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Thanks Matthew.
Yes I shouldn't do this, because the estimator of the mean for each agegroup is depondent on the estimator of the overall mean.
I did with that crude way ... you have discribed and I'm agree with it.
But, the Bonferroni adjustment may be not required in my case, because I'm not compararing the group means beteen them.
Am I right?
-----Original Message-----
From: Swank, Paul R [mailto:Paul.R.Swank@uth.tmc.edu]
Sent: February 15, 2006 5:47 PM
To: Elmaache, Hamani; SAS-L@LISTSERV.UGA.EDU
Subject: RE: Overall mean vs group means
Use trinary or effect coding. Only three effects can be tested since the
last one is the sum of the others.
proc glm data=upsit;
class agegroup;
model smell = agegroup;
contrast 'effect 1' age group 1 0 0 -1;
contrast 'effect 2' age group 0 1 0 -1;
contrast 'effect 3' age group 0 0 1 -1;
means agegroup ;
run;
Paul R. Swank, Ph.D.
Professor, Developmental Pediatrics
Director of Research, Center for Improving the Readiness of Children for
Learning and Education (C.I.R.C.L.E.)
Medical School
UT Health Science Center at Houston
-----Original Message-----
From: SAS(r) Discussion [mailto:SAS-L@LISTSERV.UGA.EDU] On Behalf Of
Elmaache, Hamani
Sent: Wednesday, February 15, 2006 2:47 PM
To: SAS-L@LISTSERV.UGA.EDU
Subject: Overall mean vs group means
Hi there.
I would kile ask you how can I use Proc GLM to compare the Overall mean
of dependent variable smell to each group mean.
Let's say I have data like that:
data upsit;
input agegroup smell @@;
datalines;
1 1.381 1 1.322 1 1.162 1 1.275 1 1.381 1 1.275 1 1.322
1 1.492 1 1.322 1 1.381 1 1.162 1 1.013 1 1.322 1 1.322
1 1.275 1 1.492 1 1.322 1 1.322 1 1.492 1 1.322 1 1.381
1 1.234 1 1.162 1 1.381 1 1.381 1 1.381 1 1.322 1 1.381
1 1.322 1 1.381 1 1.275 1 1.492 1 1.275 1 1.322 1 1.275
1 1.381 1 1.234 1 1.105
2 1.234 2 1.234 2 1.381 2 1.322 2 1.492 2 1.234 2 1.381
2 1.381 2 1.492 2 1.492 2 1.275 2 1.492 2 1.381 2 1.492
2 1.322 2 1.275 2 1.275 2 1.275 2 1.322 2 1.492 2 1.381
2 1.322 2 1.492 2 1.196 2 1.322 2 1.275 2 1.234 2 1.322
2 1.098 2 1.322 2 1.381 2 1.275 2 1.492 2 1.492 2 1.381
2 1.196
3 1.381 3 1.381 3 1.492 3 1.492 3 1.492 3 1.098 3 1.492
3 1.381 3 1.234 3 1.234 3 1.129 3 1.069 3 1.234 3 1.322
3 1.275 3 1.230 3 1.234 3 1.234 3 1.322 3 1.322 3 1.381
4 1.322 4 1.381 4 1.381 4 1.322 4 1.234 4 1.234 4 1.234
4 1.381 4 1.322 4 1.275 4 1.275 4 1.492 4 1.234 4 1.098
4 1.322 4 1.129 4 0.687 4 1.322 4 1.322 4 1.234 4 1.129
4 1.492 4 0.810 4 1.234 4 1.381 4 1.040 4 1.381 4 1.381
4 1.129 4 1.492 4 1.129 4 1.098 4 1.275 4 1.322 4 1.234
4 1.196 4 1.234 4 0.585 4 0.785 4 1.275 4 1.322 4 0.712
4 0.810
;
The Overall mean of smell is Overall_mean= 1.286543478.
I got it using the following code.
proc glm data=upsit;
model smell =
run;
I also got the 4 means for 4 groups, using the code:
proc glm data=upsit;
class agegroup;
model smell = agegroup;
means agegroup ;
run;
Level of
agegroup Mean
Mean1 1.31689474
Mean2 1.34513889
Mean3 1.30614286
Mean4 1.20109302
I want to test if there is a significant difference between Overall_mean
and any mean:
Overall_mean - Mean1 is this difference is significantly defferent from
zero?
Overall_mean - Mean2 is this difference is significantly defferent from
zero?
Overall_mean - Mean3 is this difference is significantly defferent from
zero?
Overall_mean - Mean4 is this difference is significantly defferent from
zero?
Thanks in advance for any help.
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