UNTITLED WorkFile;咪曝牝Untitled| 目 | S3 Vievvl Proc Object I Print Name Free

29、je| EstiE 曰足| Fcareca吕ResidslDependent Variable: Y Method: Least Squares ate: 06/09/12 Time: 12:51 Sample: 1990 2010 Included observations: 21VariableCoefficientStd. Erro-rt-StatisticProb.C3733.59528&5.54&1.3029270.2090 x&0.0620750.0063519.7734100.0000X2-0.0360370.054349-1.7B70410.0942R-squareJ0.966

30、402Mean dependent var2S59.B4SAdjusted R-squared0.962&6 9S.D. dependent var1476.614S.E &f regression285.2984Akaike info criterion14.27G51Sum squared resid146S113.Schwarz-criterion1442573Log likelihood-146.9034F-statistic258.8769Durbin-Watson sLat0.313811Pro bF-stati stic)0.000000作Y与X6、X3的回归，结果如下:View

31、| Pec| 0切8国 Print可加忡氏花| EstimatEil Forecagt|stats|Rjesids|Dependent Variable: YMethod: Least Squares ate: D6J09/12 Time: 12:52S-ample: 1990 2010Included observations! 20VariableCoefficientSid. Errort-StatisticProb.C-8630.824567 土 9721.51 盟 190.1470X&0.0950590 0181965.2240610.0001X30.0764500.吒痢1 一283

32、 9450.2164R-squared0.962750Mean -dependent var2626790Adjusted R-squared0.958368S. D. dependent var1481.920S.E. of regressio-n302.3691Akaike info criterion14.39866Sum squared resid1554-260.Schwarz criterio-n14.54801Log likeliho-cxi-140.9866F-stati stic219.6908Durbin-Watson stat0.329402Pro b(F-stati S

33、tic)0.000000作Y与X6、X4的回归，结果如下:O Equation: EQA4 WorkFile: 4Untitleda EMi已w| Proc| Dfaj已ct| Print| Name|Freeze EstiEate|Fermcawi:|tats|Rsjds|Dependent Variable: YMethcxi: Least SquaresDate: 06/0/12 Time: 12:S3Samiple: 1990 2010Include-d observations: 21VariableCoefficientStd. Errort-StatisticPro-b.C-29

34、3 889577.37295-3.7983500.0013X60.0120740 0036313.3253230.0038X40.1237420.00732116.903250.0000R-squared0.997663Mean dependent var2559.B48A-djusted R-squar&d0.997404S.D. dependent var1476.G14S.E. of regression7523755Akai Ice info criterion11.B1074Sum squared resid101892.4Schwarz criterion11.76996Log l

35、ikeliho-oi-118.9128F-stati stic3842 907Durbin-Watson stat1 252559Pro b(F-stati stic)0.000000作Y与X6、X5的回归，结果如下:断 Ew|Prx| Dhject| Print Name I Freese I Estinriate I Forecast! Stats I ResidslDependent Variable: Y Method: Least Squares Date: 06/09/12 Time: 12:53 Samiple: 1990 2010 Include-d observations:

36、 21VariableCoefficientS-td. Errort-StatisticProb.C-660.7700206j6408-3213Z250.0048X&0.0533260.00490210.877170.0000X50.194&620.0460194.3240260.0004R-squared0.980662Mean dependent var2559.848Adjusted R-squared0.978613S.D. dependent var1476.614S.E. of regressio-n216448SAkaike infio criterion13.72415Sum

37、squared res id843299.9Schwarz criterion13.87336Log likelih顽-141.1035F-statistic456.3973Durbin-Wat sen stat0.40E42Prob(F-statisti-c)o.oaoooo在满足经济意义和可决系数的条件下选取X4作为进入模型的第二 个解释变量，再次进行回归则：作Y与X6、X4、X1的回归，结果如下View | Pm c| 曰切己国 Print Vmrnei| Freeze | EstirnatEi| Forecast I Stats 职曲血|Dependent Variable: Y Me

38、thod: Least Squares Date: 0&/09/12 Time: 12:57 Sample: 1990 2010 Included observati-ons: 21VariableCoefficientSt-d. Errort-StatisticProb.C-3209.711931.83973+444880.0031x&0.0108870.0029983 6316500.0021X40.120+750.0060S519.798020.0000X10 0198110.0063163 1363620.0060R-squa.red0.998520Mean dependent var

39、2559 848A-djusted R-squared0.998259S.D. (dependent var1475.614S.E. of regression61.61775Akaike info criterion11.24942Sum squared res id64544 69Schwarz criterion11.44838Log ikelibDDd-114.1189F-statist ic3822 854Durbin-Watson stat2.089910Pro bF-stati stic)0.000000作Y与X6、X4、X2的回归，结果如下 Equation: UNTFTLED

