* * * * * * * * * * * * * * * * * * * * * * * *

富裕/keyword

Rank mode:0Origin weights:False

OLS Regression Results
Dep. Variable: y R-squared: 0.428
Model: OLS Adj. R-squared: 0.394
Method: Least Squares F-statistic: 12.70
Date: Wed, 18 Nov 2015 Prob (F-statistic): 0.00239
Time: 22:22:13 Log-Likelihood: -66.246
No. Observations: 19 AIC: 136.5
Df Residuals: 17 BIC: 138.4
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
const 41.6149 3.689 11.282 0.000 33.832 49.397
x1 -1.2478 0.350 -3.564 0.002 -1.986 -0.509
Omnibus: 0.293 Durbin-Watson: 1.692
Prob(Omnibus): 0.864 Jarque-Bera (JB): 0.271
Skew: 0.235 Prob(JB): 0.873
Kurtosis: 2.652 Cond. No. 20.4

* * * * * * * * * * * * * * * * * * * * * * * *

* * * * * * * * * * * * * * * * * * * * * * * *

贫穷/keyword

Rank mode:0Origin weights:False

OLS Regression Results
Dep. Variable: y R-squared: 0.330
Model: OLS Adj. R-squared: 0.290
Method: Least Squares F-statistic: 8.356
Date: Wed, 18 Nov 2015 Prob (F-statistic): 0.0102
Time: 22:22:13 Log-Likelihood: -66.197
No. Observations: 19 AIC: 136.4
Df Residuals: 17 BIC: 138.3
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
const 31.1844 3.679 8.476 0.000 23.422 38.947
x1 -1.0095 0.349 -2.891 0.010 -1.746 -0.273
Omnibus: 4.214 Durbin-Watson: 2.075
Prob(Omnibus): 0.122 Jarque-Bera (JB): 2.691
Skew: 0.918 Prob(JB): 0.260
Kurtosis: 3.159 Cond. No. 20.4

* * * * * * * * * * * * * * * * * * * * * * * *

* * * * * * * * * * * * * * * * * * * * * * * *

富裕/富裕

Rank mode:0Origin weights:False

OLS Regression Results
Dep. Variable: y R-squared: 0.022
Model: OLS Adj. R-squared: -0.036
Method: Least Squares F-statistic: 0.3768
Date: Wed, 18 Nov 2015 Prob (F-statistic): 0.547
Time: 22:22:13 Log-Likelihood: -58.637
No. Observations: 19 AIC: 121.3
Df Residuals: 17 BIC: 123.2
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
const 15.3896 2.471 6.227 0.000 10.175 20.604
x1 -0.1440 0.235 -0.614 0.547 -0.639 0.351
Omnibus: 0.232 Durbin-Watson: 2.173
Prob(Omnibus): 0.890 Jarque-Bera (JB): 0.422
Skew: 0.122 Prob(JB): 0.810
Kurtosis: 2.311 Cond. No. 20.4

* * * * * * * * * * * * * * * * * * * * * * * *

* * * * * * * * * * * * * * * * * * * * * * * *

贫穷/贫穷

Rank mode:0Origin weights:False

OLS Regression Results
Dep. Variable: y R-squared: 0.003
Model: OLS Adj. R-squared: -0.056
Method: Least Squares F-statistic: 0.04814
Date: Wed, 18 Nov 2015 Prob (F-statistic): 0.829
Time: 22:22:13 Log-Likelihood: -60.263
No. Observations: 19 AIC: 124.5
Df Residuals: 17 BIC: 126.4
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
const 24.2881 2.692 9.022 0.000 18.608 29.968
x1 0.0561 0.256 0.219 0.829 -0.483 0.595
Omnibus: 1.522 Durbin-Watson: 2.550
Prob(Omnibus): 0.467 Jarque-Bera (JB): 1.309
Skew: 0.544 Prob(JB): 0.520
Kurtosis: 2.315 Cond. No. 20.4

* * * * * * * * * * * * * * * * * * * * * * * *

* * * * * * * * * * * * * * * * * * * * * * * *

教育/keyword

Rank mode:0Origin weights:False

OLS Regression Results
Dep. Variable: y R-squared: 0.014
Model: OLS Adj. R-squared: -0.045
Method: Least Squares F-statistic: 0.2327
Date: Wed, 18 Nov 2015 Prob (F-statistic): 0.636
Time: 22:22:13 Log-Likelihood: -96.883
No. Observations: 19 AIC: 197.8
Df Residuals: 17 BIC: 199.7
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
const 123.7025 18.499 6.687 0.000 84.673 162.732
x1 0.8469 1.756 0.482 0.636 -2.858 4.551
Omnibus: 1.455 Durbin-Watson: 1.868
Prob(Omnibus): 0.483 Jarque-Bera (JB): 1.226
Skew: 0.472 Prob(JB): 0.542
Kurtosis: 2.188 Cond. No. 20.4

