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Harvard Business School 9-894-011 November 22, 1993 Professor George Wu prepared this case as the basis for class discussion rather than to illustrate either effective or ineffective handling of an administrative situation. Copyright © 1993 by the President and Fellows of Harvard College. To order copies or request permission to reproduce materials, call 1-800-545-7685, write Harvard Business School Publishing, Boston, MA 02163, or go to http://www.hbsp.harvard.edu. No part of this publication may be reproduced, stored in a retrieval system, used in a spreadsheet, or transmitted in any form or by any means—electronic, mechanical, photocopying, recording, or otherwise—without the permission of Harvard Business School. 1 Colonial Broadcasting Company Part A Barbara Warrington, Vice-President of Programming at Colonial Broadcasting Company (CBC), sat in her office preparing for a meeting with Bruce Gold, an independent movie producer. Warrington thought that Gold would probably try to sell to CBC a movie idea based on the true story of a young boy who overcomes a debilitating illness. In recent years, the number of TV movies based on real-life events (fact-based movies) had been steadily growing. The networks seemed to believe that these movies brought higher ratings than fictional movies. But Warrington wasn't so sure. She suspected that other factors could be responsible for the high ratings of fact-based movies. Warrington had data on TV movies that were broadcast during 1992. She decided to have one of her assistants run some regressions to find out what was really driving ratings. Colonial Broadcasting Company Along with American Broadcasting Network (ABN) and Bellmore Broadcasting Service (BBS), Colonial Broadcasting Company is one of three major American television networks.1 Every week, each network broadcasts hundreds of hours of national programming, including news, sports, talk shows, as well as prime-time programming (theatrical and made-for-television movies;2 action, comedy, and drama series; news specials, etc.).3 Broadcasting TV Movies TV movies were first broadcast in the mid-1960s, and in the following decades came to play a major part in network programming. By 1992, the three major networks—ABN, BBS, and CBC— 1. A fourth network, Derby Television Network (DTN), has some highly-rated national shows and is an important competitor to the three majors. However, since DTN does not have daily national prime time programming, it is not considered in this case. 2. Theatrical movies are originally released in theatres and are shown on television several years after theatrical release. In contrast, made-for-television or TV movies are made explicitly for television. 3. Prime time runs from 8–11 p.m. on the East and West Coasts and from 7–10 p.m. in the Central States. The TV season runs from September to May. Thus, the three networks jointly broadcast approximately 2,300 hours of prime-time programming annually. For the exclusive use of S. Thomas, 2020. This document is authorized for use only by Shawnar Thomas in GB513: Business Analytics_Regular_8_23_2020 taught by CHRIS OSADCZUK, Purdue University Global from Feb 2020 to Aug 2020. 894-011 Colonial Broadcasting Company 2 were underwriting the production costs for more than two hundred hours of TV movies annually, approximately 10% of network prime time programming. The typical TV movie is made by an independent producer, with the cost underwritten by one of the networks. It is approximately 92 minutes long, but with commercials, it runs for two hours. TV movies are broadcast during prime time, usually beginning at 9 p.m. on the East and West Coasts and at 8 p.m. in the Central States. The networks' aim in broadcasting TV movies is to attract a large and demographically desirable audience. Networks are funded by advertisers who pay for on-air advertising time, and advertisers pay higher prices for programs which attract larger audiences. A network's success in attracting a large audience is reflected in its Nielsen ratings.4 These ratings, expressed as a percentage of all American households with televisions, measure how many televisions are turned on to particular programs. In 1992, each rating point represented 921,000 American households. Therefore, if a movie received a 25 rating during a given half-hour time slot, about 23 million households were tuned in to that movie at that time. The TV movie with the highest rating on record is The Day After (a movie