statsmodels.regression.linear_model.RegressionResults.get_prediction¶

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

Compute prediction results.

Parameters:¶

exogarray_like, optional

The values for which you want to predict.

transformbool, 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.

weightsarray_like, optional

Weights interpreted as in WLS, used for the variance of the predicted residual.

row_labelslist

A list of row labels to use. If not provided, read exog is available.

**kwargs

Some models can take additional keyword arguments, see the predict method of the model for the details.

Returns:¶

linear_model.PredictionResults

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.