> > model1 <- aov(sales ~ store + week + shelfspace, subset=(category==1)) > summary(model1) Df Sum Sq Mean Sq F value Pr(>F) store 5 44456 8891.2 27.0049 5.154e-08 *** week 5 3450 690.1 2.0959 0.110596 shelfspace 6 7989 1331.5 4.0441 0.008864 ** Residuals 19 6256 329.2 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > TukeyHSD(model1, "shelfspace") Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = sales ~ store + week + shelfspace, subset = (category == 1)) $shelfspace diff lwr upr p adj 6-4 9.8333333 -24.575640 44.242307 0.9610684 8-4 9.7000000 -26.388436 45.788436 0.9709332 10-4 23.6666667 -10.742307 58.075640 0.3120727 12-4 42.5000000 8.091027 76.908973 0.0099595 14-4 21.1666667 -13.242307 55.575640 0.4344175 18-4 -16.5000000 -80.873295 47.873295 0.9769457 8-6 -0.1333333 -36.221769 35.955102 1.0000000 10-6 13.8333333 -20.575640 48.242307 0.8342896 12-6 32.6666667 -1.742307 67.075640 0.0695074 14-6 11.3333333 -23.075640 45.742307 0.9259723 18-6 -26.3333333 -90.706628 38.039961 0.8233253 10-8 13.9666667 -22.121769 50.055102 0.8565174 12-8 32.8000000 -3.288436 68.888436 0.0898272 14-8 11.4666667 -24.621769 47.555102 0.9367854 18-8 -26.2000000 -91.486437 39.086437 0.8353889 12-10 18.8333333 -15.575640 53.242307 0.5648652 14-10 -2.5000000 -36.908973 31.908973 0.9999799 18-10 -40.1666667 -104.539961 24.206628 0.4184105 14-12 -21.3333333 -55.742307 13.075640 0.4255872 18-12 -59.0000000 -123.373295 5.373295 0.0856223 18-14 -37.6666667 -102.039961 26.706628 0.4908371 > > model2 <- aov(sales ~ store + week + shelfspace, subset=(category==2)) > summary(model2) Df Sum Sq Mean Sq F value Pr(>F) store 5 8286.7 1657.33 22.2810 1.514e-07 *** week 5 981.7 196.33 2.6395 0.0546 . shelfspace 5 395.0 79.00 1.0621 0.4103 Residuals 20 1487.7 74.38 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > TukeyHSD(model2, "shelfspace") Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = sales ~ store + week + shelfspace, subset = (category == 2)) $shelfspace diff lwr upr p adj 4-2 8.6666667 -6.984873 24.31821 0.5228752 6-2 5.1666667 -10.484873 20.81821 0.8995178 8-2 5.5000000 -10.151540 21.15154 0.8737920 10-2 10.6666667 -4.984873 26.31821 0.3066048 12-2 6.0000000 -9.651540 21.65154 0.8293731 6-4 -3.5000000 -19.151540 12.15154 0.9794227 8-4 -3.1666667 -18.818207 12.48487 0.9867757 10-4 2.0000000 -13.651540 17.65154 0.9984374 12-4 -2.6666667 -18.318207 12.98487 0.9939476 8-6 0.3333333 -15.318207 15.98487 0.9999998 10-6 5.5000000 -10.151540 21.15154 0.8737920 12-6 0.8333333 -14.818207 16.48487 0.9999785 10-8 5.1666667 -10.484873 20.81821 0.8995178 12-8 0.5000000 -15.151540 16.15154 0.9999983 12-10 -4.6666667 -20.318207 10.98487 0.9319073 > > model3 <- aov(sales ~ store + week + shelfspace, subset=(category==3)) > summary(model3) Df Sum Sq Mean Sq F value Pr(>F) store 5 6192.1 1238.43 24.9711 5.84e-08 *** week 5 315.8 63.16 1.2736 0.3139 shelfspace 5 161.8 32.36 0.6525 0.6631 Residuals 20 991.9 49.59 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > TukeyHSD(model3, "shelfspace") Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = sales ~ store + week + shelfspace, subset = (category == 3)) $shelfspace diff lwr upr p adj 9-6 -0.6666667 -13.446811 12.11348 0.9999806 12-6 1.5000000 -11.280145 14.28014 0.9989608 15-6 2.8333333 -9.946811 15.61348 0.9801839 18-6 4.8333333 -7.946811 17.61348 0.8369912 21-6 4.6666667 -8.113478 17.44681 0.8555013 12-9 2.1666667 -10.613478 14.94681 0.9940848 15-9 3.5000000 -9.280145 16.28014 0.9515400 18-9 5.5000000 -7.280145 18.28014 0.7531548 21-9 5.3333333 -7.446811 18.11348 0.7754112 15-12 1.3333333 -11.446811 14.11348 0.9994120 18-12 3.3333333 -9.446811 16.11348 0.9603538 21-12 3.1666667 -9.613478 15.94681 0.9680231 18-15 2.0000000 -10.780145 14.78014 0.9959229 21-15 1.8333333 -10.946811 14.61348 0.9972918 21-18 -0.1666667 -12.946811 12.61348 1.0000000 > > model4 <- aov(sales ~ store + week + shelfspace, subset=(category==4)) > summary(model4) Df Sum Sq Mean Sq F value Pr(>F) store 5 6475.8 1295.16 20.2809 3.274e-07 *** week 5 529.5 105.89 1.6582 0.1908 shelfspace 5 508.5 101.69 1.5924 0.2078 Residuals 20 1277.2 63.86 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > TukeyHSD(model4, "shelfspace") Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = sales ~ store + week + shelfspace, subset = (category == 4)) $shelfspace diff lwr upr p adj 9-6 -3.1666667 -17.668987 11.33565 0.9814596 12-6 1.0000000 -13.502320 15.50232 0.9999227 15-6 -1.5000000 -16.002320 13.00232 0.9994361 18-6 -1.8333333 -16.335654 12.66899 0.9985157 21-6 8.3333333 -6.168987 22.83565 0.4840456 12-9 4.1666667 -10.335654 18.66899 0.9412007 15-9 1.6666667 -12.835654 16.16899 0.9990611 18-9 1.3333333 -13.168987 15.83565 0.9996823 21-9 11.5000000 -3.002320 26.00232 0.1732752 15-12 -2.5000000 -17.002320 12.00232 0.9936112 18-12 -2.8333333 -17.335654 11.66899 0.9886961 21-12 7.3333333 -7.168987 21.83565 0.6142419 18-15 -0.3333333 -14.835654 14.16899 0.9999997 21-15 9.8333333 -4.668987 24.33565 0.3116334 21-18 10.1666667 -4.335654 24.66899 0.2792000 >