40、 Workfile:咪咪WJntitled 1=1 | 回 | S3 |ViEw Fhoc| Dbject| Print Name FEeze| EMEate| Forecast Ststs I Reside|epenient Variable: Y Method: Least Squares ate: 06/09/12 Time: 12:58 Sample: 1990 2010 Included observations: 21VariableCoefficientStd. Erro-rt-StatisticPro-b.C-981.7&68S20.997E-11958220 2482X60

41、0119720.003&633.2686370.0045X40.12S7980.00822515415230.0000X20.0135540.0161040.84166304117R-squareJ0.997767Mean dependent var2569.048Adjusted R-squareri0997361S.D. dependent var147G.614S.E. of regression76.85449Akaike info criterion11 515Sum squared resid97S1635Schwarz-criterion11.8G411L叫 likelihood

42、-110.4341F-statistic2520605Durbin-Watson stat1 396193Pro bF-statistic0.000000作Y与X6、X4、X3的回归，结果如下切匠*| Pnx:|obj氐t| Print| P汽me Freere| Ewtirgte Foircastl Stats ResidMependent Variable: Y Method: Least Squares Date: 06/09/12 Time: 12:58Sample: 1990 2010Included observations: 50VariableCoefficientStri.

43、Errort-StatisticProb.C-2274.689141&.323-1.6050530.1278X50.0186360.006329Z.944-&580.0095X40.1226700.00734816.694560.0000X30.0208460.0146831.4196740.1749R-squared0.997978Mean dependent vsr2626.790Adjusted R-squared0.997538S.D. depen-dent war1481.920S.E. of regression72.62163Akaikc irfc criterion11.585

44、26Sum squared resid84382.41Schwarz criterion11.78441L&g likelihood-111.8626F-statistic2B31.907urbin-Watson stat1.561242P ro b(F-s tat i stic0.000000作Y与X6、X4、X5的回归，结果如下O Equation: UNTITLED Workfile-: 4Untitledui 回Wew | Proc| Objgct| Print Name Freeze| EMemte | Fear的ca吐| tats ResidslDependent Variable

45、: Y Method: Least Squares ate: 06/0/12 Time: 12:59Sample: 1990 2010Included observations: 21VariableCoefficientStd. Errort-Stati sticProb.C-284-.8028T9.3ST43-3.5605960.0024X60.0130 D60 0039753.2720030.0046X401189970.01055811.270430.0000X50.0U3040.0225700.6337630.5347R-squared0.997717Mean -dependent

46、var2559 848AJj u sted R-s q u aredD 997315S.D. dependent var1476.614S.E. of regression76.62012Akaike info criterion11.&8263Sum squared res id99540.68Schwarz criterion11.8&158Log likelihood-118 一6676F-stati sti-c2476.846Durbin-Watson stat1170793Pro b(F-statistic)0.000000在满足经济意义和可决系数的条件下选取X1作为进入模型的第三

47、个解释变量，再次进行回归则：作Y与X4、X6、X1、X2的回归，结果如下Equation: UNTITLED Workf le-:映峰1=1 回吐| PrintPame|Fi匪回 Ewtigfe|F陡匚母|Stats|只曲血|Dependent Variable: YMethod: Least Squares ate: D&/09/12 Time: 13:01Sample: 1990 2010Included observations: 21VariableCoefficientStd. ErrorL-StatisticPro-b.C-3063.274946.4814-3.2399090.00

48、61XB0.0107400.00300B3.5724300.0026X40.1162170.00750715.480370.0000X10.0242150.0077843.110925O.OOE7X2-0.015&10.01&121-0.9714320.3458R.-squared0.998602Mean dependent var2669.848Adjusted R-squared0.998263S.D. dependent 盹r1476614S.E. of regressio-n61.72007Aka ike info criterion11.28735Sum squared res id

49、60949.87Schwarz criterion11.53E05Log likelih&od-113.5172F-statisti-c2867.E78Durbin-Wats-on stat2.132460Prob(F-stati Stic)0.000000T-作Y与X4、X6、X1、X3的回归，结果如下CJ Equation: UNTITLED Worldlie-:咪咪dlJtitled1=1 回yiEwllVod 口切巳匚PrintName I Freeze I EEstimate I ForecastEtatsDependent Variable: Y4Method: Least Squ

50、aresDate: 06/09/12 Time: 13:02Sample: 1990 2010Included obseivati&ns: 20VariableCoefficientStd. Errort-Sta.ti sticPro-b.C-2973.97813Q8.2G3-2.2732270.0381X60 0099140 0069051.4357980.1716X40.1212040 005&2018.308850.0000X10 01953G0 0080162.2160140.0426X3-0.0019270.01G&990 11539B0.9097R-squared0.998476M

