RegressionResults.get_prediction()

statsmodels.regression.linear_model.RegressionResults.get_prediction

RegressionResults.get_prediction(exog=None, transform=True, weights=None, row_labels=None, **kwds) [source]

compute prediction results

Parameters:

exog : array-like, optional The values for which you want to predict. transform : bool, optional If the model was fit via a formula, do you want to pass exog through the formula. Default is True. E.g., if you fit a model y ~ log(x1) + log(x2), and transform is True, then you can pass a data structure that contains x1 and x2 in their original form. Otherwise, you’d need to log the data first. weights : array_like, optional Weights interpreted as in WLS, used for the variance of the predicted residual. args, kwargs : Some models can take additional arguments or keywords, see the predict method of the model for the details.

Returns:

prediction_results : instance The prediction results instance contains prediction and prediction variance and can on demand calculate confidence intervals and summary tables for the prediction of the mean and of new observations.