On Tue, 16 Jun 2009, jose romero wrote:
Hello list:
(This is probably a stupid question).?Is there a "quick and easy" way to
confirm the gauss-markov conditions of a linear multiple regression
model?
Well, those 'conditions' are _assumptions_, and as often happens they
can be hard to verify.?
That the mean of the residuals is 0 can easily be tested for.
Wrong. In general, it cannot. The residuals at issue here are not the
deviations of the data from the fitted values, which are set to have mean
zero. Rather they are the unobserved differences between what is observed
and what would have been predicted given the true values of the regression
coefficients.
The
normality of the residuals as well (shapiro-wilk?).?But what about
homoscedasticity?
Well, if you have a good candidate for departures from homoscedasticity,
you are in business. But you have to 'know something' about your setup
to be this lucky. Or, if you have replicate observations for some values
of the regressors - as in designed experiments with replication - it is
possible. If neither if these applies, it will usually be difficult.
And independence of residuals with respect to the
model variables?
This can be tough. If there is a variable that is omitted and that is
related to (e.g. correlated with) your regressors, then the assumption
fails. But you cannot test for this in most circumstances.
Also, certain kinds of measurement error will cause the assumption
to fail.
HTH,
Chuck
Thanks in advance
[[alternative HTML version deleted]]
Charles C. Berry (858) 534-2098
Dept of Family/Preventive Medicine
E mailto:cbe...@tajo.ucsd.edu UC San Diego
http://famprevmed.ucsd.edu/faculty/cberry/ La Jolla, San Diego 92093-0901
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