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1. (TCO 3) Using the following information regarding actual sales for Seafood City, calculate the regression (trend) line:
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Sales for Seafood City ($)
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|
Day
|
Week 1
|
Week 2
|
|
Monday
|
1,700
|
1,800
|
|
Tuesday
|
1,900
|
2,000
|
|
Wednesday
|
2,100
|
2,100
|
|
Thursday
|
2,300
|
2,200
|
|
Friday
|
4,200
|
4,300
|
|
Saturday
|
4,400
|
4,600
|
|
Sunday
|
2,100
|
2,200
|
(Points : 4)
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y = 2,092.31. + 81.98x
y = 2,092.31 + 121.98x
y = 1,892.31 + 81.98x
y = 1,892.31 + 81.98x
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Question 2.
2. (TCO 3) Using the following information regarding actual sales for Paradise Pools, project sales for May of Year 3 using simple linear regression:
|
Sales for Paradise Pools ($000s)
|
|
Month
|
First Year
|
Second Year
|
|
January
|
84
|
84
|
|
February
|
80
|
82
|
|
March
|
88
|
98
|
|
April
|
100
|
120
|
|
May
|
150
|
160
|
|
June
|
200
|
210
|
|
July
|
240
|
250
|
|
August
|
220
|
215
|
|
September
|
180
|
195
|
|
October
|
160
|
165
|
|
November
|
120
|
130
|
|
December
|
92
|
100
|
(Points : 4)
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168.79
174.52
178.35
180.26
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Question 3.
3. (TCO 3) Using the following information regarding actual sales for Paradise Pools, calculate the seasonal ratio for June of Year 3:
|
Sales for Paradise Pools ($000s)
|
|
Month
|
First Year
|
Second Year
|
|
January
|
84
|
84
|
|
February
|
80
|
82
|
|
March
|
88
|
98
|
|
April
|
100
|
120
|
|
May
|
150
|
160
|
|
June
|
200
|
210
|
|
July
|
240
|
250
|
|
August
|
220
|
215
|
|
September
|
180
|
195
|
|
October
|
160
|
165
|
|
November
|
120
|
130
|
|
December
|
92
|
100
|
(Points : 4)
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0.67
0.77
1.08
1.41
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Question 4.
4. (TCO 3) Using the following information regarding actual sales for Seafood City, calculate the seasonal forecast of sales for Friday of Week 3:
|
Sales for Seafood City ($)
|
|
Day
|
Week 1
|
Week 2
|
|
Monday
|
1,700
|
1,800
|
|
Tuesday
|
1,900
|
2,000
|
|
Wednesday
|
2,100
|
2,100
|
|
Thursday
|
2,300
|
2,200
|
|
Friday
|
4,200
|
4,300
|
|
Saturday
|
4,400
|
4,600
|
|
Sunday
|
2,100
|
2,200
|
(Points : 4)
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2,822.29
3,006.17
4,300.00
5,614.30
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Question 5.
5. (TCO 3) The regression statistic that measures the accuracy of regression predictions is the: (Points : 4)
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correlation coefficient.
coefficient of determination.
standard error of the estimate.
t-statistic.
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