Gene Gallagher wrote:
> I have recently seen examples of the thrip fallacy in the op-ed
> pages of the Boston Globe. Massachusetts has implemented
> state-wide standardized testing and has increased state funding
> for school districts with low test scores. Statistical analysis
> reveals that Fi
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"Donald F. Burrill" schrieb:
> On Wed, 10 May 2000, Johannes Hartig wrote:
>
> > I guess I have to accept there is no way to customize within * between
> > interactions in GLM. Thanks for the tip using regression, but I think
> > in future I'd rather try to give a meaning to the default intera
- Original Message -
From: Gene Gallagher > Here is an error that is subtle, but very
common. The statistical
> test (multiple regression) was applied perfectly, but the
> statistical inference was wrong.
> My first reference to this type of error is in the classic,
> but highly controve
Jeff Li wrote:
>
> test
I gotta say...I like the rotating ORACLE in the
v card. Cheap trick, but very effective made
better by keeping it out of the body of the
message.
Required statistical content: which test?
===
I would like to obtain a prediction equation using linear regression for
some data that I have collected. I have read in some stats books that
linear regression has 4 assumptions, 2 of them being that 1) data is
normally distributed and 2) constant variance. In SAS, I have run
univariate analysi
I'm not sure if this will work for your experiment but have you tried
respecifying the model in terms of a nested design.
Using syntax you can type A(B) in the DESIGN subcommand which means A nested
within B. The code example below would fit the model
Y = Constant + A+ B(A)
UNIANOVA
Y BY A B
Mike:
It's really the error terms in the regression model that are required to
have normal distributions with constant variance. We check this by looking
at the properties of the residuals from the regression. You shouldn't expect
the response (dependent) variable to have a normal distribution wi
"G. Anthony Reina" wrote:
> 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)
Depending on
