On 29 Mar 2004 09:47:30 -0800, [EMAIL PROTECTED] (Paul R Swank) wrote: > The size of the error in prediction is smallest at the mean of the > predictors and increases as the values for the predictors move toward the > extremes. One woul need to take this into account. Any good regression book
Now we can talk about two different 'predictions' -- Paul S. is describing the nature of actual predictions for new values, where extreme predictions are increasingly inaccurate. Up to now, I have assumed that the 'error in prediction' was a reference to the error of the fitted values, concerning only the sample in hand. Here, the errors are supposed to be (by assumption) homogeneous, across the range of the prediction; and that is one of the assumptions that you can check by looking at the right plots of the residuals. > (like Draper and Smith) will have the formula, which is difficult to > represent in text format. Var(mean Y hat) = X(0)'(X'X)inv X(0)sigma hat > squared: where X(0) represents the vector of X's you are predicting from. > Assuming normality, then a t could be determined that would give an > approximate idea of how far the observation was from the prediction. -- Rich Ulrich, [EMAIL PROTECTED] http://www.pitt.edu/~wpilib/index.html - I need a new job, after March 31. Openings? - . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