about the aftermath of a nuclear holocaust) which garnered a rating of 46 when broadcast in 1983. The broadcast of the 1992 Super Bowl, by comparison, received a Nielsen rating of 40. Making a TV Movie The networks do not produce their own TV movies but instead contract with independent producers to have them made. Producers must first sell a movie concept to a network. The concepts the producer has to choose from fall into two basic categories: those drawn from fact and those drawn from fiction. A typical fact-based concept for a TV movie might be drawn from national or regional newspapers or a nonfiction book. On the other hand, a fictional movie might be based on a novel, play, screenplay, or simply the producer's brainstorming. If necessary, the producer must arrange for an option on the story rights. If the movie concept is based on a true-life occurrence, the option takes the form of an agreement between the producer and the rights holder, usually the people involved in the real-life events. In contrast, for novels, plays, and screenplays, the producer merely needs to get an option on the appropriate copyright. In most cases, a fee is paid for an exclusive option. The option then gives the producer the right to buy a piece of material within a specified period of time (e.g., the producer might pay $10,000 for a one-year option to buy the rights to a story for an additional $100,000). Once the producer has the story-rights, he can approach the networks and pitch his concept for the movie. The networks have several basic criteria for judging potential TV movies. Unlike television series, for which audience loyalty can be built over the course of a season or even many seasons, TV movies are usually a one-shot deal. Ideally, network executives believe, TV movies should be "’believable’ and sensational at the same time ... Characters should be simple and simply motivated, heroes familiar, stories full of conflict, endings resolved, uplift apparent, and each act should end on a note of suspense sufficient to carry the viewer through the commercial break."5 4. A. C. Nielsen Company is a major research firm which provides data on ratings and market-share for prime- time programming based on information gathered from sample households throughout the continental United States. 5. Todd Gitlin, Inside Prime Time (New York: Pantheon Books, 1983): 161, 165. For the exclusive use of S. Thomas, 2020. This document is authorized for use only by Shawnar Thomas in GB513: Business Analytics_Regular_8_23_2020 taught by CHRIS OSADCZUK, Purdue University Global from Feb 2020 to Aug 2020. Colonial Broadcasting Company 894-011 3 If a movie concept generates enough interest from the network, the network will pay a script-writer to develop the idea into a full-length script. If the script is acceptable, then the network will commit to produce the movie. Typically, the network and production company agree on a licensing fee (usually $2.60 to $2.75 million which covers the bulk of the production costs) for the network's exclusive North American right to broadcast the movie twice in four years. The licensing fee covers the bulk of the producer's production costs.6 Warrington's Decision Warrington knew that CBC's programming decisions were motivated primarily by ratings. She made a mental list of factors that might affect a TV movies' ratings: the day of the week or month it was broadcast, the broadcasting network, whether the movie had a big-name star, whether it was scheduled against tough competition, or whether the program immediately before it on the same network had high or low ratings. Was the movie concept—fact-based or fictional—one of the factors that drove ratings? Warrington looked down at the regressions which her assistant had brought in (see Exhibits 1 and 2). What did they tell her? 6. The licensing fee paid by the network usually falls about $400,000 short of actual production costs. After the network broadcasts the movie, the rights revert to the production company. The production company covers the short-fall with national and international fees for theater and television syndication and video cassette release. For the exclusive use of S. Thomas, 2020. This document is authorized for use only by Shawnar Thomas in GB513: Business Analytics_Regular_8_23_2020 taught by CHRIS OSADCZUK, Purdue University Global from Feb 2020 to Aug 2020. 