* * * * * * * * * * * * * * * * * * * * * * * *

* * * * * * * * * * * * * * * * * * * * * * * *

外交/keyword

Rank mode:0Origin weights:False

OLS Regression Results
Dep. Variable: y R-squared: 0.231
Model: OLS Adj. R-squared: 0.186
Method: Least Squares F-statistic: 5.104
Date: Wed, 18 Nov 2015 Prob (F-statistic): 0.0373
Time: 22:22:13 Log-Likelihood: -79.968
No. Observations: 19 AIC: 163.9
Df Residuals: 17 BIC: 165.8
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
const 39.7370 7.595 5.232 0.000 23.713 55.761
x1 -1.6286 0.721 -2.259 0.037 -3.149 -0.108
Omnibus: 21.360 Durbin-Watson: 1.751
Prob(Omnibus): 0.000 Jarque-Bera (JB): 28.764
Skew: 1.874 Prob(JB): 5.68e-07
Kurtosis: 7.720 Cond. No. 20.4

* * * * * * * * * * * * * * * * * * * * * * * *

* * * * * * * * * * * * * * * * * * * * * * * *

教育/教育

Rank mode:0Origin weights:False

OLS Regression Results
Dep. Variable: y R-squared: 0.093
Model: OLS Adj. R-squared: 0.039
Method: Least Squares F-statistic: 1.733
Date: Wed, 18 Nov 2015 Prob (F-statistic): 0.205
Time: 22:22:13 Log-Likelihood: -37.781
No. Observations: 19 AIC: 79.56
Df Residuals: 17 BIC: 81.45
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
const 3.7243 0.825 4.517 0.000 1.985 5.464
x1 0.1030 0.078 1.316 0.205 -0.062 0.268
Omnibus: 4.869 Durbin-Watson: 2.865
Prob(Omnibus): 0.088 Jarque-Bera (JB): 1.538
Skew: -0.135 Prob(JB): 0.463
Kurtosis: 1.633 Cond. No. 20.4

* * * * * * * * * * * * * * * * * * * * * * * *

* * * * * * * * * * * * * * * * * * * * * * * *

外交/外交

Rank mode:0Origin weights:False

OLS Regression Results
Dep. Variable: y R-squared: 0.029
Model: OLS Adj. R-squared: -0.028
Method: Least Squares F-statistic: 0.5105
Date: Wed, 18 Nov 2015 Prob (F-statistic): 0.485
Time: 22:22:13 Log-Likelihood: -51.946
No. Observations: 19 AIC: 107.9
Df Residuals: 17 BIC: 109.8
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
const 21.6788 1.738 12.475 0.000 18.012 25.345
x1 0.1179 0.165 0.714 0.485 -0.230 0.466
Omnibus: 3.402 Durbin-Watson: 2.892
Prob(Omnibus): 0.183 Jarque-Bera (JB): 1.478
Skew: 0.521 Prob(JB): 0.478
Kurtosis: 3.884 Cond. No. 20.4

* * * * * * * * * * * * * * * * * * * * * * * *

* * * * * * * * * * * * * * * * * * * * * * * *

经济/keyword

Rank mode:0Origin weights:False

OLS Regression Results
Dep. Variable: y R-squared: 0.182
Model: OLS Adj. R-squared: 0.134
Method: Least Squares F-statistic: 3.792
Date: Wed, 18 Nov 2015 Prob (F-statistic): 0.0682
Time: 22:22:14 Log-Likelihood: -118.62
No. Observations: 19 AIC: 241.2
Df Residuals: 17 BIC: 243.1
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
const 278.2404 58.080 4.791 0.000 155.702 400.779
x1 -10.7352 5.513 -1.947 0.068 -22.366 0.896
Omnibus: 9.288 Durbin-Watson: 2.813
Prob(Omnibus): 0.010 Jarque-Bera (JB): 6.534
Skew: 1.323 Prob(JB): 0.0381
Kurtosis: 4.121 Cond. No. 20.4

* * * * * * * * * * * * * * * * * * * * * * * *

* * * * * * * * * * * * * * * * * * * * * * * *

环保/keyword

Rank mode:0Origin weights:False

OLS Regression Results
Dep. Variable: y R-squared: 0.009
Model: OLS Adj. R-squared: -0.050
Method: Least Squares F-statistic: 0.1486
Date: Wed, 18 Nov 2015 Prob (F-statistic): 0.705
Time: 22:22:14 Log-Likelihood: -87.429
No. Observations: 19 AIC: 178.9
Df Residuals: 17 BIC: 180.7
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
const 76.5497 11.248 6.806 0.000 52.819 100.280
x1 -0.4115 1.068 -0.385 0.705 -2.664 1.841
Omnibus: 3.248 Durbin-Watson: 2.535
Prob(Omnibus): 0.197 Jarque-Bera (JB): 2.403
Skew: -0.860 Prob(JB): 0.301
Kurtosis: 2.721 Cond. No. 20.4

