附件二：实验报告格式（首页）山东轻工业学院实验报告成绩课程名称计量经济学指导教师实验日期 2013-5-25 院（系）商学院专业班级实验地点二机房学生姓名学号同组人无实验项目名称多重共线性的检验与修正一、实验目的和要求掌握Eviews软件的操作和多重共线性的检验与修正二、实验原理Eviews软件的操作和多重共线性的检验修正方法三、主要仪器设备、试剂或材料Eviews软件，计算机四、实验方法与步骤（1）准备工作：建立工作文件，并输入数据：CREATE EX-7-1 A 1974 1981;TATA Y X1 X2 X3 X4 X5 ;(2)OLS估计：LS Y C X1 X2 X3 X4 X5;(3)计算简单相关系数COR X1 X2 X3 X4 X5 ;(4)多重共线性的解决LS Y C X1;LS Y C X2;LS Y C X3;LS Y C X4;LS Y C X5;LS Y C X1 X3;LS Y C X1 X3 X2;LS Y C X1 X3 X4;LS Y C X1 X3 X5;五、实验数据记录、处理及结果分析（1）建立工作组，输入以下数据：98.45 560.20 153.20 6.53 1.23 1.89100.70 603.11 190.00 9.12 1.30 2.03102.80 668.05 240.30 8.10 1.80 2.71133.95 715.47 301.12 10.10 2.09 3.00140.13 724.27 361.00 10.93 2.39 3.29143.11 736.13 420.00 11.85 3.90 5.24146.15 748.91 491.76 12.28 5.13 6.83144.60 760.32 501.00 13.50 5.47 8.36148.94 774.92 529.20 15.29 6.09 10.07158.55 785.30 552.72 18.10 7.97 12.57169.68 795.50 771.16 19.61 10.18 15.12162.14 804.80 811.80 17.22 11.79 18.25170.09 814.94 988.43 18.60 11.54 20.59178.69 828.73 1094.65 23.53 11.68 23.37 （2）OLS估计Dependent Variable: YMethod: Least SquaresDate: 05/25/13 Time: 11:10Sample: 1974 1987Included observations: 14Variable Coefficient Std. Error t-Statistic Prob.C -3.496563 30.00659 -0.116526 0.9101X1 0.125330 0.059139 2.119245 0.0669X2 0.073667 0.037877 1.944897 0.0877X3 2.677589 1.257293 2.129646 0.0658X4 3.453448 2.450850 1.409082 0.1965X5 -4.491117 2.214862 -2.027719 0.0771R-squared 0.970442 Mean dependent var 142.7129Adjusted R-squared 0.951968 S.D. dependent var 26.09805S.E. of regression 5.719686 Akaike info criterion 6.623232Sum squared resid 261.7185 Schwarz criterion 6.897114Log likelihood -40.36262 F-statistic 52.53086Durbin-Watson stat 1.972755 Prob(F-statistic) 0.000007用Eviews进行最小二乘估计得，Yˆ=-3.497+0.125X1+0.074X2+2.678X3+3.453X4-4.491X5(-0.1) (2.1) (1.9) (2.1) (1.4) (-2.0)R2=0.970, 2R=0.952, DW=1.97, F=52.53其中括号内的数字是t值。

给定显著水平α=0.05，回归系数估计值都没有显著性。

查F 分布表，得临界值为F0.05（5,8）=3.69，故F=52.53>3.69，回归方程显著。

(3)计算简单相关系数COR X1 X2 X3 X4 X5 ;X1 X2 X3 X4 X5X1 1 0.866551867279170.8822931086064990.852*******193940.821305444858646X2 0.86655186727917 10.9458956983200270.9647730220121920.98253206329193X3 0.8822931086064990.945895698320027 10.9405058208239960.948361346495427X4 0.852*******193940.9647730220121920.940505820823996 10.98197917741363X5 0.8213054448586460.982532063291930.9483613464954270.98197917741363 1 r12=0.867，r13=0.882，r14=0.852，r15=0.821，r23=0.946，r24=0.965,r25=0.983，r34=0.941，r35=0.948，r45=0.982可见解释变量之间是高度相关的。

