When making predictions, it is important that the data used to fit the model is similar to future populations to which you want to apply the prediction. You should be careful of making predictions outside the range of the observed data. Assumptions met for the observed data may not be met outside the range. Non-constant variance can cause confidence intervals for the predicted values to become unrealistically narrow or so large as to be useless. Alternatively, a different fit function may better describe the unobserved data outside the range.
When making multiple predictions of the population mean at different sets of predictor values the confidence intervals can be simultaneous or individual. A simultaneous interval ensures you achieve the confidence level simultaneously for all predictions, whereas individual intervals only ensure confidence for the individual prediction. With individual inferences, the chance of at least one interval not including the true value increases with the number of predictions.
From the Statistical Reference Guide for Analyse-it 6.24.0: https://analyse-it.com/docs/user-guide/fit-model/linear/prediction
We use essential cookies to run the site. With your permission we'd also like to use analytics and
advertising cookies to see how visitors find and use Analyse-it, so we can keep improving it and reach
more people like you.
You can change your mind at any time, see our
Privacy policy.