* * * * * * * * * * * * * * * * * * * * * * * *

* * * * * * * * * * * * * * * * * * * * * * * *

经济/经济

Rank mode:0Origin weights:False

OLS Regression Results
Dep. Variable: y R-squared: 0.006
Model: OLS Adj. R-squared: -0.052
Method: Least Squares F-statistic: 0.1106
Date: Wed, 18 Nov 2015 Prob (F-statistic): 0.744
Time: 22:22:14 Log-Likelihood: -70.888
No. Observations: 19 AIC: 145.8
Df Residuals: 17 BIC: 147.7
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
const 19.9075 4.710 4.227 0.001 9.971 29.844
x1 -0.1487 0.447 -0.333 0.744 -1.092 0.794
Omnibus: 3.842 Durbin-Watson: 2.958
Prob(Omnibus): 0.146 Jarque-Bera (JB): 2.307
Skew: 0.640 Prob(JB): 0.316
Kurtosis: 1.872 Cond. No. 20.4

* * * * * * * * * * * * * * * * * * * * * * * *

* * * * * * * * * * * * * * * * * * * * * * * *

环保/环保

Rank mode:0Origin weights:False

OLS Regression Results
Dep. Variable: y R-squared: 0.022
Model: OLS Adj. R-squared: -0.035
Method: Least Squares F-statistic: 0.3913
Date: Wed, 18 Nov 2015 Prob (F-statistic): 0.540
Time: 22:22:14 Log-Likelihood: -108.98
No. Observations: 19 AIC: 222.0
Df Residuals: 17 BIC: 223.9
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
const 126.4592 34.971 3.616 0.002 52.678 200.241
x1 2.0762 3.319 0.626 0.540 -4.927 9.079
Omnibus: 3.477 Durbin-Watson: 2.322
Prob(Omnibus): 0.176 Jarque-Bera (JB): 2.833
Skew: -0.891 Prob(JB): 0.243
Kurtosis: 2.367 Cond. No. 20.4

* * * * * * * * * * * * * * * * * * * * * * * *

* * * * * * * * * * * * * * * * * * * * * * * *

经济/keyword

Rank mode:0Origin weights:False

OLS Regression Results
Dep. Variable: y R-squared: 0.132
Model: OLS Adj. R-squared: 0.081
Method: Least Squares F-statistic: 2.585
Date: Wed, 18 Nov 2015 Prob (F-statistic): 0.126
Time: 22:22:14 Log-Likelihood: -122.75
No. Observations: 19 AIC: 249.5
Df Residuals: 17 BIC: 251.4
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
const 311.6595 72.183 4.318 0.000 159.366 463.953
x1 -11.0165 6.851 -1.608 0.126 -25.472 3.439
Omnibus: 5.741 Durbin-Watson: 2.813
Prob(Omnibus): 0.057 Jarque-Bera (JB): 4.102
Skew: 1.136 Prob(JB): 0.129
Kurtosis: 3.131 Cond. No. 20.4

* * * * * * * * * * * * * * * * * * * * * * * *

* * * * * * * * * * * * * * * * * * * * * * * *

稳定/keyword

Rank mode:0Origin weights:False

OLS Regression Results
Dep. Variable: y R-squared: 0.369
Model: OLS Adj. R-squared: 0.332
Method: Least Squares F-statistic: 9.938
Date: Wed, 18 Nov 2015 Prob (F-statistic): 0.00581
Time: 22:22:14 Log-Likelihood: -79.924
No. Observations: 19 AIC: 163.8
Df Residuals: 17 BIC: 165.7
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
const 69.9101 7.577 9.226 0.000 53.923 85.897
x1 -2.2672 0.719 -3.152 0.006 -3.785 -0.750
Omnibus: 0.653 Durbin-Watson: 2.789
Prob(Omnibus): 0.721 Jarque-Bera (JB): 0.656
Skew: 0.163 Prob(JB): 0.720
Kurtosis: 2.150 Cond. No. 20.4

* * * * * * * * * * * * * * * * * * * * * * * *

* * * * * * * * * * * * * * * * * * * * * * * *

经济/经济

Rank mode:0Origin weights:False

OLS Regression Results
Dep. Variable: y R-squared: 0.015
Model: OLS Adj. R-squared: -0.043
Method: Least Squares F-statistic: 0.2635
Date: Wed, 18 Nov 2015 Prob (F-statistic): 0.614
Time: 22:22:14 Log-Likelihood: -71.824
No. Observations: 19 AIC: 147.6
Df Residuals: 17 BIC: 149.5
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
const 20.8151 4.947 4.207 0.001 10.377 31.253
x1 -0.2411 0.470 -0.513 0.614 -1.232 0.750
Omnibus: 2.978 Durbin-Watson: 2.949
Prob(Omnibus): 0.226 Jarque-Bera (JB): 2.175
Skew: 0.678 Prob(JB): 0.337
Kurtosis: 2.048 Cond. No. 20.4

