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statsmodels.discrete.discrete_model.BinaryResults.conf_int

BinaryResults.conf_int(alpha=0.05, cols=None, method='default')

Returns the confidence interval of the fitted parameters.

Parameters:

alpha : float, optional

The significance level for the confidence interval. ie., The default alpha = .05 returns a 95% confidence interval.

cols : array-like, optional

cols specifies which confidence intervals to return

method : string

Not Implemented Yet Method to estimate the confidence_interval. “Default” : uses self.bse which is based on inverse Hessian for MLE “hjjh” : “jac” : “boot-bse” “boot_quant” “profile”

Returns:

conf_int : array

Each row contains [lower, upper] limits of the confidence interval for the corresponding parameter. The first column contains all lower, the second column contains all upper limits.

Notes

The confidence interval is based on the standard normal distribution. Models wish to use a different distribution should overwrite this method.

Examples

>>> import statsmodels.api as sm
>>> data = sm.datasets.longley.load()
>>> data.exog = sm.add_constant(data.exog)
>>> results = sm.OLS(data.endog, data.exog).fit()
>>> results.conf_int()
array([[-5496529.48322745, -1467987.78596704],
       [    -177.02903529,      207.15277984],
       [      -0.1115811 ,        0.03994274],
       [      -3.12506664,       -0.91539297],
       [      -1.5179487 ,       -0.54850503],
       [      -0.56251721,        0.460309  ],
       [     798.7875153 ,     2859.51541392]])
>>> results.conf_int(cols=(2,3))
array([[-0.1115811 ,  0.03994274],
       [-3.12506664, -0.91539297]])

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