Good comment, Paige--
"> A well-designed experiment will yield regression estimates with more
> desirable properties than a poorly-designed experiment will.
> Specifically, the parameter estimates may have smaller variance in a
> well-design experiment, and the parameters will be less correlated (or
> uncorrelated) with each other. The predicted values of the responses
> likewise will have smaller variance in a well-designed experiment."
However, it is safest to be sure that the "packaged" analyses do what
the researcher wants. Do many "packaged COVARIANCE algorithms" still
assume NO INTERACTION? Does SAS (or other stat packages) warn us
when there is a "missing cell" in an ANOVA-LIKE GLM computation?
-- Joe
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----- Original Message -----
From: "Paige Miller" <[EMAIL PROTECTED]>
To: <[EMAIL PROTECTED]>
Sent: Wednesday, June 28, 2000 11:08 AM
Subject: Re: Novice questions about regression analysis.
> Wen-Feng Hsiao wrote:
> >
> > Dear listers,
> >
> > I am stuck with the experiment design of my dissertation. My experiment
> > would like to investigate the influences of different factors of stimuli
> > on the subject's response (each factor is a continuous variable), and
> > further build a regression model for these relations. My questions are:
> >
> > 1. It seems that no experiment-design issues related to Regression
> > Analysis are discussed in the usual statistics textbook. Why? Does it
> > mean one needn't consider the experiment design if he uses Regression
> > Analysis to analyze his data?
>
> A well-designed experiment will yield regression estimates with more
> desirable properties than a poorly-designed experiment will.
> Specifically, the parameter estimates may have smaller variance in a
> well-design experiment, and the parameters will be less correlated (or
> uncorrelated) with each other. The predicted values of the responses
> likewise will have smaller variance in a well-designed experiment.
>
> > 2. Due to the measure of the dependent variable is the participants'
> > subjective responses, to remove unrelated subject-specific variables, I
> > am considering to employ a within-subject design. But there seems no
> > statistical packages ready for dealing with within-subject design of
> > Regression Analysis?
>
> SAS and JMP will perform these analyses, although the manual may not
> specifically call them 'within-subject' analyses. Other packages
> probably will handle them as well, but I cannot advise you of specifics.
>
> > Suppose a design in which each of the n subjects gives rise to a Y
> > observation under each of c different conditions, then a total of N=ncY
> > observations could be obtained. How can I use Regression Analysis to
> > analyze these observations?
>
> The model will predict the response Y as a function of the subject and
> each of the design variables, plus any desired interactions between
> design variables, interactions between subject and design variables, and
> polynomial terms (if desired) involving design variables.
>
>
> --
> Paige Miller
> Eastman Kodak Company
> [EMAIL PROTECTED]
>
> "It's nothing until I call it!" -- Bill Klem, NL Umpire
> "Those black-eyed peas tasted all right to me" -- Dixie Chicks
>
>
>
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