* * * * * * * * * * * * * * * * * * * * * * * *

* * * * * * * * * * * * * * * * * * * * * * * *

稳定/稳定

Rank mode:0Origin weights:False

OLS Regression Results
Dep. Variable: y R-squared: 0.030
Model: OLS Adj. R-squared: -0.027
Method: Least Squares F-statistic: 0.5253
Date: Wed, 18 Nov 2015 Prob (F-statistic): 0.478
Time: 22:22:14 Log-Likelihood: -56.501
No. Observations: 19 AIC: 117.0
Df Residuals: 17 BIC: 118.9
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
const 12.7216 2.209 5.760 0.000 8.062 17.381
x1 -0.1519 0.210 -0.725 0.478 -0.594 0.290
Omnibus: 12.097 Durbin-Watson: 2.774
Prob(Omnibus): 0.002 Jarque-Bera (JB): 9.699
Skew: 1.370 Prob(JB): 0.00783
Kurtosis: 5.178 Cond. No. 20.4

* * * * * * * * * * * * * * * * * * * * * * * *

* * * * * * * * * * * * * * * * * * * * * * * *

民主/keyword

Rank mode:0Origin weights:False

OLS Regression Results
Dep. Variable: y R-squared: 0.224
Model: OLS Adj. R-squared: 0.178
Method: Least Squares F-statistic: 4.901
Date: Wed, 18 Nov 2015 Prob (F-statistic): 0.0408
Time: 22:22:14 Log-Likelihood: -83.567
No. Observations: 19 AIC: 171.1
Df Residuals: 17 BIC: 173.0
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
const 61.6966 9.179 6.722 0.000 42.331 81.062
x1 -1.9286 0.871 -2.214 0.041 -3.767 -0.091
Omnibus: 1.617 Durbin-Watson: 2.368
Prob(Omnibus): 0.446 Jarque-Bera (JB): 1.295
Skew: 0.595 Prob(JB): 0.523
Kurtosis: 2.530 Cond. No. 20.4

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* * * * * * * * * * * * * * * * * * * * * * * *

互联网/keyword

Rank mode:0Origin weights:False

OLS Regression Results
Dep. Variable: y R-squared: 0.115
Model: OLS Adj. R-squared: 0.063
Method: Least Squares F-statistic: 2.219
Date: Wed, 18 Nov 2015 Prob (F-statistic): 0.155
Time: 22:22:14 Log-Likelihood: -75.732
No. Observations: 19 AIC: 155.5
Df Residuals: 17 BIC: 157.4
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
const 40.5369 6.077 6.671 0.000 27.716 53.358
x1 -0.8592 0.577 -1.490 0.155 -2.076 0.358
Omnibus: 7.352 Durbin-Watson: 2.036
Prob(Omnibus): 0.025 Jarque-Bera (JB): 4.600
Skew: 1.004 Prob(JB): 0.100
Kurtosis: 4.334 Cond. No. 20.4

* * * * * * * * * * * * * * * * * * * * * * * *

* * * * * * * * * * * * * * * * * * * * * * * *

民主/民主

Rank mode:0Origin weights:False

OLS Regression Results
Dep. Variable: y R-squared: 0.157
Model: OLS Adj. R-squared: 0.108
Method: Least Squares F-statistic: 3.169
Date: Wed, 18 Nov 2015 Prob (F-statistic): 0.0929
Time: 22:22:15 Log-Likelihood: -69.479
No. Observations: 19 AIC: 143.0
Df Residuals: 17 BIC: 144.8
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
const 33.2880 4.373 7.612 0.000 24.062 42.514
x1 0.7389 0.415 1.780 0.093 -0.137 1.615
Omnibus: 2.779 Durbin-Watson: 1.752
Prob(Omnibus): 0.249 Jarque-Bera (JB): 1.696
Skew: 0.732 Prob(JB): 0.428
Kurtosis: 3.012 Cond. No. 20.4

* * * * * * * * * * * * * * * * * * * * * * * *

* * * * * * * * * * * * * * * * * * * * * * * *

互联网/互联网

Rank mode:0Origin weights:False

OLS Regression Results
Dep. Variable: y R-squared: 0.027
Model: OLS Adj. R-squared: -0.031
Method: Least Squares F-statistic: 0.4657
Date: Wed, 18 Nov 2015 Prob (F-statistic): 0.504
Time: 22:22:15 Log-Likelihood: -29.856
No. Observations: 19 AIC: 63.71
Df Residuals: 17 BIC: 65.60
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
const 3.8055 0.543 7.004 0.000 2.659 4.952
x1 0.0352 0.052 0.682 0.504 -0.074 0.144
Omnibus: 0.276 Durbin-Watson: 2.683
Prob(Omnibus): 0.871 Jarque-Bera (JB): 0.441
Skew: -0.193 Prob(JB): 0.802
Kurtosis: 2.361 Cond. No. 20.4

