2.计量经济学检验

　　The above table can be seen to explain the positive correlation between the height of the variable X1 and X2, X3, X4, X2, X1, X3, between the highly positively correlated, showing that there is serious multicollinearity. Following amendment stepwise regression：

　　Y = 60.21976901*X1 - 61096.25048

　　（6.311944） （42959.23）

　　t = (9.540606) (-1.422191) Adjusted R-squared=0.825725 F=91.02316

　　Y = 27.05878289*X2 - 2993786.354

　　( 5.622791) (680596.9) t = (4.812340) (-4.398766) R-squared=0.562668 F=23.15862

　　Y = 1231.659997*X3 - 371863.6509

　　(161.9045) (90051.37) t = (7.607324) (-4.129461)

　　Adjusted R-squared=0.749576 F=57.87138

　　Y = 1053.519847*X4 - 964699.7964 (65.85948) (79072.71)

　　t = (15.99648) (-12.20016)

　　Adjusted R-squared=0.930628 F=255.8874

　　The analysis shows that the four simple regression model, the total number of graduate students for the linear relationship between Y college x4, goodness of fit： Y = 1053.519847*X4 - 964699.7964 (65.85948) (79072.71)

　　t = (15.99648) (-12.20016)

　　Adjusted R-squared=0.930628 F=255.887

　　Y = 714.1694264*X4 + 25.58237739*X1 - 708247.7381 （48.45708） （2.930053） （45496.23） t = （14.73818） （8.731029） （-15.56718）

　　Adjusted R-squared=0.986606 F=700.7988

　　Y = 886.3583756*X4 + 8.974091045*X2 - 1852246.686

　　（55.52670） （1.837722） （189180.7） t = （15.96274） （4.883269） （-9.790886）

　　Adjusted R-squared=0.969430 F=302.2581

　　Y = 791.519267*X4 + 436.7502136*X3 - 885870.134

　　（69.64253） （90.10899） （55171.66） t = （11.36546） （4.846910） （-16.05662） Adjusted R-squared=0.969163 F=299.5666

　　By the data analysis, comparison, per capita GDP of the new entrants to the X1 equation of the Adjusted R-squared = .986606

　　, The largest improvement, and each parameter, T-test significant, so I chose to retain the X1

　　Then add the other new variables to the stepwise regression:

　　五、Analysis and conclusions of the model

　　It can be seen from the model:

　　(1) model: significantly correlated only with colleges and universities total and per capita GDP in the total number of graduate students.

　　(2) X1, X4 is in line with economic significance of the test. Economic sense, the total number of graduate students with the increase in per capita GDP increases, the increase with the increase in the total number of universities. And universities is the total impact of the total number of the most important factor in the graduate students.

　　(3) the amendment of the model coefficient of determination and F values are very high goodness of fit of the model is good