Mark Gershon, owner of a musical instrument distributorship, thinks that demand for guitars may be related to the number of television appearances by the popular group Maroon 5 during the previous month. Mark has collected the data shown in the following table:   Demand for Guitars 3 6 7 5 10 7 Maroon 5 TV Appearances 3 4 7 6 8 5     Graph these data to see whether a linear equation might describe the relationship between the group’s television shows and guitar sales. Use the least-squares regression method to derive a forecasting equation. Use the least-squares regression method to derive a forecasting equation. What is your estimate for guitar sales if Maroon 5 performed on TV nine times last month?   Weekly sales of copy paper at Cubicle Suppliers are provided in the table below. Compute a three-period moving average and a four-period moving average for weeks 5, 6, and 7. Compute the MAD for both forecasting methods. Which model is more accurate? Forecast week 8 with the more accurate method.   Week                 Sales (cases)     1                               17     2                               21     3                               27     4                               31     5                               19     6                               17     7                               21 Answer Week Sales (cases) 3MA |error|   4MA |error| 1 17           2 21           3 27           4 31           5 19           6 17           7 21           8                         Sales for refrigerators are given in the table below. Using a 3-month moving average, forecast sales for February the following year. Calculate the MAD for this model.         3-Month Absolute Month Sales Moving Average Deviation January 11     February 14     March 16     April 10     May 15     June 17     July 11     August 14     September 17     October 12     November 14     December 16     January 11     February         Use the weighted moving average to forecast  sales for February the following year .  Calculate the MAD for this model. Which model is better?     Month Sales 3-Month Weighted Moving Average Absolute Deviation January 11     February 14     March 16     April 10     May 15     June 17     July 11     August 14     September 17     October 12     November 14     December 16     January 11     February                       Lillian Sam, a New Orleans psychologist, specializes in treating patients who are agoraphobic (i.e., afraid to leave their homes). The following table indicates how many patients Dr. Sam has seen each year for the past 10 years. It also indicates what the robbery rate was in New Orleans during the same year:     Crime Patients       Year Rate X Y        1  58.3  36        2  61.1  33        3  73.4  40        4  75.7  41        5  81.1  40        6  89.0  55        7 101.1  60        8  94.8  54        9 103.3  58       10 116.2  61       Column totals   854.0     478                                               Using trend (linear regression) analysis, predict the number of patients Dr. Sam will see in years 11 and 12 as a function of time.     The following gives the number of pints of type B blood used at Woodlawn Hospital in the past 6 weeks: Week of Pints used August 31 360 September 7 389 September 14 410 September 21 381 September 28 368 October 5 374 October 12     Forecast the demand for the week of October 12 using     Sales of Volkswagen’s popular Beetle have grown steadily at auto dealerships in Nevada during the past 5 years (see table below). The sales manager had predicted before the new model was introduced that first year sales would be 410 VWs. Using exponential smoothing with a weight of α =.30, develop forecasts for years 2 through 6.         Forecast Exponential Absolute Year Sales Smoothing a = 0.6 Deviation 1 450 410.0   2 495     3 518     4 563     5 584     6                              Forecast Exponential Absolute Year Sales Smoothing a = 0.9 Deviation 1 450 410.0   2 495     3 518     4 563     5 584     6

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  1. Mark Gershon, owner of a musical instrument distributorship, thinks that demand for guitars may be related to the

number of television appearances by the popular group Maroon 5 during the previous month. Mark has collected the data shown in the following table:

 

Demand for Guitars

3

6

7

5

10

7

Maroon 5 TV Appearances

3

4

7

6

8

5

 

 

  1. Graph these data to see whether a linear equation might describe the relationship between the group’s television shows and guitar sales.
  2. Use the least-squares regression method to derive a forecasting equation.
  3. Use the least-squares regression method to derive a forecasting equation.
  4. What is your estimate for guitar sales if Maroon 5 performed on TV nine times last month?