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* * * * * * * * * * * * * * * * * * * * * * * *

清廉/keyword

Rank mode:0Origin weights:False

OLS Regression Results
Dep. Variable: y R-squared: 0.030
Model: OLS Adj. R-squared: -0.027
Method: Least Squares F-statistic: 0.5312
Date: Wed, 18 Nov 2015 Prob (F-statistic): 0.476
Time: 22:22:15 Log-Likelihood: -65.528
No. Observations: 19 AIC: 135.1
Df Residuals: 17 BIC: 136.9
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
const 16.8089 3.552 4.732 0.000 9.315 24.303
x1 -0.2457 0.337 -0.729 0.476 -0.957 0.466
Omnibus: 16.628 Durbin-Watson: 2.610
Prob(Omnibus): 0.000 Jarque-Bera (JB): 16.169
Skew: 1.720 Prob(JB): 0.000308
Kurtosis: 5.931 Cond. No. 20.4

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* * * * * * * * * * * * * * * * * * * * * * * *

腐败/keyword

Rank mode:0Origin weights:False

OLS Regression Results
Dep. Variable: y R-squared: 0.107
Model: OLS Adj. R-squared: 0.055
Method: Least Squares F-statistic: 2.039
Date: Wed, 18 Nov 2015 Prob (F-statistic): 0.171
Time: 22:22:15 Log-Likelihood: -67.784
No. Observations: 19 AIC: 139.6
Df Residuals: 17 BIC: 141.5
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
const 24.7686 4.000 6.193 0.000 16.330 33.207
x1 -0.5421 0.380 -1.428 0.171 -1.343 0.259
Omnibus: 8.763 Durbin-Watson: 2.371
Prob(Omnibus): 0.013 Jarque-Bera (JB): 6.138
Skew: 1.313 Prob(JB): 0.0465
Kurtosis: 3.926 Cond. No. 20.4

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* * * * * * * * * * * * * * * * * * * * * * * *

清廉/清廉

Rank mode:0Origin weights:False

OLS Regression Results
Dep. Variable: y R-squared: 0.370
Model: OLS Adj. R-squared: 0.333
Method: Least Squares F-statistic: 9.998
Date: Wed, 18 Nov 2015 Prob (F-statistic): 0.00570
Time: 22:22:15 Log-Likelihood: -52.975
No. Observations: 19 AIC: 109.9
Df Residuals: 17 BIC: 111.8
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
const 29.6881 1.835 16.183 0.000 25.818 33.559
x1 0.5506 0.174 3.162 0.006 0.183 0.918
Omnibus: 0.084 Durbin-Watson: 2.020
Prob(Omnibus): 0.959 Jarque-Bera (JB): 0.297
Skew: -0.087 Prob(JB): 0.862
Kurtosis: 2.413 Cond. No. 20.4

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* * * * * * * * * * * * * * * * * * * * * * * *

腐败/腐败

Rank mode:0Origin weights:False

OLS Regression Results
Dep. Variable: y R-squared: 0.004
Model: OLS Adj. R-squared: -0.054
Method: Least Squares F-statistic: 0.07236
Date: Wed, 18 Nov 2015 Prob (F-statistic): 0.791
Time: 22:22:15 Log-Likelihood: -67.040
No. Observations: 19 AIC: 138.1
Df Residuals: 17 BIC: 140.0
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
const 39.8018 3.846 10.349 0.000 31.687 47.916
x1 0.0982 0.365 0.269 0.791 -0.672 0.868
Omnibus: 1.400 Durbin-Watson: 2.269
Prob(Omnibus): 0.497 Jarque-Bera (JB): 0.878
Skew: 0.087 Prob(JB): 0.645
Kurtosis: 1.961 Cond. No. 20.4

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* * * * * * * * * * * * * * * * * * * * * * * *

人权/keyword

Rank mode:0Origin weights:False

OLS Regression Results
Dep. Variable: y R-squared: 0.200
Model: OLS Adj. R-squared: 0.153
Method: Least Squares F-statistic: 4.248
Date: Wed, 18 Nov 2015 Prob (F-statistic): 0.0549
Time: 22:22:15 Log-Likelihood: -73.952
No. Observations: 19 AIC: 151.9
Df Residuals: 17 BIC: 153.8
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
const 33.9426 5.534 6.134 0.000 22.268 45.617
x1 -1.0826 0.525 -2.061 0.055 -2.191 0.026
Omnibus: 1.530 Durbin-Watson: 2.332
Prob(Omnibus): 0.465 Jarque-Bera (JB): 1.008
Skew: 0.553 Prob(JB): 0.604
Kurtosis: 2.782 Cond. No. 20.4

