Missing p-values in Proc GLM

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Missing p-values in Proc GLM

I have a research experiment to analyze where I would like to know if there is an interaction between two experimental factors. Let's say that the experiment has 4 replications (rep) and 4 treatments (trt) and I will call the measured variable xyz. I use the following language:

proc glm;

class rep trt;

model xyz=rep|trt;

run;

The output will return dots in place of where p-values usually are. If I remove the interaction term from the model statement, I can get a p-value for rep and trt. Why does SAS not return p-values with more terms in the model? Is it a degrees of freedom issue?


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Re: Missing p-values in Proc GLM

Posted in reply to Wedgery325

There is no room for error in your model, or in other words, you used up all your DFs.

The terms are

Overall mean : 1 df

treatment means : 3 df

replicate means : 3 df

interaction treatment x replicate means : 3x3 = 9 df

Total : 16 df = nb of observations

Your model has enough terms to fit each observation exactly. Without an estimate of error, you cannot derive p-values.

PG

PG
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