对此模型进行White检验得： Heteroskedasticity Test: White
F-statistic 1.003964 Prob. F(2,31) 0.3780
Obs*R-squared 2.068278 Prob. Chi-Square(2) 0.3555 Scaled explained SS 1.469638 Prob. Chi-Square(2) 0.4796
Test Equation: Dependent Variable: RESID^2 Method: Least Squares Date: 12/24/15 Time: 21:45 Sample: 1 34 Included observations: 34
Variable Coefficient Std. Error t-Statistic Prob. C 0.039547 0.046759 0.845753 0.4042 LNX -0.011601 0.014012 -0.827969 0.4140 LNX^2 0.000932 0.001028 0.906774 0.3715 R-squared 0.060832 Mean dependent var 0.004950
Adjusted R-squared 0.000240 S.D. dependent var 0.006365 S.E. of regression 0.006364 Akaike info criterion -7.192271 Sum squared resid 0.001255 Schwarz criterion -7.057592 Log likelihood 125.2686 Hannan-Quinn criter. -7.146342 F-statistic 1.003964 Durbin-Watson stat 2.022904 Prob(F-statistic) 0.378027
从上图中可以看出，nR2
=2.068278，比较计算的统计量的临界值，nR2=2.068278<0.05（2）=5.9915，所以接受原假设，此模型消除了异方差。
综合两种方法，改进后的模型最好为：
LnY=0.946887 LNX+0.201861
为因 (2)
1）考虑价格因素，首先用软件三者关系进行分析如下： Dependent Variable: Y Method: Least Squares Date: 12/24/15 Time: 21:51 Sample: 1 34 Included observations: 34
Variable Coefficient Std. Error t-Statistic X 0.741684 0.019905 37.26095 P 0.235025 0.271701 0.865012 C 43.41715 71.22946 0.609539 R-squared 0.979911 Mean dependent var
Adjusted R-squared 0.978615 S.D. dependent var S.E. of regression 173.8449 Akaike info criterion Sum squared resid 936883.7 Schwarz criterion Log likelihood -222.0511 Hannan-Quinn criter. F-statistic 756.0627