复习多元线性回归模型案例文档格式.docx

1、y x2x3x4x5x6x7x8年份78年可比价比重%千公顷亿千瓦时1986133.6013.4329.5017.9236.0179.99150104.07253.101987137.6312.2031.3019.3938.6275.63146379.53320.801988147.867.6637.6023.7145.9069.25143625.87508.901989196.769.4239.9026.2149.2362.75146553.93790.501990220.539.9826.4149.9364.66148362.27844.501991223.2510.2640.3026.9

2、450.9263.09149585.80963.201992233.1910.0541.5027.4651.5361.51149007.101106.901993265.679.4943.6027.9951.8660.07147740.701244.901994335.169.2045.7028.5152.1258.22148240.601473.901995411.298.4347.8029.0452.4158.43149879.301655.701996460.688.8249.5030.4853.2360.57152380.601812.701997477.968.3050.1031.9

3、154.9358.23153969.201980.101998474.0210.6950.2033.3555.8458.03155705.702042.201999466.808.2349.9034.7857.1657.53156372.812173.452000466.167.7550.0036.2259.3355.68156299.852421.302001469.807.7137.6660.6255.24155707.862610.782002468.957.1739.0962.0254.51154635.512993.402003476.247.1250.9040.5363.7250.

4、08152414.963432.922004499.399.6753.1041.7665.6450.05153552.553933.032005521.207.2255.2042.9967.5949.72155487.734375.70资料来源中国统计年鉴2006。（二）、计量经济学模型建立我们设定模型为下面所示的形式：利用Eviews软件进行最小二乘估计，估计结果如下表所示：Dependent Variable: YMethod: Least SquaresSample: 1986 2004Included observations: 19VariableCoefficientStd. Er

5、rort-StatisticProb. C-1102.373375.8283-2.9331840.0136X2-6.6353933.781349-1.7547690.1071X318.229422.0666178.8208990.0000X42.4300398.3703370.2903160.7770X5-16.237375.894109-2.7548470.0187X6-2.1552082.770834-0.7778190.4531X70.0099620.0023284.2788100.0013X80.0633890.0212762.9793480.0125R-squared0.995823

6、 Mean dependent var345.5232Adjusted R-squared0.993165 S.D. dependent var139.7117S.E. of regression11.55028 Akaike info criterion8.026857Sum squared resid1467.498 Schwarz criterion8.424516Log likelihood-68.25514 F-statistic374.6600Durbin-Watson stat1.993270 Prob（F-statistic）0.000000表1 最小二乘估计结果回归分析报告为

7、：二、计量经济学检验（一）、多重共线性的检验及修正、检验多重共线性（a）、直观法从“表1 最小二乘估计结果”中可以看出，虽然模型的整体拟合的很好，但是x4 x6的t统计量并不显著，所以可能存在多重共线性。（b）、相关系数矩阵 1.000000-0.717662-0.695257-0.731326 0.737028-0.332435-0.594699 0.922286 0.935992-0.945701 0.742251 0.883804 0.986050-0.937751 0.753928 0.974675-0.974750 0.687439 0.940436-0.603539-0.887428 0.742781表2 相关系数矩阵从“表2 相关系数矩阵”中可以看出，个个解释变量之间的相关程度较高，所以应该存在多重共线性。、多重共线性的修正逐步迭代法A、 一元回归820.3133151.87125.401374-51.3783616.18923-3.1736140.00560.3720410.335102113.922712.40822220632.412.50763-115.878110.071830.6444000.005554表3 y对x2的回归结果-525.889164.11333-8.202492