Graduates to apply for the quantitative analysis of changes in number of graduatestudents一Topics raisedIn this paper, the total number of students from graduate students (variable) multivariate analysis (see below) specific analysis, and collect relevant data, model building, this quantitative analysis. The number of relations between the school the total number of graduate students with the major factors, according to the size of the various factors in the coefficient in the model equations, analyze the importance of various factors, exactly what factors in changes in the number of graduate students aspects play a key role in and changes in the trend for future graduate students to our proposal.The main factors affect changes in the total number of graduate students for students are as follows：Per capita GDP - which is affecting an important factor to the total number of students in the graduate students (graduate school is not a small cost, and only have a certain economic base have more opportunities for post-graduate)The total population - it will affect the total number of students in graduate students is an important factor (it can be said to affect it is based on source)The number of unemployed persons - this is the impact of a direct factor of the total number of students in the graduate students (it is precisely because of the high unemployment rate, will more people choose Kaoyan will be their own employment weights) Number of colleges and universities - which is to influence precisely because of the emergence of more institutions of higher learning in the school the total number of graduate students is not a small factor (to allow more people to participate in Kaoyan)二Establish ModelY=α+β1X1+β2X2+β3X3+β4X4 +uAmong them, theY-in the total number of graduate students (variable)X1 - per capita GDP (explanatory variables)X2 - the total population (explanatory variables)X3 - the number of unemployed persons (explanatory variables)X4 - the number of colleges and universities (explanatory variables)三、Data collection1.date ExplainHere, using the same area (ie, China) time-series data were fitted2.Data collectionTime series data from 1986 to 2005, the specific circumstances are shown in Table 1Table 1：Y X1 X2 X3 X41986 110371 963 107507 264.4 10541987 120191 1112 109300 276.6 10631988 112776 1366 111026 296.2 10751989 101339 1519 112704 377.9 10751990 93018 1644 114333 383.2 10751991 88128 1893 115823 352.2 10751992 94164 2311 117171 363.9 10531993 106771 2998 118517 420.1 10651994 127935 4044 119850 476.4 10801995 145443 5046 121121 519.6 10541996 163322 5846 122389 552.8 10321997 176353 6420 123626 576.8 10201998 198885 6796 124761 571 10221999 233513 7159 125786 575 10712000 301239 7858 126743 595 10412001 393256 8622 127627 681 12252002 500980 9398 128453 770 13962003 651260 10542 129227 800 15522004 819896 12336 129988 827 17312005 978610 14040 130756 839 1792四、Model parameter estimation, inspection and correction1. Model parameter estimation and its economic significance, statistical inference test. twoway(scatter Y X1)2000004000006000008000001.0e +0twoway(scatter Y X2)2000004000006000008000001.0e +06twoway(scatter Y X3)2000004000006000008000001.0e +0twoway(scatter Y X4)2000004000006000008000001.0e +06graph twoway lfit y X120000040000060000080000F i t t e d v a l u e sgraph twoway lfit y X2 -20000200000400000600000F i t t e d v a l u e sgraph twoway lfit y X320000040000060000080000F i t t e d v a l u e sgraph twoway lfit y X42000004000006000008000001000000F i t t e d v a l u e s. reg Y X1 X2 X3 X4Source SS df MS Number of obs = 20F( 4, 15) = 945.14Model 1.2988e+12 4 3.2471e+11 Prob > F = 0.0000Residual 5.1533e+09 15 343556320 R-squared = 0.9960Adj R-squared = 0.9950Total 1.3040e+12 19 6.8631e+10 Root MSE = 18535Y Coef. Std. Err. t P>|t| [95% Conf. Interval]X1 59.22455 6.352288 9.32 0.000 45.68496 72.76413X2 -7.158603 3.257541 -2.20 0.044 -14.10189 -.2153182X3 -366.8774 157.9402 -2.32 0.035 -703.5189 -30.23585X4 621.3348 46.72257 13.30 0.000 521.748 720.9216_cons 270775.2 369252.9 0.73 0.475 -516268.7 1057819Y = 59.22454816*X1- 7.158602346*X2- 366.8774279*X3+621.3347694*X4 （6.352288）（3.257541）（157.9402）（46.72256）t= （9.323341）（-2.197548）（-2.322889）（13.29839）+ 270775.151（369252.8）（0.733306）R2=0.996048 Adjusted R-squared=0.994994 F=945.1415 DW=1.596173Visible, X1, X2, X3, X4 t values are significant, indicating that the per capita GDP, the total population of registered urban unemployed population, the number of colleges and universities are the main factors affecting the total number of graduate students in school.Model coefficient of determination for 0.996048 amendments coefficient of determination of 0.994994, was relatively large, indicating high degree of model fit, while the F value of 945.1415, indicating that the model overall is significant。