51、ean dependent var2626.790A-djusted R-squared0.998070S.D. dependent var1481.920S.E. of regression55.10023Akaike info criterion11.40205Sum squared res id63570.60Schwarz criterion11.B6098Log likelihtxxi-109.0205F-statistic2467.626Durbin-Watson stat2.213093Pro b(F-stati Stic)0.000000作Y与X4、X6、X1、X5的回归，结果

52、如下Q Equation: UNTITLED Wc?kfiIm;咪咪4Utitledu 回4巨伸|尸口:| 口bj已匚PrintNaE|FrEEze| EM e ate | %旦由5土| Stats | Res-id |Dependent Variable: YMethod: Least SquaresDate: 06J09/12 Time: 13:03Sample: 19 9 0 2010Included obseivations: 21VariableCoefficientStd. Errort-Stati sticPro-b.C-332&.310926,4023-3.5905680.00

53、24X60.0122280.00317.33S536520.0014X40 1132340 00&58513.1898S0.0000X10.0206950.0052893.2905210.0046X50 0213890.0180941.1821050.2544R-squared0.998639Mean iependent var2559.B48A-djusted R-squared0.998298S.D. dependent var1476.E14S.E. of regression60.90998Akaike info criterion11.26093Sum squared resid59

54、360.41Schwarz criterion11.50962Log likelihood-113.2397F-stati stic2934.508Durbin-Watsen stat2.0E0097Pro b(F-stati Stic)0.000000可见加入其余任何一个变量都会导致系数符号与经济意义不符，故 最终修正后的回归模型为：Y=-3209.71+0.01089X6+0.1205X4+0.01981X1(-3.4449)(3.6317)(19.7980)(3.1364)履2二0.99852=0.9983 F=3822.85 D.W.=2.0899异方差检验图示法32与X6的散点图如下

55、：说明e2与X6不存在异方差性。e2与X4的散点图分析说明e2与X4不存在异方差性。厂2与X1的散点图分析 Graph: UNITTLED Wcrkfile: IOUntitled| u | 回 | S3 |傩讪 Pec| Object| Printl Nmrnd AddTEKt| LinE/iShatJeTErnphtE| Z&orn|32000 -1280002400020000窟 1&000-120006000-。4000-oooQ _144-000152000160000 1l&400X1说明e2与X1不存在异方差性。G-Q检验对20组数据剔除中间五组剩下的进行分组后第一组(1990-

56、1997)数据的分析结果：O Equaticn: UNTITLED Workfile:咪;4UntitledView| Pmd 0切己国 Print Name | Freeze | EMrnate| F 口ecbs七 | Sts is | Ressids |Dependent Variable: Y Method: Least Squares ate: 0&/09/12 Time: S-ample: 1990 199-7 Included observations: E13:103VariableCoefficientStd. Error t-Stati sticProb.C4636.2671

57、143.983 40527400.0154X60.0274240.01125324369930.0714X40.09 配 90.0118250.2900000 0012X10.0270070.0035943.1065740.0360R.-squared0.998867Mean dependent var1239.425Adjusted R-squared0.990017S.D. dependent var561.3869S.E. of regression24.99686Akaike info criterion9.582230Sum squared res id2499 372Schwarz

58、 criterion9.621951Log likeliho-oKi-34.32892F-statistic1176.542Durbin-Wats-on stat2.286976Prob(F-stati sti-c)0.000002残差平方和RSS1=2499.372第二组(2003-2010)数据的回归结果:3 Equation: UNTITLED Workfile: SSSEUntitled=i 回Poi:|dEe吐| Print Name | Freeze | Etirnatg Fueh弱t| 5拍is 只应曲|Dependent Variable: YMethod: Least Squ

59、ares ate: 06/05/12 Time: 13:11Sample: 2003 2010Included observations: 3VariableCoefficientStd. Errort-Stati sticProb.C-3473.2863291.539-1.0552160.350SX60.0490560 0197752.4807480.0682X40.0715910.0300352 3835400.0757X10.0117010.0211900.5521810.6102R.-sq uared0.997113Mean dependent var4046.713Adjusted

60、R-squared0.994947S.D. dependent rar1155.640S.E. of regression82.14653Akaike info criterion11.96174Sum squared res id26992 25Schwarz criterion12.00146Log likelihio-oKi43.84696F-statistic460.4546 urbin-Wats-on stat1.770331ProbF-stati stic)0.000016残差平方和RSS2=26992.25所以 F二RSS2/RSS1=26992.25/2499.372=10.7