Week 4 Homework

MAT540 Homework Week 4

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MAT540

Week 4 Homework

Chapter 15

1. The manager of the Carpet City outlet needs to make an accurate forecast of the demand for Soft

Shag carpet (its biggest seller). If the manager does not order enough carpet from the carpet mill,

customer will buy their carpet from one of Carpet City’s many competitors. The manager has

collected the following demand data for the past 8 months:

Month Demand for Soft Shag

Carpet (1,000 yd.)

1 10

2 9

3 8

4 9

5 10

6 12

7 14

8 11

 

a. Compute a 3-month moving average forecast for months 4 through 9.

b. Compute a weighted 3-month moving average forecast for months 4 through 9. Assign

weights of 0.55, 0.35, and 0.10 to the months in sequence, starting with the most recent

month.

c. Compare the two forecasts by using MAD. Which forecast appears to be more accurate?

 

2. The manager of the Petroco Service Station wants to forecast the demand for unleaded gasoline

next month so that the proper number of gallons can be ordered from the distributor. The owner has

accumulated the following data on demand for unleaded gasoline from sales during the past 10

months:

 

 

 

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Month Gasoline Demanded (gal.)

October 775

November 835

December 605

January 450

February 600

March 700

April 820

May 925

June

July

1500

1200

 

a. Compute an exponential smoothed forecast, using an α value of 0.4

b. Compute the MAD.

3. Emily Andrews has invested in a science and technology mutual fund. Now she is considering

liquidating and investing in another fund. She would like to forecast the price of the science and

technology fund for the next month before making a decision. She has collected the following data

on the average price of the fund during the past 20 months:

Month Fund Price

1 $55 ¾

2 54 ¼

3 55 1/8

4 58 1/8

5 53 3/8

6 51 1/8

7 56 ¼

8 59 5/8

9 62 ¼

10 59 ¼

11 62 3/8

12 57 1/1

 

 

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13 58 1/8

14 62 ¾

15 64 ¾

16 66 1/8

17 68 ¾

18 60.5

19 65.875

20 72.25

 

a. Using a 3-month average, forecast the fund price for month 21.

b. Using a 3-month weighted average with the most recent month weighted 0.5, the next most

recent month weighted 0.30, and the third month weighted 0.20, forecast the fund price for

month 21.

c. Compute an exponentially smoothed forecast, using α=0.3, and forecast the fund price for

month 21.

d. Compare the forecasts in (a), (b), and (c), using MAD, and indicate the most accurate.

4. Carpet City wants to develop a means to forecast its carpet sales. The store manager believes that

the store’s sales are directly related to the number of new housing starts in town. The manager has

gathered data from county records on monthly house construction permits and from store records

on monthly sales. These data are as follows:

Monthly Carpet Sales

(1,000 yd.)

Monthly Construction

Permits

9 17

14 25

10 8

12 7

15 14

9 7

24 45

21 19

20 28

 

 

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29 28

 

a. Develop a linear regression model for these data and forecast carpet sales if 30 construction

permits for new homes are filed.

b. Determine the strength of the causal relationship between monthly sales and new home

construction by using correlation.

5. The manager of Gilley’s Ice Cream Parlor needs an accurate forecast of the demand for ice cream.

The store orders ice cream from a distributor a week ahead; if the store orders too little, it loses

business, and if it orders too much, the extra must be thrown away. The manager belives that a

major determinant of ice cream sales is temperature (i.e.,the hotter the weather, the more ice cream

people buy). Using an almanac, the manager has determined the average day time temperature for

14 weeks, selected at random, and from store records he has determined the ice cream consumption

for the same 14 weeks. These data are summarized as follows:

Week Average Temperature

(Degrees)

Ice Cream Sold

(gal.)

1 68 80

2 70 115

3 73 91

4 79 87

5 77 110

6 82 128

7 85 164

8 90 178

9 85 144

10 92 179

11 90 144

12 95 197

13 80 144

14 75 123

 

 

 

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a. Develop a linear regression model for these data and forecast the ice cream consumption if the

average weekly daytime temperature is expected to be 85 degrees.

b. Determine the strength of the linear relationship between temperature and ice cream

consumption by using correlation.

c. What is the coefficient of determination? Explain its meaning.