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 On the Hour On the Half-Hour Early in Program Late in Program 10:00 morning 43 34 63 51 37 42 69 46 41 37 66 49 4:00 afternoon 62 56 86 68 61 61 84 60 60 53 80 66 9:00 evening 99 98 130 102 97 95 118 104 102 103 125 110 ANOVA: Two-Factor With Replication Summary Hour Half-Hour Early Late Total Morning Count 3 3 3 3 12 Sum 121 113 198 146 578 Average 40.33 37.67 66.00 48.67 48.17 Variance 9.33 16.33 9.00 6.33 141.06 Afternoon Count 3 3 3 3 12 Sum 183 170 250 194 797 Average 61.00 56.67 83.33 64.67 66.42 Variance 1.00 16.33 9.33 17.33 120.81 Evening Count 3 3 3 3 12 Sum 298 296 373 316 1,283 Average 99.33 98.67 124.33 105.33 106.92 Variance 6.33 16.33 36.33 17.33 131.54 Total Count 9 9 9 9 Sum 602 579 821 656 Average 66.89 64.33 91.22 72.89 Variance 676.36 743.00 686.69 650.36 ANOVA Source of Variation SS df MS F P-Value F crit Sample 21,699.50 2 10,849.75 807.01 .0000 3.403 Columns 3,975.67 3 1,325.22 98.57 .0000 3.009 Interaction 29.17 6 4.86 .36 .8959 2.508 Error 322.67 24 13.444 Total 26,027.00 35 (b) Test the significance of time of day effects with = .05. (c) Test the significance of position of advertisement effects with = .05. (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.) 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 63 51 37 42 69 46 41 37 66 49 4:00 afternoon 62 56 86 68 61 61 84 60 60 53 80 66 9:00 evening 99 98 130 102 97 95 118 104 102 103 125 110.

1. 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
On the Hour
On the Half-Hour
Early in Program
Late in Program
10:00 morning
43
34
63
51
37
42
69
46
41
37
66
49
4:00 afternoon
62
56

2. 86
68
61
61
84
60
60
53
80
66
9:00 evening
99
98
130
102
97
95
118
104
102
103
125
110
ANOVA: Two-Factor With Replication
Summary
Hour
Half-Hour
Early
Late
Total
Morning

3. Count
3
3
3
3
12
Sum
121
113
198
146
578
Average
40.33
37.67
66.00
48.67
48.17
Variance
9.33
16.33
9.00
6.33
141.06
Afternoon
Count

4. 3
3
3
3
12
Sum
183
170
250
194
797
Average
61.00
56.67
83.33
64.67
66.42
Variance
1.00
16.33
9.33
17.33
120.81
Evening
Count
3
3
3
3
12
Sum

5. 298
296
373
316
1,283
Average
99.33
98.67
124.33
105.33
106.92
Variance
6.33
16.33
36.33
17.33
131.54
Total
Count
9
9
9
9
Sum
602
579
821
656
Average

6. 66.89
64.33
91.22
72.89
Variance
676.36
743.00
686.69
650.36
ANOVA
Source of Variation
SS
df
MS
F
P-Value
F crit
Sample
21,699.50
2
10,849.75
807.01
.0000
3.403
Columns
3,975.67
3
1,325.22

7. 98.57
.0000
3.009
Interaction
29.17
6
4.86
.36
.8959
2.508
Error
322.67
24
13.444
Total
26,027.00
35
(b) Test the significance of time of day effects with = .05.
(c) Test the significance of position of advertisement effects with = .05.
(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

8. day/ad position combination. (Round your answers to 2 decimal places.)
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
63
51
37
42
69
46
41
37
66
49
4:00 afternoon
62
56

9. 86
68
61
61
84
60
60
53
80
66
9:00 evening
99
98
130
102
97
95
118
104
102
103
125
110