* * * * * * * * * * * * * * * * * * * * * * * *

* * * * * * * * * * * * * * * * * * * * * * * *

污染/keyword

Rank mode:0Origin weights:False

OLS Regression Results
Dep. Variable: y R-squared: 0.246
Model: OLS Adj. R-squared: 0.202
Method: Least Squares F-statistic: 5.551
Date: Wed, 18 Nov 2015 Prob (F-statistic): 0.0307
Time: 22:22:15 Log-Likelihood: -72.876
No. Observations: 19 AIC: 149.8
Df Residuals: 17 BIC: 151.6
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
const 38.5105 5.229 7.365 0.000 27.478 49.543
x1 -1.1694 0.496 -2.356 0.031 -2.217 -0.122
Omnibus: 1.804 Durbin-Watson: 2.432
Prob(Omnibus): 0.406 Jarque-Bera (JB): 1.372
Skew: 0.629 Prob(JB): 0.504
Kurtosis: 2.610 Cond. No. 20.4

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* * * * * * * * * * * * * * * * * * * * * * * *

人权/人权

Rank mode:0Origin weights:False

OLS Regression Results
Dep. Variable: y R-squared: 0.200
Model: OLS Adj. R-squared: 0.153
Method: Least Squares F-statistic: 4.262
Date: Wed, 18 Nov 2015 Prob (F-statistic): 0.0546
Time: 22:22:15 Log-Likelihood: -70.732
No. Observations: 19 AIC: 145.5
Df Residuals: 17 BIC: 147.4
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
const 66.9236 4.671 14.327 0.000 57.069 76.779
x1 0.9153 0.443 2.064 0.055 -0.020 1.851
Omnibus: 0.040 Durbin-Watson: 2.293
Prob(Omnibus): 0.980 Jarque-Bera (JB): 0.083
Skew: -0.005 Prob(JB): 0.960
Kurtosis: 2.677 Cond. No. 20.4

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* * * * * * * * * * * * * * * * * * * * * * * *

污染/污染

Rank mode:0Origin weights:False

OLS Regression Results
Dep. Variable: y R-squared: 0.005
Model: OLS Adj. R-squared: -0.053
Method: Least Squares F-statistic: 0.08632
Date: Wed, 18 Nov 2015 Prob (F-statistic): 0.772
Time: 22:22:15 Log-Likelihood: -47.698
No. Observations: 19 AIC: 99.40
Df Residuals: 17 BIC: 101.3
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
const 12.1528 1.390 8.745 0.000 9.221 15.085
x1 -0.0388 0.132 -0.294 0.772 -0.317 0.240
Omnibus: 1.970 Durbin-Watson: 2.296
Prob(Omnibus): 0.373 Jarque-Bera (JB): 0.970
Skew: 0.550 Prob(JB): 0.616
Kurtosis: 3.119 Cond. No. 20.4

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* * * * * * * * * * * * * * * * * * * * * * * *

铁路/keyword

Rank mode:0Origin weights:False

OLS Regression Results
Dep. Variable: y R-squared: 0.150
Model: OLS Adj. R-squared: 0.100
Method: Least Squares F-statistic: 2.995
Date: Wed, 18 Nov 2015 Prob (F-statistic): 0.102
Time: 22:22:16 Log-Likelihood: -74.606
No. Observations: 19 AIC: 153.2
Df Residuals: 17 BIC: 155.1
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
const 51.3251 5.727 8.962 0.000 39.242 63.409
x1 -0.9408 0.544 -1.731 0.102 -2.088 0.206
Omnibus: 3.503 Durbin-Watson: 2.212
Prob(Omnibus): 0.174 Jarque-Bera (JB): 2.171
Skew: 0.827 Prob(JB): 0.338
Kurtosis: 3.099 Cond. No. 20.4

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* * * * * * * * * * * * * * * * * * * * * * * *

飞机/keyword

Rank mode:0Origin weights:False

OLS Regression Results
Dep. Variable: y R-squared: 0.411
Model: OLS Adj. R-squared: 0.377
Method: Least Squares F-statistic: 11.88
Date: Wed, 18 Nov 2015 Prob (F-statistic): 0.00308
Time: 22:22:16 Log-Likelihood: -69.215
No. Observations: 19 AIC: 142.4
Df Residuals: 17 BIC: 144.3
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
const 39.4384 4.313 9.145 0.000 30.340 48.537
x1 -1.4110 0.409 -3.447 0.003 -2.275 -0.547
Omnibus: 1.252 Durbin-Watson: 2.615
Prob(Omnibus): 0.535 Jarque-Bera (JB): 1.097
Skew: 0.438 Prob(JB): 0.578
Kurtosis: 2.214 Cond. No. 20.4

