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GaelVaroquauxGaelVaroquaux
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Shorter lines for small devices
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‎packages/statistics/index.rst‎

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@@ -456,7 +456,7 @@ We can inspect the various statistics derived from the fit::
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>>> print(model.summary()) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE +REPORT_UDIFF
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OLS Regression Results
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==============================================================================
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Dep. Variable: y R-squared: 0.804
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Model: OLS Adj. R-squared: 0.794
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Method: Least Squares F-statistic: 74.03
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No. Observations: 20 AIC: 120.0
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Df Residuals: 18 BIC: 122.0
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Df Model: 1
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==============================================================================
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coef std err t P>|t| [95.0% Conf. Int.]
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------------------------------------------------------------------------------
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Intercept -5.5335 1.036 -5.342 0.000 -7.710 -3.357
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x 2.9369 0.341 8.604 0.000 2.220 3.654
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==============================================================================
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Omnibus: 0.100 Durbin-Watson: 2.956
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Prob(Omnibus): 0.951 Jarque-Bera (JB): 0.322
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Skew: -0.058 Prob(JB): 0.851
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Kurtosis: 2.390 Cond. No. 3.03
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.. topic:: Terminology:
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>>> model = ols("VIQ ~ Gender + 1", data).fit()
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>>> print(model.summary()) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE +REPORT_UDIFF
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OLS Regression Results
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==============================================================================
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Dep. Variable: VIQ R-squared: 0.015
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Model: OLS Adj. R-squared: -0.010
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Method: Least Squares F-statistic: 0.5969
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No. Observations: 40 AIC: 368.8
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Df Residuals: 38 BIC: 372.2
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Df Model: 1
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coef std err t P>|t| [95.0% Conf. Int.]
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-----------------------------------------------------------------------...
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Intercept 109.4500 5.308 20.619 0.000 98.704 120.196
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Gender[T.Male] 5.8000 7.507 0.773 0.445 -9.397 20.997
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Omnibus: 26.188 Durbin-Watson: 1.709
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Prob(Omnibus): 0.000 Jarque-Bera (JB): 3.703
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Skew: 0.010 Prob(JB): 0.157
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Kurtosis: 1.510 Cond. No. 2.62
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.. topic:: **Tips on specifying model**
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>>> print(model.summary()) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE +REPORT_UDIFF
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OLS Regression Results
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coef std err t P>|t| [95.0% Conf. Int.]
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Intercept 113.4500 3.683 30.807 0.000 106.119 120.781
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type[T.piq] -2.4250 5.208 -0.466 0.643 -12.793 7.943
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@@ -635,7 +635,7 @@ Such a model can be seen in 3D as fitting a plane to a cloud of (`x`,
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>>> model = ols('sepal_width ~ name + petal_length', data).fit()
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>>> print(model.summary()) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE +REPORT_UDIFF
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OLS Regression Results
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Dep. Variable: sepal_width R-squared: 0.478
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Model: OLS Adj. R-squared: 0.468
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Method: Least Squares F-statistic: 44.63
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No. Observations: 150 AIC: 84.37
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Df Residuals: 146 BIC: 96.41
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Df Model: 3
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coef std err t P>|t| [95.0% Conf. Int.]
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Intercept 2.9813 0.099 29.989 0.000 2.785 3.178
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name[T.versicolor] -1.4821 0.181 -8.190 0.000 -1.840 -1.124
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name[T.virginica] -1.6635 0.256 -6.502 0.000 -2.169 -1.158
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petal_length 0.2983 0.061 4.920 0.000 0.178 0.418
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Omnibus: 2.868 Durbin-Watson: 1.753
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Prob(Omnibus): 0.238 Jarque-Bera (JB): 2.885
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Skew: -0.082 Prob(JB): 0.236
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Kurtosis: 3.659 Cond. No. 54.0
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@@ -818,7 +818,7 @@ Do wages increase more with education for males than females?
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gender[T.male] 0.2750 0.093 2.972 0.003 0.093 0.457
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education 0.0415 0.005 7.647 0.000 0.031 0.052
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education:gender[T.male] -0.0134 0.007 -1.919 0.056 -0.027 0.000
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Can we conclude that education benefits males more than females?

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