I guess I would approach this in a completely different manner.  I see repeated measures and a random effect here.  Thus, I would run as far away from PROC GLM as possible.  Your professor is absolutely right about PROC GLM and error terms.   First, try recoding your data into long format:   data long; set s; time=0; avcounts=avg_counts0;output; time=1; avcounts=avg_counts1;output; time=2; avcounts=avg_counts2;output; drop avg_counts0 avg_counts1 avg_counts2; run;   proc mixed data=long; class time treatment rep subjectid; /* This assumes that you have separate measurements by subject within rep */ model avcounts=time treatment time*treatment; random rep; repeated time/type=ar(1) subject=subjectid; run;   Steve Denham ... View more

I haven't read the article, but if the effect of changing this concentration is linear on the final salmonella measurements, then this is relatively easy to incorporate into the analysis. If the effect is non-linear and known, we could also incorporate this into the analysis. If the effect of changing concentration is unknown, then it could be difficult to use.   Also, some people would just lump this changing of concentration into the variability due to replicates. ... View more