Suppose I have a single-classification ANOVA, where the sample means show ordinal trend among successive levels. I construct a CONTRAST to test for trend, in the usual manner. Fine - but I'm puzzled how to interpret the parameter output from ESTIMATE (for the same contrast).

For example, consider the following code snippet:

data test;

do trt=1 to 5;

do i=1 to 5000;

x=normal(0)*1.25+trt;

output;

end;

end;

proc glm;

class trt;

model x=trt / ss3;

lsmeans trt;

contrast 'linear' trt 2 1 0 -1 -2;

estimate 'linear' trt 2 1 0 -1 -2;

estimate '1 v 2' trt 1 -1 0 0 0;

estimate '2 v 3' trt 0 1 -1 0 0;

estimate '3 v 4' trt 0 0 1 -1 0;

estimate '4 v 5' trt 0 0 0 1 -1;

run;

So, 5 treatment levels, linear increase of the treatment mean by 1 for each level of trt. So, in fact, the 'slope' of the increase in the treatment effect is 1 (confirmed by each of the paired estimate statement contrasting successive treatment means). However, the output from the linear ESTIMATE is (for a given simulated sample) something like

estimate: -9.9968 SE: 0.056

So, rounding off the estimate to -10 (which is what it should be...

(2)(1)+(1)(2)+(0)(3)+(-1)(4)+(-2)(5) = 10

how do I interpret it? It clearly isn't the slope of the change in treatment effect (even if I take it and divide it by 5 - the number of levels of the treatment - it still isn't). While I *know* this is an acceptable approach to testing significance of a trend, I also need to estimate the slope of the trend. So, how to go from what ESTIMATE gives me, to what I actually want/need.

Pointers to the obvious?

Thanks in advance.