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1、Graduates to apply for the quantitative analysis of changes in number of graduate students 一 Topics raised In 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 a
2、nalysis. 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 gradua
3、te 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
4、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 sour
5、ce) 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 - whi
6、ch 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) 二二 Establish Model Y=+1X1+2X2+3X3+4X4 +u Among them, the Y-in the total number of gr
7、aduate 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、三、Data collection 1. date Explain Here, using
8、the same area (ie, China) time-series data were fitted 2. Data collection Time series data from 1986 to 2005, the specific circumstances are shown in Table 1 Table 1： YX1X2X3X4 1986110371963107507264.41054 19871201911112109300276.61063 19881127761366111026296.21075 19891013391519112704377.91075 1990
9、930181644114333383.21075 1991881281893115823352.21075 1992941642311117171363.91053 19931067712998118517420.11065 19941279354044119850476.41080 19951454435046121121519.61054 19961633225846122389552.81032 19971763536420123626576.81020 199819888567961247615711022 199923351371591257865751071 20003012397
10、8581267435951041 200139325686221276276811225 200250098093981284537701396 2003651260105421292278001552 2004819896123361299888271731 2005978610140401307568391792 四、四、Model parameter estimation, inspection and correction 1. Model parameter estimation and its economic significance, statistical inference
11、 test . . t tw wo ow wa ay y( (s sc ca at tt te er r Y Y X X1 1) ) 02000004000006000008000001.0e+06 Y 050001000015000 X1 twoway(scatter Y X2) 02000004000006000008000001.0e+06 Y 105000110000115000120000125000130000 X2 twoway(scatter Y X3) 02000004000006000008000001.0e+06 Y 200400600800 X3 twoway(scat
12、ter Y X4) 02000004000006000008000001.0e+06 Y 10001200140016001800 X4 graph twoway lfit y X1 0200000400000600000800000 Fitted values 050001000015000 X1 graph twoway lfit y X2 -2000000200000400000600000 Fitted values 105000110000115000120000125000130000 X2 graph twoway lfit y X3 0200000400000600000800
13、000 Fitted values 200400600800 X3 graph twoway lfit y X4 02000004000006000008000001000000 Fitted values 10001200140016001800 X4 _ _c co on ns s 2 27 70 07 77 75 5. .2 2 3 36 69 92 25 52 2. .9 9 0 0. .7 73 3 0 0. .4 47 75 5 - -5 51 16 62 26 68 8. .7 7 1 10 05 57 78 81 19 9 X X4 4 6 62 21 1. .3 33 34
14、48 8 4 46 6. .7 72 22 25 57 7 1 13 3. .3 30 0 0 0. .0 00 00 0 5 52 21 1. .7 74 48 8 7 72 20 0. .9 92 21 16 6 X X3 3 - -3 36 66 6. .8 87 77 74 4 1 15 57 7. .9 94 40 02 2 - -2 2. .3 32 2 0 0. .0 03 35 5 - -7 70 03 3. .5 51 18 89 9 - -3 30 0. .2 23 35 58 85 5 X X2 2 - -7 7. .1 15 58 86 60 03 3 3 3. .2
15、25 57 75 54 41 1 - -2 2. .2 20 0 0 0. .0 04 44 4 - -1 14 4. .1 10 01 18 89 9 - -. .2 21 15 53 31 18 82 2 X X1 1 5 59 9. .2 22 24 45 55 5 6 6. .3 35 52 22 28 88 8 9 9. .3 32 2 0 0. .0 00 00 0 4 45 5. .6 68 84 49 96 6 7 72 2. .7 76 64 41 13 3 Y Y C Co oe ef f. . S St td d. . E Er rr r. . t t P P | |t
16、t| | 9 95 5% % C Co on nf f. . I In nt te er rv va al l T To ot ta al l 1 1. .3 30 04 40 0e e+ +1 12 2 1 19 9 6 6. .8 86 63 31 1e e+ +1 10 0 R Ro oo ot t M MS SE E = = 1 18 85 53 35 5 A Ad dj j R R- -s sq qu ua ar re ed d = = 0 0. .9 99 95 50 0 R Re es si id du ua al l 5 5. .1 15 53 33 3e e+ +0 09 9 1 15 5 3 34 43 35 55 56 63 32 20 0
