Thanks, Robert, and to anyone else who has kindly answered what I
realised, belatedly, was a simple question (given that I was looking for
the simple normal case.)
Regards,
Alan
"Robert J. MacG. Dawson" wrote:
>
> Alan McLean wrote:
> >
> > Hi to all.
> >
> > Can anyone tell me what is the di
well, i don't have the answer but, a quick simulation (when the ratio of
variances is about 2) is as follows
maybe this helps in some strange way
===
MTB > rand 1 c1-c25;
SUBC> norm 100 5.
MTB > rand 1 c26-c50;
SUBC> norm 100 7.07.
MTB > rstdev c1-c25, c51
MTB > rstdev c26-c50, c
Alan McLean wrote:
>
> Hi to all.
>
> Can anyone tell me what is the distribution of the ratio of sample
> variances when the ratio of population vriances is not 1, but some
> specified other number?
*If* the population distributions are normal (and this is not a
robust assumption -
On Fri, 4 May 2001, Alan McLean wrote:
> Can anyone tell me what is the distribution of the ratio of sample
> variances when the ratio of population variances is not 1, but some
> specified other number?
Depends. If the two samples on which the variances are based are
_independent_, s^2(1)/s^2
Hi to all.
Can anyone tell me what is the distribution of the ratio of sample
variances when the ratio of population vriances is not 1, but some
specified other number?
I want to be able to calculate the probability of getting a sample ratio
of 1 when the population ratio is, say, 2.
Many than
Thank you for your reply, the only one I have got so far.
"Donald F. Burrill" wrote:
>
> Looks to me like a simple typo. The null hypothesis is
> H0: beta1 = beta2 = 0
> and I would attribute the "+" sign to a typing error ("+" is a shifted
> "=" on most keyboards).
You may be right
Looks to me like a simple typo. The null hypothesis is
H0: beta1 = beta2 = 0
and I would attribute the "+" sign to a typing error ("+" is a shifted
"=" on most keyboards).
While the _interpretation_ of non-zero estimates of the betas
(should H0 be rejected) depends on the spec
Suppose that I have a factor with three levels A, B, and C. If it
is used in a GLM model as a covariate, I will have two parameter
beta1 and beta2 (assuming they are for level B and C). To test a
statement "Any of the last two levels (either level B or level C)
has a different effect on the respon
