Confidence intervals for model prediction

Question

I am following along with a statsmodels tutorial An OLS model is fitted with ``` formula = 'S ~ C(E) + C(M) + X' lm = ols(formula, salary_table).fit() print lm.summary() ``` Predicted values are provided through: `lm.predict({'X' : [12], 'M' : [1], 'E' : [2]})` The result is returned as a single value array. Is there a method to also return confidence intervals for the predicted value (prediction intervals) in statsmodels? Thanks.

Accepted Answer

We've been meaning to make this easier to get to. You should be able to use

```
from statsmodels.sandbox.regression.predstd import wls_prediction_std
prstd, iv_l, iv_u = wls_prediction_std(results)
```

If you have any problems, please file an issue on github.

Confidence intervals for model prediction

Solution

Problem