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statsmodels.discrete.discrete_model.LogitResults.conf_int

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

Returns the confidence interval of the fitted parameters.

Parameters:

alpha : float, optional

The alpha 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 “jhj” : “jac” : “boot-bse” “boot_quant” “profile”

Returns:

conf_int : array

Each row contains [lower, upper] confidence interval

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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