Stata将回归分析结果直接导出到Word里
ssc install asdoc, replace
写每个命令时前面加上asdoc就可将生成的结果存在word 中
将图片保存成.emf格式，可在word中直接插入。
导入数据
数据描述
. sum#描述数据
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
A | 25 13 7.359801 1 25
index | 25 13 7.359801 1 25
y | 25 26.444 24.60308 1.9 84.7
x1 | 25 101.032 64.96047 13.2 299.5
x2 | 25 99.384 63.28338 6.1 277
-------------+---------------------------------------------------------
x3 | 25 5197.4 3649.682 209 15571
x4 | 25 11.4 7.112196 2 34
x5 | 25 11.848 7.625676 2.5 33.7
由上表可以看出每个变量的最大值，最小值，平均值，标准差以及样本个数等具体值。
describe#描述数据，和sum有一定区别
得到
Contains data
obs: 25
vars: 8
size: 925
-----------------------------------------------------------------------------------------------
storage display value
variable name type format label variable label
-----------------------------------------------------------------------------------------------
A byte %10.0g
index byte %10.0g index
y double %10.0g y
x1 double %10.0g x1
x2 double %10.0g x2
x3 int %10.0g x3
x4 byte %10.0g x4
x5 double %10.0g x5
-----------------------------------------------------------------------------------------------
Sorted by:
Note: Dataset has changed since last saved.
得到样本数据特征
线性回归
regress y x1 x2 x3 x4 x5#作线性回归
结果
Source | SS df MS Number of obs = 25
-------------+---------------------------------- F(5, 19) = 21.84
Model | 12374.4556 5 2474.89112 Prob > F = 0.0000
Residual | 2153.02602 19 113.317159 R-squared = 0.8518
-------------+---------------------------------- Adj R-squared = 0.8128
Total | 14527.4816 24 605.311733 Root MSE = 10.645
------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x1 | .1273254 .095979 1.33 0.200 -.073561 .3282118
x2 | .160566 .0556834 2.88 0.010 .0440194 .2771126
x3 | .0007636 .0013556 0.56 0.580 -.0020737 .0036009
x4 | -.333199 .3986248 -0.84 0.414 -1.16753 .5011323
x5 | -.5746462 .3087506 -1.86 0.078 -1.220869 .0715763
_cons | 4.260477 10.46798 0.41 0.689 -17.64926 26.17022
------------------------------------------------------------------------------
相关系数矩阵
correlate y x1 x2 x3 x4 x5
| y x1 x2 x3 x4 x5
-------------+------------------------------------------------------
y | 1.0000
x1 | 0.8505 1.0000
x2 | 0.8332 0.7381 1.0000
x3 | 0.7409 0.8832 0.5534 1.0000
x4 | -0.6043 -0.6231 -0.5382 -0.5225 1.0000
x5 | -0.4470 -0.2775 -0.3231 -0.2910 0.0953 1.0000
##Stata检查是否存在多重共线性
用容忍度和方差膨胀因子（VIF）,VIF 大于10 存在严重的共线性，若自变量间存在的多重相关性这里将采取逐步回归法进行修正。
estat vif
Variable | VIF 1/VIF
-------------+----------------------
x1 | 8.23 0.121460
x3 | 5.18 0.192888
x2 | 2.63 0.380237
x4 | 1.70 0.587420
x5 | 1.17 0.851750
-------------+----------------------
Mean VIF | 3.78
pca x1 x2 x3 x4 x5
Principal components/correlation Number of obs = 25
Number of comp. = 5
Trace = 5
Rotation: (unrotated = principal) Rho = 1.0000
--------------------------------------------------------------------------
Component | Eigenvalue Difference Proportion Cumulative
-------------+------------------------------------------------------------
Comp1 | 3.06752 2.13626 0.6135 0.6135
Comp2 | .931263 .42405 0.1863 0.7998
Comp3 | .507212 .0891979 0.1014 0.9012
Comp4 | .418014 .342028 0.0836 0.9848
Comp5 | .0759864 . 0.0152 1.0000
--------------------------------------------------------------------------
Principal components (eigenvectors)
------------------------------------------------------------------------------
Variable | Comp1 Comp2 Comp3 Comp4 Comp5 | Unexplained
-------------+--------------------------------------------------+-------------
x1 | 0.5411 0.0950 0.2986 0.0764 -0.7767 | 0
x2 | 0.4736 -0.0361 -0.3527 0.7615 0.2648 | 0
x3 | 0.4981 0.0407 0.6165 -0.2196 0.5674 | 0
x4 | -0.4231 -0.3830 0.6107 0.5463 -0.0531 | 0
x5 | -0.2362 0.9172 0.1828 0.2600 0.0435 | 0
------------------------------------------------------------------------------