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Sean, I don't think nesting is appropriate because the levels of B do not vary by A. In other words "+" and "-" mean the same settings regardless of A's value. I worked with your data a bit and suggest the following: 1. Start with YbyX plots of Y vs A and Y vs B (hint; color code rows for B ). 2. Modeling: I would think it would be best to leave 5 and 6 out to model A, B, and A*B. Use Effect Screening and checkout the profile plots. 3. Test A levels 5 and 6 with the other levels at only B = "-". 4. If you were to find that there is no A*B interaction over A levels 1-4 and that B has no effect, then you could consider using all data for the comparison of A levels since the "+" B's would simply be additional replicates at the A levels. Michael Bresnick BMS Medical Imaging Davern, Sean wrote: 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
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