(4)多重共线性的解决, 采用Frisch法。

&1.对Y关于X1，X2，X3，X4，X5作最小二乘回归:1) LS Y C X1Dependent Variable: YMethod: Least SquaresDate: 05/25/13 Time: 11:12Sample: 1974 1987Included observations: 14Variable Coefficient Std. Error t-Statistic Prob.C -90.92074 19.32929 -4.703781 0.0005X1 0.316925 0.026081 12.15161 0.0000R-squared 0.924841 Mean dependent var 142.7129Adjusted R-squared 0.918578 S.D. dependent var 26.09805S.E. of regression 7.446964 Akaike info criterion 6.985054Sum squared resid 665.4873 Schwarz criterion 7.076347Log likelihood -46.89537 F-statistic 147.6617Durbin-Watson stat 1.536885 Prob(F-statistic) 0.000000得回归方程为：Yˆ=-90.921+0.317X1（-4.7）（12.2)R2=0.925, 2R=0.919, DW=1.537, F=147.6192) LS Y C X2Dependent Variable: YMethod: Least SquaresDate: 05/25/13 Time: 11:14Sample: 1974 1987Included observations: 14Variable Coefficient Std. Error t-Statistic Prob.C 99.61349 6.431242 15.48900 0.0000X2 0.081470 0.010738 7.587119 0.0000 R-squared 0.827498 Mean dependent var 142.7129Adjusted R-squared 0.813123 S.D. dependent var 26.09805S.E. of regression 11.28200 Akaike info criterion 7.815858Sum squared resid 1527.403 Schwarz criterion 7.907152Log likelihood -52.71101 F-statistic 57.56437Durbin-Watson stat 0.638969 Prob(F-statistic) 0.000006 得回归方程为：Yˆ=99.614+0.0815X2（15.5）（7.6）R=0.813, DW=0.639，F=57.564R2=0.828, 23）LS Y C X3Dependent Variable: YMethod: Least SquaresDate: 05/25/13 Time: 11:14Sample: 1974 1987Included observations: 14Variable Coefficient Std. Error t-Statistic Prob.C 74.64824 8.288989 9.005711 0.0000X3 4.892712 0.563578 8.681514 0.0000R-squared 0.862651 Mean dependent var 142.7129Adjusted R-squared 0.851205 S.D. dependent var 26.09805S.E. of regression 10.06704 Akaike info criterion 7.587974Sum squared resid 1216.144 Schwarz criterion 7.679268Log likelihood -51.11582 F-statistic 75.36868Durbin-Watson stat 0.813884 Prob(F-statistic) 0.000002得回归方程为：Yˆ=74.648+4.893X3（9.0）（8.7）R2=0.863, 2R=0.851, DW=0.814，F=75.3694) LS Y C X4Dependent Variable: YMethod: Least SquaresDate: 05/25/13 Time: 11:15Sample: 1974 1987Included observations: 14Variable Coefficient Std. Error t-Statistic Prob.C 108.8647 5.934330 18.34490 0.0000X4 5.739752 0.838756 6.843175 0.0000R-squared 0.796019 Mean dependent var 142.7129Adjusted R-squared 0.779021 S.D. dependent var 26.09805S.E. of regression 12.26828 Akaike info criterion 7.983475Sum squared resid 1806.129 Schwarz criterion 8.074769Log likelihood -53.88433 F-statistic 46.82904Durbin-Watson stat 0.769006 Prob(F-statistic) 0.000018 得回归方程为：Yˆ=108.865+5.740X4（18.3）（6.8）R2=0.796, 2R=0.779, DW=0.769，F=46.8295) LS Y C X5Dependent Variable: YMethod: Least SquaresDate: 05/25/13 Time: 11:16Sample: 1974 1987Included observations: 14Variable Coefficient Std. Error t-Statistic Prob.C 113.3747 6.077133 18.65596 0.0000X5 3.080811 0.512300 6.013688 0.0001 R-squared 0.750854 Mean dependent var 142.7129Adjusted R-squared 0.730091 S.D. dependent var 26.09805S.E. of regression 13.55865 Akaike info criterion 8.183490Sum squared resid 2206.044 Schwarz criterion 8.274784Log likelihood -55.28443 F-statistic 36.16444Durbin-Watson stat 0.593639 Prob(F-statistic) 0.000061 得回归方程为：Yˆ=113.375+3.081X5（18.7）（6.0）R2=0.75, 2R=0.73, DW=0.59，F=36.16选第一个方程为基本回归方程。