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* * * * * * * * * * * * * * * * * * * * * * * *

铁路/铁路

Rank mode:0Origin weights:False

OLS Regression Results
Dep. Variable: y R-squared: 0.020
Model: OLS Adj. R-squared: -0.038
Method: Least Squares F-statistic: 0.3395
Date: Wed, 18 Nov 2015 Prob (F-statistic): 0.568
Time: 22:22:16 Log-Likelihood: -70.491
No. Observations: 19 AIC: 145.0
Df Residuals: 17 BIC: 146.9
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
const 25.4822 4.612 5.525 0.000 15.751 35.213
x1 0.2551 0.438 0.583 0.568 -0.669 1.179
Omnibus: 0.750 Durbin-Watson: 2.357
Prob(Omnibus): 0.687 Jarque-Bera (JB): 0.628
Skew: -0.397 Prob(JB): 0.730
Kurtosis: 2.595 Cond. No. 20.4

* * * * * * * * * * * * * * * * * * * * * * * *

* * * * * * * * * * * * * * * * * * * * * * * *

飞机/飞机

Rank mode:0Origin weights:False

OLS Regression Results
Dep. Variable: y R-squared: 0.055
Model: OLS Adj. R-squared: -0.001
Method: Least Squares F-statistic: 0.9902
Date: Wed, 18 Nov 2015 Prob (F-statistic): 0.334
Time: 22:22:16 Log-Likelihood: -29.284
No. Observations: 19 AIC: 62.57
Df Residuals: 17 BIC: 64.46
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
const 4.1429 0.527 7.858 0.000 3.030 5.255
x1 -0.0498 0.050 -0.995 0.334 -0.155 0.056
Omnibus: 1.302 Durbin-Watson: 2.539
Prob(Omnibus): 0.522 Jarque-Bera (JB): 0.924
Skew: 0.519 Prob(JB): 0.630
Kurtosis: 2.699 Cond. No. 20.4

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* * * * * * * * * * * * * * * * * * * * * * * *

战争/keyword

Rank mode:0Origin weights:False

OLS Regression Results
Dep. Variable: y R-squared: 0.001
Model: OLS Adj. R-squared: -0.058
Method: Least Squares F-statistic: 0.01891
Date: Wed, 18 Nov 2015 Prob (F-statistic): 0.892
Time: 22:22:16 Log-Likelihood: -80.265
No. Observations: 19 AIC: 164.5
Df Residuals: 17 BIC: 166.4
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
const 43.9881 7.715 5.702 0.000 27.712 60.265
x1 0.1007 0.732 0.138 0.892 -1.444 1.646
Omnibus: 1.723 Durbin-Watson: 2.038
Prob(Omnibus): 0.423 Jarque-Bera (JB): 1.207
Skew: 0.602 Prob(JB): 0.547
Kurtosis: 2.723 Cond. No. 20.4

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* * * * * * * * * * * * * * * * * * * * * * * *

主权/keyword

Rank mode:0Origin weights:False

OLS Regression Results
Dep. Variable: y R-squared: 0.249
Model: OLS Adj. R-squared: 0.204
Method: Least Squares F-statistic: 5.622
Date: Wed, 18 Nov 2015 Prob (F-statistic): 0.0298
Time: 22:22:16 Log-Likelihood: -71.348
No. Observations: 19 AIC: 146.7
Df Residuals: 17 BIC: 148.6
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
const 33.9030 4.825 7.027 0.000 23.723 44.083
x1 -1.0858 0.458 -2.371 0.030 -2.052 -0.120
Omnibus: 0.932 Durbin-Watson: 2.399
Prob(Omnibus): 0.628 Jarque-Bera (JB): 0.884
Skew: 0.423 Prob(JB): 0.643
Kurtosis: 2.367 Cond. No. 20.4

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* * * * * * * * * * * * * * * * * * * * * * * *

战争/战争

Rank mode:0Origin weights:False

OLS Regression Results
Dep. Variable: y R-squared: 0.365
Model: OLS Adj. R-squared: 0.328
Method: Least Squares F-statistic: 9.773
Date: Wed, 18 Nov 2015 Prob (F-statistic): 0.00615
Time: 22:22:16 Log-Likelihood: -83.437
No. Observations: 19 AIC: 170.9
Df Residuals: 17 BIC: 172.8
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
const 10.0787 9.116 1.106 0.284 -9.154 29.312
x1 2.7049 0.865 3.126 0.006 0.879 4.530
Omnibus: 0.337 Durbin-Watson: 2.060
Prob(Omnibus): 0.845 Jarque-Bera (JB): 0.491
Skew: 0.130 Prob(JB): 0.782
Kurtosis: 2.257 Cond. No. 20.4

