Missing p-values in Proc GLM

[Wedgery325]

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?

[PGStats]

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