A telemarketing firm has studied the effects of two factors on the response to i
ID: 3289880 • Letter: A
Question
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 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 table below.
Perform graphical analysis to check for interaction between time of day and position of advertisement. Then test for interaction with = .05.
(b) Test the significance of time of day effects with = .05.
F = 691.19, p-value = less than .05; (Click to select)rejectdo not reject H0: time of day is important
(c) Test the significance of position of advertisement effects with = .05.
F = 76.21, p-value = less than .001; (Click to select)rejectdo not reject H0: position of the ads is important
(d) Make pairwise comparisons of the morning, afternoon, and evening times by using Tukey simultaneous 95 percent confidence intervals. (Round your answers to 2 decimal places. Negative amounts should be indicated by a minus sign.)
(e) Make pairwise comparisons of the four ad positions by using Tukey simultaneous 95 percent confidence intervals. (Round your answers to 2 decimal places. Negative amounts should be indicated by a minus sign.)
(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. (Round your answers to 2 decimal places.)
Confidence interval = [ , ]
FINAL answers only
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 43 34 61 53 35 42 65 46 41 40 66 47 4:00 afternoon 60 56 86 66 60 64 81 59 57 54 79 64 9:00 evening 104 95 127 101 94 96 124 106 106 105 128 106 150 M orning 100 Afternoon -Evening 50 Hour Half-Hour Earty LateExplanation / Answer
in calculation done in MINITAB get table as below
Perform graphical analysis to check for interaction between time of day and position of advertisement. Then test for interaction with = .05.
the graph of each line not intersecting that clearly implies that there is no interaction effect also we can test this by the p-value for interaction from table is 0.7868 > 0.05 that mean we have to accept that there is no inetraction effect.
(b) Test the significance of time of day effects with = .05.
F = 691.19, p-value = 0.00<0.05 that implies that there is significant effect of time of day in analysis.
(c) Test the significance of position of advertisement effects with = .05.
F = 76.21, p-value = 0.000 < 0.05 that implies that there is significant effect of position of advertisement in analysis.
(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.
ANOVA Source of Variation SS df MS F P-Value F crit Sample 22,732.39 2 11,366.19 691.19 .0000 3.403 Columns 3,759.86 3 1,253.29 76.21 .0000 3.009 Interaction 51.39 6 8.56 .52 .7868 2.508 Error 394.67 24 16.444 Total 26,938.31 35 (a)Perform graphical analysis to check for interaction between time of day and position of advertisement. Then test for interaction with = .05.
the graph of each line not intersecting that clearly implies that there is no interaction effect also we can test this by the p-value for interaction from table is 0.7868 > 0.05 that mean we have to accept that there is no inetraction effect.
(b) Test the significance of time of day effects with = .05.
F = 691.19, p-value = 0.00<0.05 that implies that there is significant effect of time of day in analysis.
(c) Test the significance of position of advertisement effects with = .05.
F = 76.21, p-value = 0.000 < 0.05 that implies that there is significant effect of position of advertisement in analysis.
(d) Make pairwise comparisons of the morning, afternoon, and evening times by using Tukey simultaneous 95 percent confidence intervals.
ESTIMATES diff lwr CI upr CI B-A 17.75000 13.86919 21.63081 C-A 59.91667 56.03585 63.79748 C-B 42.16667 38.28585 46.04748Related Questions
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