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I have a big problem with this question:
A store that sells soda has noticed that the variation of selling is big
depending on the temperature. To get a opinion about the relationship
between the mean temperature and the sell of soda each day they make a
multiplicative regressionmodel.
sell(y)
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On 16 Jan 2002 11:33:15 -0800, [EMAIL PROTECTED] (Wuzzy) wrote:
> If your beta coefficients are on different scales: like
> you want to know whether temperature or pressure are affecting
> your bread baking more,
>
> Is the way to do this using Beta coefficients calculated
> as Beta=beta*SDx/SDy
On Tue, 15 Jan 2002 23:06:25 GMT, janne <[EMAIL PROTECTED]> wrote:
> Lets say I do a x2(chi) test and have the hypothesis:
>
[snip, some example]
>
> If you can have < in hypothesis, then when is it < and when is it > I
> should use? How do I know which one to use?
>
> I also wonder about t-te
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Thanks so much for the suggestions...
CCC
"Vadim and Oxana Marmer" <[EMAIL PROTECTED]> wrote in message
[EMAIL PROTECTED]">news:[EMAIL PROTECTED]...
> Casella and Berger "Statistical Inference" is a very popular graduate
> level textbook on the topic. It's not related to your field directly, but
If your beta coefficients are on different scales: like
you want to know whether temperature or pressure are affecting
your bread baking more,
Is the way to do this using Beta coefficients calculated
as Beta=beta*SDx/SDy
(SDx=standard deviation of each x)
(SDy=standard deviation of the dependant
there is nothing from stopping you (is there?) trying several methods that
are seen as sensible possibilities ... and seeing what happens?
of course, you might find a transformational algorithm that works BEST (of
those you try) with the data you have but ... A) that still might be an
"optimal
"Case, Brad" wrote:
>
> > Hello. I am hoping that my question can be answered by a statistical
> > expert out there!! (which I am not). I am carrying out a multiple linear
> > regression with two independents. It seems that a square root
> > transformation of the dependent variable effective
> Hello. I am hoping that my question can be answered by a statistical
> expert out there!! (which I am not). I am carrying out a multiple linear
> regression with two independents. It seems that a square root
> transformation of the dependent variable effectively decreases
> heterocscedasticit
hi group,
thanks to Glen and Jon, and sorry in case I did not express myself clearly.
What I want to model are recovery rates, defined as (costs and earnings at
default)/(contract value). That is, there are two or more factors, or RVs,
in the numerator which are summed up, and one in the denomina
Dennis Roberts wrote:
> 1. you could take several methods AT random (after you list out all 50) ...
This is the classical position, I think. However, in practice we
never require random sampling in order to treat people as random.
Clark argues ISTR that we should treat factors as random if sampl
Elias wrote:
Some have already been answered.
> c) how formulas are changing? mean square between... (Ms) for mixed or
> not designs?
Random factors have extra variability associated with them compared
to fixed factors. This variability reflects the fact that you are
sampling a subset of items
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