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ANOVA: Graphical

ANOVA: Graphical. Cereal Example: nknw677.sas. Y = number of cases of cereal sold (CASES) X = design of the cereal package (PKGDES) r = 4 (there were 4 designs tested) n i = 5, 5, 4, 5 (one store had a fire) n T = 19. Cereal Example: input. data cereal;

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ANOVA: Graphical

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  1. ANOVA: Graphical

  2. Cereal Example: nknw677.sas Y = number of cases of cereal sold (CASES) X = design of the cereal package (PKGDES) r = 4 (there were 4 designs tested) ni = 5, 5, 4, 5 (one store had a fire) nT = 19

  3. Cereal Example: input data cereal; infile‘H:\My Documents\Stat 512\CH16TA01.DAT'; input cases pkgdes store; procprintdata=cereal; run;

  4. Cereal Example: Scatterplot title1h=3'Types of packaging of Cereal'; title2h=2'Scatterplot'; axis1label=(h=2); axis2label=(h=2angle=90); symbol1v=circle i=nonec=purple; procgplotdata=cereal; plot cases*pkgdes /haxis=axis1 vaxis=axis2; run;

  5. Cereal Example: ANOVA procglmdata=cereal; classpkgdes; model cases=pkgdes/xpxinversesolution; meanspkgdes; run;

  6. Cereal Example: Means procmeansdata=cereal; var cases; bypkgdes; outputout=cerealmeansmean=avcases; procprintdata=cerealmeans; run; title2h=2'plot of means'; symbol1v=circle i=join; procgplotdata=cerealmeans; plotavcases*pkgdes/haxis=axis1 vaxis=axis2; run;

  7. Cereal Example: Means (cont)

  8. ANOVA Table

  9. ANOVA test

  10. Cereal Example: ANOVA table procglmdata=cereal; classpkgdes; model cases=pkgdes; run;

  11. Cereal Example: Design Matrix

  12. Cereal Example: Inverse procglmdata=cereal; classpkgdes; model cases=pkgdes/ xpxinversesolution; meanspkgdes; run;

  13. Cereal Example: /xpx

  14. Cereal Example: /inverse

  15. Cereal Example: /solution

  16. Cereal Example: ANOVA

  17. Cereal Example: Means (nknw698.sas) procmeansdata=cereal printalltypes; classpkgdes; var cases; outputout=cerealmeansmean=mclass; run; The MEANS Procedure

  18. Cereal Example: Means (cont) procprintdata=cerealmeans; run;

  19. Cereal Example: Explanatory Variables data cereal; set cereal; x1=(pkgdeseq1)-(pkgdeseq4); x2=(pkgdeseq2)-(pkgdeseq4); x3=(pkgdeseq3)-(pkgdeseq4); procprintdata=cereal; run;

  20. Cereal Example: Explanatory Variables (cont)

  21. Cereal Example: Regression procregdata=cereal; model cases=x1 x2 x3; run;

  22. Cereal Example: Regression (cont)

  23. Cereal Example: ANOVA procglmdata=cereal; classpkgdes; model cases=pkgdes; run;

  24. Cereal Example: Comparison Regression ANOVA

  25. Cereal Example: Regression (cont)

  26. Cereal Example: Means procmeansdata=cereal printalltypes; classpkgdes; var cases; outputout=cerealmeansmean=mclass; run; The MEANS Procedure

  27. Cereal Example: nknw677a.sas Y = number of cases of cereal sold (CASES) X = design of the cereal package (PKGDES) r = 4 (there were 4 designs tested) ni = 5, 5, 4, 5 (one store had a fire) nT = 19

  28. Cereal Example: Plotting Means title1h=3'Types of packaging of Cereal'; procglmdata=cereal; classpkgdes; model cases=pkgdes; outputout=cerealmeans p=means; run; title2h=2'plot of means'; axis1label=(h=2); axis2label=(h=2angle=90); symbol1v=circle i=nonec=blue; symbol2v=nonei=join c=red; procgplotdata=cerealmeans; plot cases*pkgdes means*pkgdes/overlayhaxis=axis1 vaxis=axis2; run;

  29. Cereal Example: Means (cont)

  30. Cereal Example: CI (1) (nknw711.sas) procmeansdata=cereal meanstdstderrclmmaxdec=2; classpkgdes; var cases; run; The MEANS Procedure

  31. Cereal Example: CI (2) procglmdata=cereal; classpkgdes; model cases=pkgdes; meanspkgdes/tclm; run; The GLM Procedure t Confidence Intervals for cases

  32. Cereal Example: CI

  33. Cereal Example: CI Bonferroni Correction procglmdata=cereal; classpkgdes; model cases=pkgdes; meanspkgdes/bonclm; run; The GLM Procedure Bonferroni t Confidence Intervals for cases

  34. Cereal Example: CI – Bonferroni Correction

  35. Cereal Example: Significance Test procmeansdata=cereal meanstdstderrtprobtmaxdec=2; classpkgdes; var cases; run;

  36. Cereal Example: CI for i - j procglmdata=cereal; classpkgdes; model cases=pkgdes; meanspkgdes/cldifflsdtukeybonscheffedunnett("2"); meanspkgdes/linestukey; run;

  37. Cereal Example: CI for i - j - LSD t Tests (LSD) for cases

  38. Cereal Example: CI for i - j – LSD (cont)

  39. Cereal Example: CI for i - j - Tukey Tukey's Studentized Range (HSD) Test for cases

  40. Cereal Example: CI for i - j - Scheffé Scheffe's Test for cases

  41. Cereal Example: CI for i - j - Bonferroni Bonferroni (Dunn) t Tests for cases

  42. Cereal Example: CI for i - j - Dunnett Dunnett's t Tests for cases

  43. Cereal Example: CI for i - j – Tukey (lines)

  44. Cereal Example: Contrasts procglmdata=cereal; classpkgdes; model cases = pkgdes; contrast'(u1+u2)/2-(u3+u4)/2' pkgdes .5.5 -.5 -.5; estimate'(u1+u2)/2-(u3+u4)/2' pkgdes.5.5 -.5 -.5; run;

  45. Cereal Example: Multiple Contrasts procglmdata=cereal; classpkgdes; model cases = pkgdes; contrast'u1-(u2+u3+u4)/3' pkgdes 1-.3333-.3333-.3333; estimate'u1-(u2+u3+u4)/3'pkgdes3 -1 -1 -1/divisor=3; contrast'u2=u3=u4'pkgdes01 -10, pkgdes001 -1; run;

  46. Training Example: (nknw742.sas) Y = number of acceptable pieces X = hours of training (6 hrs, 8 hrs, 10 hrs, 12 hrs) n = 7

  47. Training Example: input data training; infile'I:\My Documents\STAT 512\CH17TA06.DAT'; input product trainhrs; procprintdata=training; run; data training; set training; hrs=2*trainhrs+4; hrs2=hrs*hrs; procprintdata=training; run;

  48. Training Example: ANOVA procglmdata=training; classtrainhrs; model product=hrs trainhrs / solution; run;

  49. Training Example: ANOVA (cont)

  50. Training Example: Scatterplot Title1h=3'product vs. hrs'; axis1label=(h=2); axis2label=(h=2angle=90); symbol1v = circle i = rl; procgplotdata=training; plot product*hrs/haxis=axis1 vaxis=axis2; run;

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