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(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.

(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.

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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(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 daylad position combination.
Transcribed Image Text:(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 daylad position combination.
Exercise 11.22 METHODS AND APPLICATIONS A telemarketing firm has studied the effects of two factors on the response to its television advertisements. The first 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 10:00 morning On the Hour 42 On the Half-Hour 36 Early in Program 62 Late in Program 51 37 41 68 47 41 62 38 64 88 48 67 4:00 afternoon 57 60 58 100 96 103 60 85 60 66 105 101 107 81 127 55 9:00 evening 97 120 126 96 101 ANOVA: Two-Factor With Replication Hour Summary Half-Hour Early Late Total Morning Count 3 12 194 64.67 9.33 Sum 120 40 115 38.33 6.33 146 48.67 4.33 575 Average Variance 47.92 123.72 7 Afternoon Count 3 3 3 12 Sum 172 180 60 254 193 64.33 799 66.58 Average Variance 84.67 12.33 57.33 4 6.33 14.33 132.45 Evening Count Sum 3 373 3 313 12 1279 299 294 Average Variance 99.67 98 124.33 104.33 106.58 12.33 7 14.33 9.33 128.27 Total Count 9. 9. 9. 9. 821 599 66.56 581 64.56 652 72.44 625.03 Sum Average Variance 91.22 697.53 701.78 700.69 (b) Test the significance of time of day effects with a = .05. (c) Test the significance of position of advertisement effects with a = .05. d) Make pairwise comparisons of the morning, afternoon, and evening times by using Tukey simultaneous 95 percent confidence intervals. (e) Make pairwise comparisons of the four ad positions by using Tukey simultaneous 95 percent confidence intervals.
Transcribed Image Text:Exercise 11.22 METHODS AND APPLICATIONS A telemarketing firm has studied the effects of two factors on the response to its television advertisements. The first 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 10:00 morning On the Hour 42 On the Half-Hour 36 Early in Program 62 Late in Program 51 37 41 68 47 41 62 38 64 88 48 67 4:00 afternoon 57 60 58 100 96 103 60 85 60 66 105 101 107 81 127 55 9:00 evening 97 120 126 96 101 ANOVA: Two-Factor With Replication Hour Summary Half-Hour Early Late Total Morning Count 3 12 194 64.67 9.33 Sum 120 40 115 38.33 6.33 146 48.67 4.33 575 Average Variance 47.92 123.72 7 Afternoon Count 3 3 3 12 Sum 172 180 60 254 193 64.33 799 66.58 Average Variance 84.67 12.33 57.33 4 6.33 14.33 132.45 Evening Count Sum 3 373 3 313 12 1279 299 294 Average Variance 99.67 98 124.33 104.33 106.58 12.33 7 14.33 9.33 128.27 Total Count 9. 9. 9. 9. 821 599 66.56 581 64.56 652 72.44 625.03 Sum Average Variance 91.22 697.53 701.78 700.69 (b) Test the significance of time of day effects with a = .05. (c) Test the significance of position of advertisement effects with a = .05. d) Make pairwise comparisons of the morning, afternoon, and evening times by using Tukey simultaneous 95 percent confidence intervals. (e) Make pairwise comparisons of the four ad positions by using Tukey simultaneous 95 percent confidence intervals.
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