894-011 Colonial Broadcasting Company 4 Exhibit 1 Data on 1992 TV Movies* Variable Description NETWORK Broadcasting network (ABN, BBS, or CBC) MONTH 1 = January, 2 = February, ..., 12 = December DAY 1 = Monday, 2 = Tuesday, ..., 7 = Sunday RATING Nielsen rating for movie FACT 1 = based on true events, 0 = fictional STARS Number of actors or actresses paid over $300,000 PREVIOUS RATING Nielsen rating for program immediately preceding movie on same network COMPETITION Average of Nielsen ratings received by the two competing networks during the movie's broadcast In addition, several dummy variables derived from the variables listed here are used in the regressions which follow. Examples are ABN 1 if NETWORK = ABN, BBS 1 if NETWORK = BBS, OCT 1 if MONTH = 10, DEC 1 if MONTH = 12, APR-MAY 1 if MONTH = 4 or MONTH = 5, MON 1 if DAY = 1, SUN 1 if DAY = 7. * All 1992 TV movies, not including sequels to old television series, movies that are part of a series, two-hour pilots for television series, or two-hour segments of a television mini-series. For the exclusive use of S. Thomas, 2020. This document is authorized for use only by Shawnar Thomas in GB513: Business Analytics_Regular_8_23_2020 taught by CHRIS OSADCZUK, Purdue University Global from Feb 2020 to Aug 2020. Colonial Broadcasting Company 894-011 5 Exhibit 1 Data on 1992 TV Movies (continued) Observation Network Month Day Rating Fact Stars Previous Ratings Competition 1 BBS 1 1 15.6 0 1 14.2 14.5 2 BBS 1 7 10.8 1 0 15.3 17.2 3 BBS 1 7 14.1 0 1 13.8 14.4 4 BBS 1 1 16.8 1 1 12.8 15.3 5 BBS 2 1 14.3 1 1 12.4 13.3 6 BBS 2 1 17.1 1 1 12.9 15.1 7 BBS 3 1 8.9 0 0 10.8 14.9 8 BBS 3 7 16.2 1 0 13.3 11.6 9 BBS 4 7 9.4 0 1 12.3 12.8 10 BBS 5 1 10.2 0 1 10.7 15.6 11 BBS 5 7 9.4 0 0 10.7 14.5 12 BBS 5 1 12.1 0 1 10.1 15.6 13 BBS 5 1 10.7 1 0 8.6 17.0 14 BBS 9 7 15.0 1 0 9.8 8.2 15 BBS 9 7 10.2 0 0 11.7 13.5 16 BBS 9 7 10.3 0 1 10.1 15.2 17 BBS 10 7 10.8 0 1 10.9 13.1 18 BBS 10 7 14.4 1 0 15.9 12.6 19 BBS 11 7 14.4 1 1 12.1 14.2 20 BBS 11 7 13.6 1 0 11.4 11.9 21 ABN 1 7 14.6 0 0 19.3 14.4 22 ABN 1 2 10.8 0 1 16.3 15.2 23 ABN 1 7 16.2 0 0 20.1 14.4 24 ABN 1 2 12.8 0 0 14.8 13.1 25 ABN 1 7 16.0 0 1 19.3 13.5 26 ABN 2 7 18.9 0 1 17.8 13.0 27 ABN 2 2 14.0 1 1 14.3 13.8 28 ABN 3 7 19.5 1 1 16.2 11.8 29 ABN 3 2 14.7 1 0 13.8 15.7 30 ABN 3 7 16.3 0 1 18.0 11.4 31 ABN 3 7 15.8 1 0 17.7 13.3 32 ABN 3 7 17.1 0 1 17.1 11.3 33 ABN 3 2 11.5 0 0 13.8 13.1 34 ABN 3 7 16.0 1 0 15.3 11.8 35 ABN 3 2 11.7 0 1 16.6 14.3 36 ABN 4 2 14.2 0 0 13.6 11.4 37 ABN 4 7 11.2 0 0 14.3 14.4 38 ABN 4 2 10.9 0 0 12.4 13.0 39 ABN 4 7 13.3 0 1 13.1 10.1 40 ABN 4 7 15.5 1 0 17.0 12.4 41 ABN 4 2 16.6 1 0 13.6 11.8 42 ABN 5 7 16.3 1 0 16.5 12.8 43 ABN 5 7 15.8 0 1 15.7 11.3 44 ABN 5 2 13.3 1 0 10.7 12.8 For the exclusive use of S. Thomas, 2020. This document is authorized for use only by Shawnar Thomas in GB513: Business Analytics_Regular_8_23_2020 taught by CHRIS OSADCZUK, Purdue University Global from Feb 2020 to Aug 2020. 894-011 Colonial Broadcasting Company 6 Exhibit 1 Data on 1992 TV Movies (continued) Observation Network Month Day Rating Fact Stars Previous Ratings Competition 45 ABN 9 7 15.4 0 1 17.3 10.9 46 ABN 9 2 14.7 0 0 15.5 13.9 47 ABN 9 7 15.5 0 0 17.4 12.6 48 ABN 9 2 14.7 1 0 15.3 14.0 49 ABN 10 7 15.9 1 0 18.4 10.5 50 ABN 10 7 13.8 1 0 24.7 12.1 51 ABN 10 2 10.0 0 1 14.2 12.9 52 ABN 11 7 12.9 0 1 16.9 18.6 53 ABN 11 2 15.4 1 0 15.9 12.4 54 ABN 11 7 14.5 0 2 19.4 14.2 55 ABN 12 7 18.8 0 2 16.7 14.7 56 ABN 12 2 16.7 0 0 14.9 10.1 57 ABN 12 2 12.8 0 0 16.3 12.0 58 ABN 12 7 16.8 0 1 15.7 10.1 59 CBC 1 7 14.0 0 1 8.2 14.8 60 CBC 1 1 11.3 1 0 13.0 13.2 61 CBC 1 1 13.6 0 0 13.7 15.1 62 CBC 2 7 12.9 1 0 8.8 16.0 63 CBC 2 1 13.2 1 0 13.1 17.0 64 CBC 2 7 16.0 1 0 6.9 15.8 65 CBC 2 1 14.6 1 1 13.8 17.4 66 CBC 2 7 16.6 0 1 16.8 14.4 67 CBC 3 1 17.5 1 0 14.8 14.2 68 CBC 3 7 11.6 0 0 10.0 14.0 69 CBC 4 7 8.9 0 0 8.6 13.0 70 CBC 4 1 15.6 0 0 13.3 16.8 71 CBC 4 7 9.2 0 1 6.8 12.1 72 CBC 4 1 11.8 0 0 12.9 12.0 73 CBC 4 7 11.0 0 0 5.3 14.7 74 CBC 4 1 9.5 1 0 13.0 17.3 75 CBC 9 7 11.6 0 0 10.1 12.8 76 CBC 9 1 13.3 1 0 13.1 20.3 77 CBC 9 1 13.6 1 0 14.1 18.3 78 CBC 10 1 12.4 0 0 13.6 20.2 79 CBC 10 1 13.8 1 0 10.2 16.6 80 CBC 10 7 11.9 1 0 11.8 12.2 81 CBC 10 1 14.6 0 0 14.9 14.9 82 CBC 11 1 15.8 1 1 13.4 17.2 83 CBC 11 1 15.4 0 1 13.6 16.8 84 CBC 11 1 12.8 0 0 12.7 14.6 85 CBC 12 7 12.8 0 0 12.0 18.6 86 CBC 12 1 15.1 0 0 14.1 15.5 87 CBC 12 1 11.4 0 1 11.2 16.4 88 CBC 12 1 19.1 1 0 12.6 15.4 For the exclusive use of S. Thomas, 2020. This document is authorized for use only by Shawnar Thomas in GB513: Business Analytics_Regular_8_23_2020 taught by CHRIS OSADCZUK, Purdue University Global from Feb 2020 to Aug 2020. Colonial Broadcasting Company 894-011 7 Exhibit 2 Regressions on 1992 TV Movies For the exclusive use of S. Thomas, 2020. This document is authorized for use only by Shawnar Thomas in GB513: Business Analytics_Regular_8_23_2020 taught by CHRIS OSADCZUK, Purdue University Global from Feb 2020 to Aug 2020. 