Hi Gus, I think I see what you mean. When two lines cross, where they cross they will be equal and error will be zero. As the nonparallel lines extend, they grow further apart.
I see a potential problem with this model... there is only one line, the regression of y on x. Where the fit is not perfect, then the predicted values of y will deviate from the regression line on both sides. This deviation is greater in the extremes of x. What you are describing sounds more like an interaction multiple regression model than a simple linear regression. Am I missing something? Best, Bill "Gus Gassmann" <[EMAIL PROTECTED]> wrote in message [EMAIL PROTECTED]">news:[EMAIL PROTECTED]... > [EMAIL PROTECTED] wrote: > > > This is pretty standard stuff. For example, on page57 of Kleinbaum and > > Kuppers book Applied Regressions Analysis..., confidence bands are > > graphically displayed for a regression model. The bands get wider towards > > the ends of the regression slope, thus illustrating wider variation in the > > extremes. The width of the confidence band is a function of the standard > > error of the estimated y value at each level of the predictor variable. I > > remember as a student asking Jamie Algina why this occured but did not get > > an answer, and have not heard one since. Perhaps the band gets wider when > > the predictor (x) is normally distributed but not when x is uniformly > > distributed? > > It's pretty simple and has nothing to do with normal/not normal. > If you have two lines, y = beta_0 + beta_1 x and y = b_0 + b_1 x, > where b_i is close to beta_i, then the lines diverge and the distance between > them increases as you move away from the center (where they typically > intersect). So your confidence limits widen as you move out into the tails. > You can couch this in fancy formulas, if you want, but that's all there is to > it. > . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
