LIMDEP怎么出2个同的结果啊?

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同样的数据,我在学校用的LIMDEP出的结果,和我自己电脑LIMDEP出的结果怎么不一样呢?

有人知道吗?

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关键词：Limdep LIMD IMD Lim 结果 Limdep

你的把数据、程序、结果贴出来，人家才可能知道是那里的问题啊

这个是在学校做出来的结果:

+-----------------------------------------------------------------------+

| Ordinary    least squares regression    Weighting variable = none     |

| Dep. var. = P        Mean=   .1416666667    , S.D.=   .3487886800     |

| Model size: Observations =    2160, Parameters =  10, Deg.Fr.=   2150 |

| Residuals:  Sum of squares= 222.1533290    , Std.Dev.=         .32145 |

| Fit:        R-squared=  .154185, Adjusted R-squared =          .15064 |

| Model test: F[  9,   2150] =   43.55,    Prob value =          .00000 |

| Diagnostic: Log-L =   -608.4519, Restricted(b=0) Log-L =    -789.3028 |

|             LogAmemiyaPrCrt.=   -2.265, Akaike Info. Crt.=       .573 |

| Autocorrel: Durbin-Watson Statistic =   1.23044,   Rho =       .38478 |

+-----------------------------------------------------------------------+

+---------+--------------+----------------+--------+---------+----------+

|Variable | Coefficient  | Standard Error |b/St.Er.|P[|Z|>z] | Mean of X|

+---------+--------------+----------------+--------+---------+----------+

 Constant  1.601583796      .21908134        7.310   .0000

 X1       -.1185225351E-02  .54200411E-03   -2.187   .0288 -.46215278

 X2       -.3222028924      .22654172E-01  -14.223   .0000  .44596296

 X3       -.7991153292E-02  .61609828E-02   -1.297   .1946  1.5374861

 X4        .6094792532E-02  .17242008E-02    3.535   .0004  9.1074074

 X5       -.1351273443      .22521602E-01   -6.000   .0000  8.9764167

 X6        .4481329790E-01  .15741503E-01    2.847   .0044  .83013796E-01

 X7       -.1681142853      .69648120E-01   -2.414   .0158  .15427315

 X8       -.1466655758      .67063745E-01   -2.187   .0287  .41250926

 X9       -.1706733489      .62428635E-01   -2.734   .0063  .37686111

这个是从这里下载的LIMDEP在家做出来的结果:

+-----------------------------------------------------------------------+
| Ordinary    least squares regression    Weighting variable = none     |
| Dep. var. = P        Mean=   .1416666667    , S.D.=   .3487886800     |
| Model size: Observations =    2160, Parameters =  10, Deg.Fr.=   2150 |
| Residuals:  Sum of squares= 223.1878437    , Std.Dev.=         .32219 |
| Fit:        R-squared=  .150246, Adjusted R-squared =          .14669 |
| Model test: F[  9,   2150] =   42.24,    Prob value =          .00000 |
| Diagnostic: Log-L =   -613.4696, Restricted(b=0) Log-L =    -789.3028 |
|             LogAmemiyaPrCrt.=   -2.261, Akaike Info. Crt.=       .577 |
| Autocorrel: Durbin-Watson Statistic =   1.21908,   Rho =       .39046 |
+-----------------------------------------------------------------------+
+---------+--------------+----------------+--------+---------+----------+
|Variable | Coefficient  | Standard Error           |b/St.Er.|       P[|Z|>z] | Mean of X|
+---------+--------------+----------------+--------+---------+----------+
 Constant  1.598049902      .21959086            7.277          .0000
 X1       -.1223886248E-02  .54326464E-03     -2.253        .0243    -.46215278
 X2       -.2660963900      .22706859E-01       -11.719        .0000     .44596296
 X3       -.9485014737E-02  .61753112E-02     -1.536        .1245    1.5374861
 X4        .5020542373E-02  .17282108E-02     2.905          .0037    9.1074074
 X5       -.1398592265      .22573980E-01       -6.196          .0000    8.9764167
 X6        .4809811782E-01  .15778113E-01    3.048           .0023     .83013796E-01
 X7       -.1564699306      .69810099E-01       -2.241          .0250     .15427315
 X8       -.1426104143      .67219714E-01       -2.122          .0339    .41250926
 X9       -.9290335188E-01  .62573824E-01   -1.485          .1376    .37686111

尤其是这个X9的结果最不一样了....怎么会这样呢???