I'm having a problem concerning cross-correlation and was hoping someone
could help explain.
Here's what I'm doing:
I create two random signals (each 100 points from gaussian distribution
from -1 to 1) and find the maximum cross-correlation value (either
negative or positive, whichever has the l
really too high of a value and was being
biased (or artifically inflated) by the transform. That is what I was disputing.
-Tony
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// G. Anthony Reina, MD //
// The Neurosciences Institute //
//
kind of smoke and mirrors, a "trick" that
somehow makes the analysis flawed. In Sokal and Rohlf's Biometry (3rd ed.,
1995), it gives a nice description of this on p. 411.
-Tony
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// G. Anthony Reina
the same conclusions, but I'd really
like to have a little more solid footing in the statistical theory of any biases I
may be introducing.
Thanks.
-Tony
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// G. Anthony Reina, MD //
// The Neurosciences Institute //
// 1064
eoretical merit? I
can't see how this can be so. I thought that the square-root transform
was a pretty sound way of reducing your chance of biasing the analysis
if the data is non-normal (which most parametric tests require).
Thanks.
-Tony
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I'm looking for a way to show how two continuous signals are correlated
over time. In other words, if x(t) and y(t) are correlated signals (with
some phase lag between them), does that correlation change over time?
(and if so, then how does it vary)
What I'd ideally like to get is something like
I have two physiological signals (discharge rate of a neuron and
electrical voltage [EMG] of a muscle). I'd like to determine the
cross-correlation between the two signals as a function of time (i.e. if
a "relationship" between the two signals exists, how does this
"relationship" change over time)
[EMAIL PROTECTED] wrote:
> I have found a difference between the results produced by SPSS and
> SYSTAT in linear regression with no constant term. Below are the
> results from the programs. As you can see the adjusted R2 given by the
> 2 programs is different. Which one is correct?
>
Quoting f
