Exercise 11.22 METHODS AND APPLICATIONS A telemarketing rm has studied the effects of two factors on the response to its television advertisements. The rst factor is the time of day at which the ad is run, while the second is the position of the ad within the hour. The data in the following table, which were obtained by using a completely randomized experimental design, give the number of calls placed to an 800 number following a sample broadcast of the advertisement. If we use Excel to analyze these data, we obtain the output in the table below. The Telemarketing Data and the Excel Output of a Two-Way ANOVA Position of Advertisement Time of Day On the Hour On the Half-Hour Early in Program Late in Program 10:00 morning 42 36 62 51 37 41 68 47 41 38 64 48 4:00 afternoon 62 57 88 67 60 60 85 60 58 55 81 66 9:00 evening 100 97 127 105 96 96 120 101 103 101 126 107 ANOVA: Two-Factor With Replication Summary Hour Half-Hour Early Late Total Morning Count 3 3 3 3 12 Sum 120 115 194 146 575 Average 40 38.33 64.67 48.67 47.92 Variance 7 6.33 9.33 4.33 123.72 Afternoon Count 3 3 3 3 12 Sum 180 172 254 193 799 Average 60 57.33 84.67 64.33 66.58 Variance 4 6.33 12.33 14.33 132.45 Evening Count 3 3 3 3 12 Sum 299 294 373 313 1279 Average 99.67 98 124.33 104.33 106.58 Variance 12.33 7 14.33 9.33 128.27 Total Count 9 9 9 9 Sum 599 581 821 652 Average 66.56 64.56 91 .22 72.44 Variance 697.53 701 .78 700.69 625.03 (b) Test the significance of time of day effects with a = .05. (c) Test the signicance of position of advertisement effects with a = .05. (I) Make pairwise comparisons of the morning, afternoon, and evening times by using Tukey simultaneous 95 percent condence intervals. (e) Make painrvise comparisons of the four ad positions by using Tukey simultaneous 95 percent condence intervals. (f) Which time of day and advertisement position maximizes consumer response? Compute a 95 percent (individual) confidence interval for the mean number of calls placed for this time of day/ad position combination