I've completed the following screening experiment:
Factor A (categorical): 6 levels
Factor B (categorical): 2 levels: + / -
Two levels of factor A (5 & 6) are internal controls linking this
experiment's results to other work. However, those levels of factor A were
only run at the - level of factor B since that's how they were run in the
other work. Each treatment was run twice (1 replicate). So, here's the
(un-randomized) design (without the replicates):
A B
1 +
2 +
3 +
4 +
1 -
2 -
3 -
4 -
5 -
6 -
If I analyze the data with
Fit Model(Effects( :A, :B, :A* :B), Y(:Y))
there will be errors (singularities) because the 5+ and 6+ treatments don't
exist. So, I can analyze the data excluding all runs of level 5 and 6.
However, then if I want to know if the performance of runs 5+ and 6+ are
statistically distinguishable from any or all the others (using Tukey's HSD)
I don't know how to get the result.
I'm wondering if I can use the nested model:
Fit Model(Y( :Y), Effects( :A, :B[ :A]), Personality(Standard Least
Squares))
to do the analysis. I believe it is calculating the means correctly but I'm
concerned that it will have incorrect estimates of variances and degrees of
freedom and that the hypothesis testing will be wrong. It certainly gives
different estimates of MSE, etc. but I'm not sure if that is because it now
has more data to work with.
Is use of the nested model appropriate to compare the performance at each
treatment as well as the parameter estimations?
Here is the JMP file including models and scripts for the fits:
<<Experiment Design and Data.JMP>>
Thanks, in advance, for your help.
Sean
Sean Davern
Engineer III
Cell Sciences Process Development
Mail Stop AW2/D2152
Ext. 57074
Experiment Design and Data.JMP
Description: Binary data
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