First you need to transpose the data so you have
Wideget S1 S2 S3
1 xx xx xx
2 xx xx xx
...
6 xx xx xx
Then you can use the MANOVA (Fit Models Platform) to do a repeated measures
analysis on those three measures. I assume that for each widget S1 is a
measurement in the same location, ie all theS1's are comparable, etc.
You could also use the random effects univariate approach by stacking the data:
Widget Location Measurement
1 1 xx
1 2 xx
1 3 xx
2 1 xx
2 2 xx
etc
then use fit models with measurement as Y, and Widget and location and possibly
their interaction in the model. If widget is a random sample then make widget
a random effect - ie select widget in the model and choose 'random effect' from
the Attributes menu below.
Location may also be a random effect.
The analysis will then tell you what proportion of the variation is due to
widget and what proportion due to location.
Of locations S1 to S3 are just random locations on the widget and don't
correspond, ie the labels could be permuted without changing the meaning, then
you can run location as a nested effect within widget.
Cheers
Gunter
-----Original Message-----
From: jmp-l-owner@lists.biostat.wustl.edu
[mailto:jmp-l-owner@lists.biostat.wustl.edu]On Behalf Of roncross
Sent: Friday, 1 April 2005 11:47 AM
To: jmp-l@lists.biostat.wustl.edu
Subject: [jmp-l] repeated measurements
Ok, I have a question about how to look at repeated measurements in jmp.
How do I perform an analysis for repeated measurements:
For example, I have the following data:
w1 w2 w3 w4
w5 w6
s1 s1 s1 s1
s1 s1
s2 s2 s2 s2
s2 s2
s3 s3 s3 s3
s3 s3
w1 through w6 are independent runs of wigets. s1, s2, s3 are different
measurements location on a given widget, but each widget is measured in
the same location.
What do I need to do in jmp to get the variation from run to run and
the variation within each run? If I plot Y by X in a oneway anova, am I
making the assumption that s1, s2, s3 are independent.
thx
RLC
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