894-011 Colonial Broadcasting Company 8 Questions For Questions 1a and 1b only, consider Regression 1. 1a. Rank the networks in terms of average ratings for TV movies during 1992. 1b. On average, how much higher are the ratings for the leading network than the ratings for the second-highest network? 2a. In 1992, what were the average ratings for fact-based movies? 2b. In 1992, what were the average ratings for fictional movies? 3. Consider Regression 2. Is the difference between the ratings for fact-based and fictional movies statistically significant? Explain. 4. Compare Regression 2 and Regression 3. Do the regressions suggest that, on average, a. a fact-based movie has fewer stars than a fictional movie; b. a fact-based movie has more stars than a fictional movie; c. a fact-based movie has just as many stars as a fictional movie; d. cannot be determined. Choose one and explain. For the next two questions, consider Regression 5. 5. On Sunday nights, CBC usually presents "Josette and Yvette" at 8:00 p.m., followed by the Sunday night movie at 9:00 p.m. Typical ratings for "Josette and Yvette" are 17.5. This week, Warrington is considering replacing "Josette and Yvette" with a live rock concert that is expected to garner a rating of 20 points. What is the expected change in ratings for the Sunday night movie? 6a. Warrington fears that a movie with high expected ratings might provoke the other networks to schedule better programming against CBC. Suppose that in response to CBC's programming, both ABN and BBS schedule different programs, each of which is expected to rate 2 rating points higher. What is the expected impact on the ratings of CBC's TV movie? 6b. Oskar Morgenstern, a CBC network executive, believes that network programming does not affect the size of the total television audience in a given time slot. Instead, he believes that a network's programming only determines the network's percentage share of the total audience. Does Regression 5 support Morgenstern's position? Explain. 7. Warrington believes that movies with stars tend to be shown in favorable time slots (e.g., good months, good days of the week, and following highly rated programs). a. Are the regressions consistent with her beliefs? Explain. b. Warrington is planning to add a fictional movie to the programming schedule. She must decide whether or not to use a star. What is the difference in expected ratings between using a star and not using a star? 8. The conventional industry wisdom is that fact-based movies have higher ratings than movies based on fictional stories. Do the regressions support or contradict this view? For the exclusive use of S. Thomas, 2020. This document is authorized for use only by Shawnar Thomas in GB513: Business Analytics_Regular_8_23_2020 taught by CHRIS OSADCZUK, Purdue University Global from Feb 2020 to Aug 2020. Colonial Broadcasting Company 894-011 9 Colonial Broadcasting Company Part B Warrington could barely believe her ears: just minutes before, she had gotten a call from Harsanyi Electric, a leading international consumer electronics company. Harsanyi was willing to sponsor a CBC TV movie, paying CBC a fee of $7,500,000 for the 28 minutes of advertising. The only catch was that Harsanyi Electric wanted CBC to guarantee ratings of 19 points. For every point that CBC fell short of this target, Harsanyi would get a rebate of $1,000,000. If the rating exceeded 19, however, no additional fee would be paid. Fractional ratings would be pro-rated: for example, if the rating was 18.4, then Harsanyi would get a rebate of $600,000. Warrington was unsure of whether the risk was worth it: $7,500,000 was a lot of money, but 19 points was an aggressive target. If Warrington rejected Harsanyi's offer, then she could always get a fee of $5,000,000 with no risk. For the questions below, assume it is impossible to predict the ratings for the competitor's programs. 9. Warrington wants to put the TV movie in the best possible slot so as to help ensure high ratings. She has 3 slots available: APRIL, SUNDAY (following a show that typically receives a rating of 8.5) MARCH, MONDAY (following a show that typically receives a rating of 13.0) DECEMBER, SUNDAY (following a show that typically receives a rating of 8.5) If Warrington wants to maximize the chance of high ratings, when should she schedule the TV movie? 