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Description

Part 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 Assessment template here 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.

Using Data Analysis in Excel

Before any data analysis can be performed using Excel, the Data Analysis ToolPak must first be installed on your computer.

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

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

13

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.

Use the charting tool in Excel to develop a regression model to t 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 t 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

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 this Data Sheet. Make sure to use this file. Do not type anything in manually or download anything from the Internet.

You will be analyzing the “Colonial Broadcasting” case in your course pack. 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 ToolPak 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. Answer the following:

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.

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