Download Example F15: SAS Program, Proc Mixed Output | STAT 479 and more Study notes Statistics in PDF only on Docsity!
Example^ F15 SAS Programdata^ parts;input^ process $ @;do^ team =^1 to^^3 ; input^ finish @;output;end;datalines;1 29 34 161 12 24 171 21 29 242 42 35 362 37 39 182 32 30 233 38 23 163 35 36 273 28 30 194 16 10 124 13 16 174 25 20 18; run ; proc^ mixed^ data=parts noclprint^ noinfo
cl covtest;class process team;model finish = process /ddfm=satterth;random team(process);lsmeans process/diff cl adj=bon;estimate 'Process 4 mean:'intercept 3 process 0 0 0 3 | team(process) 0 0 0 0 0 0 0 0 0 1 1 1 /divisor=^3 ; title 'Analysis of Surface Finish by Process using PROC MIXED';
run ;
Proc MIXED Output^ Analysis^ of^ Surface Finish by Process
using^ PROC^ MIXED^1 The Mixed ProcedureIteration HistoryIteration Evaluations -2 Res Log Like^ Criterion 0 1 222.99895630 1 1 220.51004767^ 0.00000000Convergence criteria met. Covariance Parameter EstimatesStandard Z Cov^ Parm^ Estimate^ Error^ Value
Pr^ Z^ Alpha^ Lower^ Upper team(process)^ 15.9722^ 14.^
1.12^ 0.1317^ 0.05^ 4.^
Residual^ 35.3056^ 10.^
3.46^ 0.0003^ 0.05^ 21.5255^ 68.3270Fit Statistics-2 Res Log Likelihood^ 220.5AIC (smaller is better)^ 224.5AICC (smaller is better)^ 224.9BIC (smaller is better)^ 225.
Analysis^ of^ Surface Finish by Process^ using^
PROC^ MIXED^2 The Mixed ProcedureLeast Squares MeansStandard Effect^ process^ Estimate^ Error^ DF^ t^ Value^ Pr >^ |t|^ Alpha^ Lower^ Differences^ of^ Least Squares^ MeansStandardEffect process _process Estimate^ Error^ DF^ t Value^ Pr >^ |t|^
- process^1 22.8889 3. Upper
- 8 7.53 <.0001 0.05 15.
- process^2 32.4444 3.
- 8 10.67 <.0001 0.05 25.
- process^3 28.0000 3.
- 8 9.21 <.0001 0.05 20.
- process^4 16.3333 3.
- 8 5.37 0.0007 0.05 9.
- process^1 2 -9.5556 4. Adjustment Adj P
- 8 -2.22 0.0570 Bonferroni 0.
- process^1 3 -5.1111 4.
- 8 -1.19 0.2687 Bonferroni 1.
- process^1 4 6.5556 4.
- 8 1.52 0.1659 Bonferroni 0.
- process^2 3 4.4444 4.
- 8 1.03 0.3316 Bonferroni 1.
- process^2 4 16.1111 4.
- 8 3.75 0.0057 Bonferroni 0.
- process^3 4 11.6667 4. - 8 2.71 0.0265 Bonferroni 0.1592Differences of Least Squares MeansAdj AdjEffect process _process Alpha Lower Upper Lower Upperprocess 1 2 0.05 -19.4724 0.3613 -24.5163 5.4052process 1 3 0.05 -15.0280 4.8057 -20.0718 9.8496process 1 4 0.05 -3.3613 16.4724 -8.4052 21.5163process 2 3 0.05 -5.4724 14.3613 -10.5163 19.4052process 2 4 0.05 6.1943 26.0280 1.1504 31.0718process 3 4 0.05 1.7498 21.5835 -3.2941 26.
