# Linear Regression

Forecasting

Your boss comes in “with their hair on fire”, and says, “just came  from a staff meeting and the CEO announced the building next door is  coming available at a really good price per square foot.”  He needs you  to tell him if the forecasted demand for the next 36 months is high  enough that we may need some of the new space.  He follows up with, “If  we miss this opportunity, and later we figure out that we need more  space then the only option is to rent space in another building about 5  miles away at almost double the price per square foot”.

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As the operations manager, the last thing you want to do is to split  your staff across two different buildings.

That is going to be a  staffing and scheduling headache, not to mention morale will take a  hit. Do you shoot from the hip and tell your boss, “no doubt, forecasted  demand indicates we can use the space”, or do you say, “let me run the  forecast and get back to you later today”?  Which response has the most  risk?  Are you willing to bet your job on your answer?

Forecasting is both an art and a science.  In the assignments for  Unit 2 you will get the opportunity to practice forecasting by solving  end of chapter problems and completing a linear trending assignment. In  order to determine if it makes sense to rent additional space, it will  be important to assess the level of operational efficiency of existing  processes associated with the delivery of products to consumers.  Improving process efficiencies could result in a reduction in labor  requirements. Maybe the company can support greater demand with a leaner  labor force. In this unit, in addition to applying forecasting models  to scenarios, you will also gain a more in-depth understanding of the  concept and practices used to create efficient organizations.

## Unit Learning Outcomes

1. Develop a plan for forecasting impacts to an organization’s bottom line. (CLO 1, 2, 4, and 7)
2. Demonstrate how to perform forecasting using data and statistics.  (CLO 4 and 5)
3. Identify trends and patterns in data as they apply to forecasting. (CLO 1, 3, 5, and 7)
4. Develop a data collection plan that will permit the creation of an accurate and reliable forecasting model. (CLO 3, 4, and 5)

## Directions

1. End of Chapter Problems (40 points):  Answer the following end of chapter problems from the textbook:

Chapter 3 – problems 2, 3, 4 and 8 (pages 125-126, 10 points each).

View the following example videos before working the problems:

C3 Example 03-03

Play media comment.

(mp4, 37MB)

C3 Example 03-04

Play media comment.              (mp4, 60MB)

C2 Example 03-08

Play media comment.              (mp4, 82MB)

Video files are also available in

Files

2. Linear Regression Forecasting Assignment: Watch the video and  complete the assignment below (20 points). You must use the data  analysis tool pak method  in Excel (see second part of video) to perform  the linear regression, and include the regression table in your  spreadsheet.  Simply adding a trend line to the data graph does not meet  this requirement.

Unit 2 – Individual Assignment – Linear Regression

, follow the directions, and submit when complete. Document is also located in Files on the Student View folder.

MBA576 Unit 2 Individual Assignment

You just finished your MBA degree and your boss wants “to show you off” in an upcoming staff meeting because she feels you have a lot of potential. Needless to say, you feel some pressure.

She says the company’s machines have required “high number of repairs” over the past year and feels the repair service staff needs to be “beefed up”, but she is not sure how many new staff are needed. The repair department was stood up one year ago, and she tasks you to come up with staffing estimates to make sure there are enough repair staff to meet the demand. Below is the data she gives you for the past 16 months, and then she asks, “How many repair staff positions would you forecast we need in the next 6 months?”

Year 1 – # of repair calls received. The repair staff is open from 7am to 7pm, seven days a week, and repair staff work the entire 12-hour shift. You can assume a single repair takes an average of 30 minutes for each repair.

Month

Average # of repair calls received per day for each month the repair office was open in year 1

Jan

25

Feb

19

Mar

28

Apr

32

May

42

Jun

47

Jul

50

Aug

49

Sep

52

Oct

54

Nov

53

Dec

59

Jan

49

Feb

40

Mar

42

Apr

47

a. Using Excel, create a linear regression equation to model the number of repairs per month.

b. How many repair calls does the regression equation predict for June of year 2?

c. Given your prediction for June of year 2, how many repair staff members do you forecast will be needed per 12-hour shift to handle the load?

Submit your findings in a Word document and include the Excel spread sheet.

PROBLEMS

Question 2. National Scan, Inc., sells radio frequency inventory tags. Monthly sales for a seven-month period were as follows:

Month Sales

(000 units)

Feb. 19

Mar. 18

Apr. 15

May 20

Jun. 18

Jul. 22

Aug. 20

Plot the monthly data on a sheet of graph paper.

Forecast September sales volume using each of the following:

(1) The naive approach

(2) A five-month moving average

(3) A weighted average using .60 for August, .30 for July, and .10 for June

(4) Exponential smoothing with a smoothing constant equal to .20, assuming a a March ­forecast of 19(000)

(5) A linear trend equation

Which method seems least appropriate? Why? (Hint: Refer to your plot from part a.)

What does use of the term sales rather than demand presume?

Question 3. A dry cleaner uses exponential smoothing to forecast equipment usage at its main plant. August usage was forecasted to be 88 percent of capacity; actual usage was 89.6 percent of capacity. A smoothing constant of .1 is used.

Prepare a forecast for September.

Assuming actual September usage of 92 percent, prepare a forecast for October usage.

Question 4. An electrical contractor’s records during the last five weeks indicate the number of job requests:

Week 1 2 3 4 5

Requests 20 22 18 21 22

Predict the number of requests for week 6 using each of these methods:

Naive

A four-period moving average

Exponential smoothing with α = .30; use 20 for week 2 forecast

Question 8. Air travel on Mountain Airlines for the past 18 weeks was:

Week Passengers

1 405

2 410

3 420

4 415

5 412

6 420

7 424

8 433

9 438

10 440

11 446

12 451

13 455

14 464

15 466

16 474

17 476

18 482

Explain why an averaging technique would not be appropriate for forecasting.

Use an appropriate technique to develop a forecast for the expected number of passengers for the next three weeks.

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