### Navigation

- index
- modules |
- next |
- previous |
- statsmodels v0.2.0 documentation »
- Sandbox »

# scikits.statsmodels.sandbox.regression.wls_prediction_std

`scikits.statsmodels.sandbox.regression.``wls_prediction_std`(res, exog=None, weights=None, alpha=0.050000000000000003)

  calculate standard deviation and confidence interval for prediction

  applies to WLS and OLS, not to general GLS, that is independently but not identically distributed observations

  Parameters: | res : regression result instance results of WLS or OLS regression required attributes see notes exog : array_like (optional) exogenous variables for points to predict weights : scalar or array_like (optional) weights as defined for WLS (inverse of variance of observation) alpha : float (default: alpha = 0.5) confidence level for two-sided hypothesis
  Returns: | predstd : array_like, 1d standard error of prediction same length as rows of exog interval_l, interval_u : array_like lower und upper confidence bounds

  Notes

  The result instance needs to have at least the following res.model.predict() : predicted values or res.fittedvalues : values used in estimation res.cov_params() : covariance matrix of parameter estimates

  If exog is 1d, then it is interpreted as one observation, i.e. a row vector.

  testing status: not compared with other packages

  References

  Greene p.111 for OLS, extended to WLS by analogy

### Navigation

- index
- modules |
- next |
- previous |
- statsmodels v0.2.0 documentation »
- Sandbox »