I agree that rho=0 as typically used is silly. Well are
you are arguing then for the Bayesian framework of getting a probability
distribution on rho.??
dennis roberts wrote:
>
> At 11:58 AM 4/11/00 -0400, you wrote:
> >at the bottom
> >
> >
> >it would be easy to show whether rho is more likely
at the bottom
dennis roberts wrote:
>
> At 11:26 AM 4/11/00 -0400, you wrote:
>
> >dennis roberts wrote:
> > >
> > > this was not about a difference in rhos .. just the rho singly from that
> > > population ...
> > >
> >
> >
> >
> >
> >It can be framed similarly replace mu1-mu2 with rho
> >
>
dennis roberts wrote:
>
> this was not about a difference in rhos .. just the rho singly from that
> population ...
>
It can be framed similarly replace mu1-mu2 with rho
If the null hypothesis is H0:rho=0
and the alternative is H1:rho>0
What does the test say about rho if we reject H
If the null hypothesis is H0: mu1-mu2=0
and the alternative is H1: mu1-mu2>0
What does the test say about mu1-mu2 if we reject H0 at level
alpha(say at the magical 0.05)? Not much on its own. However, what
if we plan a statistical experiment as follows:
Given a desired power of 0.
here are two sample r values ... done in minitab ... and the associated output
Correlations: C52, C53
Pearson correlation of C52 and C53 = 0.599
P-Value = 0.000
MTB > corr c54 c55
Correlations: C54, C55
Pearson correlation of C54 and C55 = 0.586
P-Value = 0.075
now, minitab prints out a p
