Thanks, Milo! I agree with everything you write, with the exception of
(essentially) one matter of fact, as noted below.
On Fri, 21 Jan 2000, Milo Schield wrote in part:
> Consider the Minitab PULSE dataset.
> Pulse1 is first measurement of pulse (beats per minute)
> Pulse2 is second measu
Interpretation requires a knowledge of the subject matter -- not just of
p-values.
One should look for plausible explanations for the coefficients:
Plausibility is a sign of a good model.
A lack of plausibility raises questions about a model.
Plausibility requires a knowledge of the subjects matte
Here is a bit of Don's example, and then a closing comment about what
I had said, about interpreting coefficients.
On 10 Jan 2000 00:20:59 -0800, [EMAIL PROTECTED] (Donald F.
Burrill) wrote:
< snip, much example >
> the coefficients b3 and b4 are indistinguishable from zero. Dropping
> SEX a
>From Burrill and Ulrich's discussion.
All this orthagonalization is fine. To me the bottom line is still the
residuals and if the model can do a reasonable prediction just outside the
data set boundaries. Obviously the different methods and pruning out of
variables will give different values of
Sorry, this turned out to be rather longer than I'd anticipated.
Maybe I should have broken it into parts...
On Wed, 22 Dec 1999, Rich Ulrich wrote:
<< There were several earlier messages, and then
I thought Don Burrill said most of what needed to be said -- >>
[ snip, vario
Well put Donald. The only additional points I wish to make are that in my
career I've
never seen balanced factorial data with normal errors. Only in the case where
the
study was done in a balanced way (i.e., experimental study, no missing data,
etc.) AND
where the model is a regression model wit
In response to a comment of mine:
> Incidentally, I'd strongly recommend constructing interaction variables
> that are orthogonal at least to their own main effects (and lower-order
> interactions, when there are any), and possibly orthogonal to some or all
> of the apparently irrelevant other pr
There were several earlier messages, and then
I thought Don Burrill said most of what needed to be said --
On 20 Dec 1999 22:43:52 -0800, [EMAIL PROTECTED] (Donald F.
Burrill) wrote:
>
> For openers, I quote from Pedhazur (2nd edition), p 329 (summary for
> Chapter 9), so that we're all on th
"Donald F. Burrill" wrote:
> Inicidentally, I'd strongly recommend constructing interaction variables
> that are orthogonal at least to their own main effects (and lower-order
> interactions, when there are any), and possibly orthogonal to some or all
> of the apparently irrelevant other predic
For openers, I quote from Pedhazur (2nd edition), p 329 (summary for
Chapter 9), so that we're all on the same wavelength, more or less:
"... Regardless of the coding method used, the results of the
overall analysis are the same. ..."
(This is the point that other respondents
Thanks for your replies, I have 5 minutes to reply to some of your comments because my
wife and friends are waiting for me to get home so we can go to New Orleans.
1. I agree with Joe that the term "dummy" in dummy coding is a rather dumb term to use
for indicator variables. The terms is widely
On Thu, 16 Dec 1999, Burke Johnson wrote:
> A student of mine is getting ready to develop a GLM prediction model
> that will include a mixture of categorical and quantitative predictor
> variables. We will probably not include interaction terms in the model
> (i.e., it will be a main effects on
On 16 Dec 1999 09:55:51 -0800, [EMAIL PROTECTED] (Burke
Johnson) wrote (with no control on line length) :
< snip -- a GLM prediction model ... a main effects
only model >
" Here's my question: Do you suggest using dummy
coding (0,1) or effects coding (1,0,-1) for the
categorical variables
Furthermore, if you stick to doing sensible tests, the tests are independent of
the coding. For example, interaction tests are invariant to coding and so are
global tests of combined main effect + interaction (e.g., H0: age is not a risk factor
for
either sex vs. Ha: age is a risk factor for at
Burke Johnson wrote:
>
> Hi,
>
> A student of mine is getting ready to develop a GLM prediction model that will
>include a mixture of categorical and quantitative predictor variables. We will
>probably not include interaction terms in the model (i.e., it will be a main effects
>only model).
>
- Original Message -
From: Burke Johnson <[EMAIL PROTECTED]>
To: <[EMAIL PROTECTED]>; <[EMAIL PROTECTED]>
Sent: Thursday, December 16, 1999 9:13 AM
Subject: Prediction Model Question
| Hi,
|
| A student of mine is getting ready to develop a GLM prediction model tha
Hi,
A student of mine is getting ready to develop a GLM prediction model that will include
a mixture of categorical and quantitative predictor variables. We will probably not
include interaction terms in the model (i.e., it will be a main effects only model).
Here's my question: Do you suggest
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