> > Rich Ulrich wrote:
> me >
> > > > Plus, I've run
the multiple
> > > > regression without the transform and seen only about a 5%
difference
> > > > (not much).
> > >
> > > - damned if I know what that sentence means. You mean, like,
> > > accoun
Rich Ulrich wrote:
> - Since you don't see the grievous problem with that, I will try to
> point to it, and recommend that you need more advice than you are apt
> to receive over the Net.
>
> When you approach a limit, such as 100%, it is wise to consider what
> you have as the DIFFERENCE, or 10
On Tue, 23 May 2000 13:49:38 -0700, "G. Anthony Reina" <[EMAIL PROTECTED]>
wrote:
> Rich Ulrich wrote:
me >
> > > Plus, I've run the multiple
> > > regression without the transform and seen only about a 5% difference
> > > (not much).
> >
> > - dam
Rich Ulrich wrote:
> > Plus, I've run the multiple
> > regression without the transform and seen only about a 5% difference
> > (not much).
>
> - damned if I know what that sentence means. You mean, like,
> accounting for 99% of the variance, instea
On Mon, 22 May 2000 11:49:44 -0700, "G. Anthony Reina" <[EMAIL PROTECTED]>
wrote:
> We use multiple linear regression to perform our analyses. Because we
> work with binned data (discharge frequency of a neuron) which follow a
> non-normal (Poisson) distribution, we typically use the square root
Herman Rubin wrote:
> Is there enough independence that the counts should be Poisson?
>
> If so, the square root transformation does stabilize the
> variance, but it introduces a bias. In addition, any
> non-linear transformation destroys the linearity of the
> model.
>
y use the square root
>transform on the dependent variable (discharge rate of the neuron).
>(Actually, the transformation is sqrt(spike rate + 3/8) )
Is there enough independence that the counts should be Poisson?
If so, the square root transformation does stabilize the
variance, but it introduces a
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You can try a straight Poisson regression. If the conclus
We use multiple linear regression to perform our analyses. Because we
work with binned data (discharge frequency of a neuron) which follow a
non-normal (Poisson) distribution, we typically use the square root
transform on the dependent variable (discharge rate of the neuron).
(Actually, the transf
