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Unit 6: Assignment

In this Assignment, you will be assessed based on the following outcomes:

GB513-4: Evaluate real-world situations and present solutions using statistical methods.

PC-6.1: Incorporate data, inferences, and reasoning to solve problems.

This Assignment has two parts. Part 1 has questions about forecasting. You will submit your

answers using the Unit 6 Assignment template located in Course Documents for Part 1.

Part 2 requires you to analyze a case. For this, you will prepare a PowerPoint presentation to

present your findings. See below under “Part 2-Case Analysis” for more details.

Part 1 – Forecasting

Answer the following three questions using the template provided.

Question 1

A marketing manager is forecasting the sales of cars per week. Determine the error for each of the

following forecasts. Then, calculate MAD and MSE.

Period Value Forecast Error

1 202 — —

2 191 202

3 173 192

4 169 181

5 171 174

6 175 172

7 182 174

8 196 179

9 204 189

10 219 198

11 227 211

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Question 2

The U.S. Census Bureau publishes data on factory orders for all manufacturing, durable goods,

and nondurable goods industries. Shown below are factory orders in the United States over a 13 –

year period (\$ billion).

First, use the data to develop forecasts for years 6 through 13 using a 5-year moving average.

Then, use the data to develop forecasts for years 6 through 13 using a 5-year weighted moving

average. Weight the most recent year by 6, the previous year by 4, the year before that by 2,

and the other years by 1.

Answer the following questions:

a) What is the forecast for year 13 based on the 5-year moving average?

b) What is the forecast for year 13 based on the 5-year weighted moving average?

c) What is the MAD for the moving average forecast?

d) What is the MAD for the weighted moving average forecast?

e) Which forecasting model is better?

Year Factory

orders

1 2,512.70

2 2,739.20

3 2,874.90

4 2,934.10

5 2,865.70

6 2,978.50

7 3,092.40

8 3,052.60

9 3,145.20

10 3,114.10

11 3,257.40

12 3,654.00

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Question 3

The “Economic Report to the President of the United States” included data on the amounts

of manufacturers’ new and unfilled orders in millions of dollars. Shown here are the figures

for new orders over a 21-year period.

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Use the charting tool in Excel to develop a regression model to fit the trend effects for the data.

Use a linear model and then try a polynomial (order 2) model. Make sure the charts show the line

formula and the r-squared value. Include both charts in your report. Then, answer the following

question:

● How well does either model fit the data? Which model should be used for forecasting?

Explain using the relevant metrics.

Year Total Number

of New Orders

1 55,022

2 55,921

3 64,182

4 76,003

5 87,327

6 85,139

7 99,513

8 115,109

9 116,251

10 121,547

11 123,321

12 141,200

13 162,140

14 168,420

15 171,250

16 176,355

17 195,204

18 209,389

19 237,025

20 272,544

21 293,475

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Part 2 – Case Analysis

To answer Part 2, you will prepare a PowerPoint presentation to present your findings. Make sure

you also submit the Excel file to show your work for Part 2. You will receive a reduction in points

if you fail to include the Excel file showing your work for Part 2.

Place all calculations for each of the questions on a separate worksheet. Then, using the

results of your work from Excel, prepare PowerPoint slides to answer the questions in a

presentation format. All relevant content should be on the slides; do not use the notes

section or leave information in the Excel file. The executives reviewing the presentation should

not need to switch to another document to see the required information.

The data you need is provided to you in the Unit 6 Excel file in Course Documents. Make

sure to use that file. Do not type anything in manually or download anything from the

Internet.

You will be analyzing the “Colonial Broadcasting” case in the coursepack. Begin by reading

the description in the case. Then, answer the questions listed below, NOT the questions

listed in the case. Ignore everything in the case after the end of page 4.

The executives at CBC want to see how they are doing in ratings against the other networks and

how the ratings will continue to change in the upcoming months. They also want to know if hiring

stars makes a difference and the impact of fact-based programming compared to hiring stars.

Remember that your audience is the management of CBC. Therefore, make sure your

presentation is professional and provides sufficient explanation.

1. Answer the following questions:

a. What is the average rating for all CBC movies? How about ABN movies and

BBS movies?

b. Include a table that shows the average and the other descriptive statistics (using

the data analysis tool pack in Excel) for the ratings of the three networks (one

column for each network). Explain what you learn from each of the metrics in the

table.

c. Comment on which network is doing best.

2. Create a line graph of the monthly average ratings for CBC for the year. Note that there

are multiple ratings data for the months; you will need to calculate an average for each

month first, and then plot the averages. After you create the graph, fit a linear trend line,

displaying the formula and the r-squared. Explain to the executives if you can use this

time series data to forecast the ratings of upcoming months. How accurate can you expect

this forecast to be?

3. Should the CBC hire stars for their movies? To answer this question, run a hypothesis test

to see if there is a significant difference between the ratings of movies with stars versus

movies without stars. Use the data for CBC movies only. Use 95% confidence.

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a. What are the null and alternative hypotheses (state in full sentences)?

b. Run the test using Excel and include the output table. Use a t-test assuming

equal variances.

c. What is your recommendation to the executives? Justify your answer referring to

the relevant figures.

4. Run a multiple regression where the dependent variable is ratings and the independent

variables are star and fact. Use data from CBC only. CBC Management has several

questions:

a. Which has more impact on a movie’s rating: Being fact-based or having one star?

How much does each of these factors change the ratings?

b. How well does this regression analysis explain the ratings? Justify your

answers referring to the relevant figures.

c. Are either, both, or neither of the independent variables significantly related to

the ratings at 95% confidence? Justify your answers referring to the relevant

figures.

Directions for Submitting your Assignment:

Be sure to complete the Unit 6 Assignment template. Submit your Assignment to the Unit 6

Assignment Dropbox.

Unit 6 Assignment

Content

Points

Possible

Points

Earned

Part 1 – Forecasting

Question 1

5

Question 1

Provided the MSE.

5

Question 2a

Correct forecast for year 13 using a 5-year moving average.

5

Question 2b

Correct forecast for year 13 using a 5-year weighted moving

average.

5

Question 2c

Correct MAD for moving average forecast.

5

Question 2d

Correct MAD for weighted moving average forecast.

5

Question 2e

Recommended the better model with justification.

5

Question 3

Used Excel charting to fit a linear trendline, including the formula

and r-squared.

5

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Question 3

Used Excel charting to fit a polynomial trendline, including the

formula and r-squared.

5

Question 3

Recommended the better model with justification.

5

Part 2 – Case Analysis

Question 1

Correct average rating for all three networks. 10

Question 1

Correct table showing the average and other descriptive statistics

for the ratings of the three networks, using one column for each

network.

10

Question 1

Appropriate explanation and analysis of what is learned from each

of the metrics in the descriptive statistics table.

20

Question 2

Correct line graph using the calculated average monthly ratings of

CBC for the year, showing r-squared and the formula.

20

Question 2

Summary to executives regarding whether the linear forecast can

be used to project ratings, including an assessment of how

accurate the forecast can be expected to be.

20

Question 3

Correct null and alternative hypotheses stated in full sentences. 20

Question 3

Accurate hypothesis test results. 20

Question 3

Correct recommendation and justification for whether CBC should

hire stars.

20

Question 4

Appropriate explanation on what has more impact on a movie’s

rating: Whether the movie includes a star or whether it is fact-

based.

20

Question 4

Explanation of how well this regression analysis explains the

ratings.

20

Question 4

Accurate identification and justification of which variables are

significantly related to ratings.

20

PowerPoint is formatted appropriately and communicated clearly. 50

Total 300