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* * * * * * * * * * * * * * * * * * * * * * * *

主权/主权

Rank mode:0Origin weights:False

OLS Regression Results
Dep. Variable: y R-squared: 0.022
Model: OLS Adj. R-squared: -0.036
Method: Least Squares F-statistic: 0.3779
Date: Wed, 18 Nov 2015 Prob (F-statistic): 0.547
Time: 22:22:16 Log-Likelihood: -14.093
No. Observations: 19 AIC: 32.19
Df Residuals: 17 BIC: 34.07
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
const 2.4855 0.237 10.487 0.000 1.985 2.986
x1 0.0138 0.022 0.615 0.547 -0.034 0.061
Omnibus: 12.343 Durbin-Watson: 2.704
Prob(Omnibus): 0.002 Jarque-Bera (JB): 10.100
Skew: 1.368 Prob(JB): 0.00641
Kurtosis: 5.297 Cond. No. 20.4

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* * * * * * * * * * * * * * * * * * * * * * * *

资本主义/keyword

Rank mode:0Origin weights:False

OLS Regression Results
Dep. Variable: y R-squared: 0.317
Model: OLS Adj. R-squared: 0.277
Method: Least Squares F-statistic: 7.905
Date: Wed, 18 Nov 2015 Prob (F-statistic): 0.0120
Time: 22:22:16 Log-Likelihood: -55.714
No. Observations: 19 AIC: 115.4
Df Residuals: 17 BIC: 117.3
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
const 21.4168 2.119 10.107 0.000 16.946 25.888
x1 -0.5655 0.201 -2.811 0.012 -0.990 -0.141
Omnibus: 1.404 Durbin-Watson: 2.834
Prob(Omnibus): 0.496 Jarque-Bera (JB): 1.196
Skew: 0.545 Prob(JB): 0.550
Kurtosis: 2.431 Cond. No. 20.4

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* * * * * * * * * * * * * * * * * * * * * * * *

社会主义/keyword

Rank mode:0Origin weights:False

OLS Regression Results
Dep. Variable: y R-squared: 0.002
Model: OLS Adj. R-squared: -0.057
Method: Least Squares F-statistic: 0.03173
Date: Wed, 18 Nov 2015 Prob (F-statistic): 0.861
Time: 22:22:17 Log-Likelihood: -148.25
No. Observations: 19 AIC: 300.5
Df Residuals: 17 BIC: 302.4
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
const 263.7744 276.306 0.955 0.353 -319.180 846.729
x1 -4.6716 26.226 -0.178 0.861 -60.003 50.660
Omnibus: 49.028 Durbin-Watson: 2.113
Prob(Omnibus): 0.000 Jarque-Bera (JB): 201.271
Skew: 3.964 Prob(JB): 1.97e-44
Kurtosis: 16.835 Cond. No. 20.4

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* * * * * * * * * * * * * * * * * * * * * * * *

资本主义/资本主义

Rank mode:0Origin weights:False

OLS Regression Results
Dep. Variable: y R-squared: 0.013
Model: OLS Adj. R-squared: -0.045
Method: Least Squares F-statistic: 0.2297
Date: Wed, 18 Nov 2015 Prob (F-statistic): 0.638
Time: 22:22:17 Log-Likelihood: -79.054
No. Observations: 19 AIC: 162.1
Df Residuals: 17 BIC: 164.0
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
const 59.3229 7.238 8.196 0.000 44.052 74.594
x1 0.3293 0.687 0.479 0.638 -1.120 1.779
Omnibus: 2.100 Durbin-Watson: 2.823
Prob(Omnibus): 0.350 Jarque-Bera (JB): 1.039
Skew: -0.050 Prob(JB): 0.595
Kurtosis: 1.859 Cond. No. 20.4

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* * * * * * * * * * * * * * * * * * * * * * * *

社会主义/社会主义

Rank mode:0Origin weights:False

OLS Regression Results
Dep. Variable: y R-squared: 0.001
Model: OLS Adj. R-squared: -0.058
Method: Least Squares F-statistic: 0.01052
Date: Wed, 18 Nov 2015 Prob (F-statistic): 0.920
Time: 22:22:17 Log-Likelihood: -164.72
No. Observations: 19 AIC: 333.4
Df Residuals: 17 BIC: 335.3
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [95.0% Conf. Int.]
const 905.8476 657.245 1.378 0.186 -480.818 2292.513
x1 6.3982 62.383 0.103 0.920 -125.218 138.015
Omnibus: 47.326 Durbin-Watson: 2.117
Prob(Omnibus): 0.000 Jarque-Bera (JB): 182.370
Skew: 3.816 Prob(JB): 2.51e-40
Kurtosis: 16.119 Cond. No. 20.4

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