On Wed, 3 Apr 2002 08:13:18 +0200, "Laurence" <[EMAIL PROTECTED]> wrote:
> Mr Ulrich, > > No it's only to make a Chi square test on residuals against a Chi square > value. > This test is needing to prove if the model i have find by least square > method is valid or not. > Then i've to verify is the residual distribution is Normal or not. > I've to do a Chi square test. But in this test we have to find different > frequencies in different intervals. This reminds me of why the Artificial Intelligence approach to statistical consulting doesn't work yet, and why it will never work with a small decision tree. It's true, that 'normal' is a useful condition for residuals. It's true, that there is a chi-squared test for normality, built on something like a contingency table. But it is true that the X^2 test is not a very powerful test for normality, in general, and it is especially (in my opinion) not very appropriate as a test on residuals, since what matters for residuals is non-normality of other sorts (and I don't know a test for them, either). - an extreme outlier or two may invalidate any least-squares statistics, without disturbing that X^2. If I had to *test* for normality of residuals, I would certainly prefer either of the other two popular tests (Shapiro-Wilks; or K-S) over that one, by a large margin. Robert Dawson says a lot of appropriate things in his post. -- Rich Ulrich, [EMAIL PROTECTED] http://www.pitt.edu/~wpilib/index.html . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