 

Weekly sales of copy paper at Cubicle Suppliers are provided in the table below. Compute a three-period moving average and a four-period moving average for weeks 5, 6, and 7. Compute the MAD for both forecasting methods. Which model is more accurate? Forecast week 8 with the more accurate method.

 

Week                 Sales (cases)

    1                               17

    2                               21

    3                               27

    4                               31

    5                               19

    6                               17

    7                               21

Answer

Week

Sales (cases)

3MA

|error|

 

4MA

|error|

1

17

 

 

 

 

 

2

21

 

 

 

 

 

3

27

 

 

 

 

 

4

31

 

 

 

 

 

5

19

 

 

 

 

 

6

17

 

 

 

 

 

7

21

 

 

 

 

 

8

 

 

 

 

 

 

 

 

 

 

 

 

  1. Sales for refrigerators are given in the table below. Using a 3-month moving average, forecast sales for February the following year. Calculate the MAD for this model.

 

 

 

 

3-Month

Absolute

Month

Sales

Moving Average

Deviation

January

11

 

 

February

14

 

 

March

16

 

 

April

10

 

 

May

15

 

 

June

17

 

 

July

11

 

 

August

14

 

 

September

17

 

 

October

12

 

 

November

14

 

 

December

16

 

 

January

11

 

 

February

 

 

 

 

Use the weighted moving average to forecast  sales for February the following year .  Calculate the MAD for this model.

Which model is better?

 

 

Month

Sales

3-Month Weighted Moving Average

Absolute Deviation

January

11

 

 

February

14

 

 

March

16

 

 

April

10

 

 

May

15

 

 

June

17

 

 

July

11

 

 

August

14

 

 

September

17

 

 

October

12

 

 

November

14

 

 

December

16

 

 

January

11

 

 

February

 

 

 

 

 

 

 

 

 

 

 

  1. Lillian Sam, a New Orleans psychologist, specializes in treating patients who are agoraphobic (i.e., afraid to leave their homes). The following table indicates how many patients Dr. Sam has seen each year for the past 10 years. It also indicates what the robbery rate was in New Orleans during the same year:

 

 

Crime

Patients

 

 

 

Year

Rate X

Y

 

 

 

 1

 58.3

 36

 

 

 

 2

 61.1

 33

 

 

 

 3

 73.4

 40

 

 

 

 4

 75.7

 41

 

 

 

 5

 81.1

 40

 

 

 

 6

 89.0

 55

 

 

 

 7

101.1

 60

 

 

 

 8

 94.8

 54

 

 

 

 9

103.3

 58

 

 

 

10

116.2

 61

 

 

 

Column totals

 

854.0

 

 

478

 

 

 

 

 

 

 

 

                           

 

Using trend (linear regression) analysis, predict the number of patients Dr. Sam will see in years 11 and 12 as a function of time.

 

 

  1. The following gives the number of pints of type B blood used at Woodlawn Hospital in the past 6 weeks:

Week of

Pints used

August 31

360

September 7

389

September 14

410

September 21

381

September 28

368

October 5

374

October 12

 

 

Forecast the demand for the week of October 12 using

 

 

  1. Sales of Volkswagen’s popular Beetle have grown steadily at auto dealerships in Nevada during the past 5 years (see table below). The sales manager had predicted before the new model was introduced that first year sales would be 410 VWs. Using exponential smoothing with a weight of α =.30, develop forecasts for years 2 through 6.

 

 

 

 

Forecast Exponential

Absolute

Year

Sales

Smoothing a = 0.6

Deviation

1

450

410.0

 

2

495

 

 

3

518

 

 

4

563

 

 

5

584

 

 

6

 

 

 

 

 

 

 

  

 

 

 

 

 

 

Forecast Exponential

Absolute

Year

Sales

Smoothing a = 0.9

Deviation

1

450

410.0

 

2

495

 

 

3

518

 

 

4

563

 

 

5

584

 

 

6

 

 

 

 

 

 

 

 

 

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