分析回归法分析研究试题.docx
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1、3.2(1)用Eviews分析如下Dependent Variable: YMethod: Least SquaresDate: 12/01/14 Time: 20:25Sample: 1994 2011Included observations: 18Variable Coefficient Std. Error t-Statistic Prob.X20.1354740.01279910.58454X318.853489.7761811.928512C-18231.588638.216-2.1105730.00000.07290.0520R-squaredAdjusted R-squared
2、S.E. of regressionSum squared residLog likelihoodF-statistic0.985838 Mean dependent var0.983950 S.D. dependent var730.6306 Akaike info criterion8007316. Schwarz criterion-142.5903 Hannan-Quinn criter.522.0976 Durbin-Watson stat6619.1915767.15216,1767016,3251016,197171.173432Prob(F-statistic)0.000000
3、由表可知模型为:Y = 0.135474X2 4- 18.85348X3- 18231.58检验:可决系数是0.985838,修正的可决系数为0.983950,说明模型对样本拟合较好。F 检验,F=522.0976F (2,15) =4.77,回归方程显著。t检验,t统计量分别为X2的系数对应t值为10.58454,大于t(15) =2.131,系数是显著的,X3的系数对应t值为L928512,小于t (15) =2.131,说明此系数是不显著的。(16) (2)表内数据In后重新输入数据:Dependent Variable: LNYMethod: Least SquaresDate: 10
4、/25/15 Time: 22:18Sample: 1994 2011Included observations: 18VariableCoefficientStd. Error t-Statistic Prob.c-10.810901.698653-6.3643970. 0000LNX21.5737840.09154717.191060. 0000X30. 0024380.0009362. 6053210.0199R-squaredAdjusted R-squaredS.E. of regressionSum squared residLog likelihoodF-statisticPro
5、b(F-statistic)8.4001120.941530-1.302176-1.153780-1.2817140.684080Mean dependent0. 986373varS. D. dependent0.984556varAkaike info0.117006criterionSchwarz0.205355criterionHannan-Quinn14.71958criter.0.000000Durbin-Watson542. 8930stat模型为 Iny=10.81090+l.5737841nx2+0.002438x3检验:经济意义为其他条件不变的情况下,工业增加值每增加一个单
6、位百分比出口货物总和增加1.57单位百分比,汇率每增加一单位百分比,出口总额增加0.0024个单位百分比。拟合优度检验,R八2=0.986373修正可决系数为0.984556,拟合很好。F检验对于H0:X2=X3=0,给定显著性水平a=0.05 F(2,15)=4.77F=542.8930F(2,15)显著t检验对于H0:Xj=0 (j=2,3),给定显著性水平a=0.05 t (15) =2.131当j=2时tt(15)显著,当j=3时tt(15)显著。两个模型表现出的汇率对Y的印象存在巨大差异3.3(1)用Eviews分析如下Dependent Variable: YMethod: Lea
7、st SquaresDate: 12/01/14 Time: 20:30Sample: 1 18Included observations: 18Variable Coefficient Std. Error t-Statistic Prob.X0.0864500.0293632.9441860.0101T52.370315.20216710.067020.0000C-50.0163849.46026-1.0112440.3279R-squaredAdjusted R-squaredS.E. of regressionSum squared residLog likelihoodF-stati
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