10. Warrington is unsure of which TV movie to schedule. Due to the limited budget for a TV movie, CBC can choose either a fictional movie with a star or a fact-based movie without a star. Both movies are identical in all other respects. Assuming she wishes to maximize ratings, which movie should Warrington choose? For the next two questions, assume that a normal distribution with mean m and standard deviation s, can be approximated with the following discrete 5-point distribution: Probability Value .20 m - 1.3s .20 m - 0.5s .20 m .20 m + 0.5s .20 m + 1.3s . Thus, each point gets the same probability, .20. 11. Suppose that Warrington has scheduled a fact-based movie without a star for a Monday time slot in March (again, following a show that typically receives ratings of 13.0). Should Warrington accept Harsanyi Electric's offer or accept the fixed fee of $5,000,000? 12. Suppose that, prior to accepting or rejecting Harsanyi Electric's offer, Warrington could purchase a regression that would tell with virtual certainty what the Nielsen rating of the proposed movie would be. What is the most that Warrington would be willing to pay for such a regression? For the exclusive use of S. Thomas, 2020. This document is authorized for use only by Shawnar Thomas in GB513: Business Analytics_Regular_8_23_2020 taught by CHRIS OSADCZUK, Purdue University Global from Feb 2020 to Aug 2020.

GB513: Business Analytics GB513: Business Analytics You will be assessed based on the following outcomes: Evaluate real-world situations and present solutions using statistical methods. Incorporate data, inferences, and reasoning to solve problems. This has two parts. Part 1 has questions about forecasting. You will submit your answers to part 1 using the template attached. You still need to submit the Excel file you used to generate your answers, in addition to the report in Word. Part 2 covers requires you to analyze a case. For this, you will prepare a PowerPoint presentation to present your findings. See further instructions below under “Part 2-Case Analysis” for more details. Part 1 – Forecasting Answer the following three questions using the template provided. Question 1 A store managers wishes to forecast the weekly number of television sets sold. Calculate the error for each of the following forecasts, the MAD and the MSE. Be sure to show the entire table in the work area of the template. 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 data below shows the number of goods manufactured in one year. ($ billion). Calculate forecasts for years 6 through 13 using a 5-year moving average. Then, calculate 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. Be sure to show the entire table in the work area of the template. 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 that you calculated is better? Why? 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 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 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 100 point reduction 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 part 2 Excel file. 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 course pack that you bought at the beginning of the course. 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 document 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 how the networks are performing, using the metrics in the table. Your analysis should extend beyond simply comparing the average ratings for each network. 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 dependent variable contributes more when determining 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. Be sure to complete the template. Content Points Possible Points Earned Part 1 - Forecasting Question 1 Provided the MAD. 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 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 Correct figures and explanation of how much contribution each independent variable makes when determining a movie’s rating: 20 Question 4 Correct figures and explanation of how well this regression analysis explains the ratings. 20 Question 4 Correct figures, accurate identification and justification of which variables are significantly related to ratings. 20 PowerPoint is formatted appropriately and communicated clearly. 50 Total 300 of of 7 of 7

Sheet1 Observation Network Month Day Rating Fact Stars 1 BBS 1 1 15.6 0 1 2 BBS 1 7 10.8 1 0 3 BBS 1 7 14.1 0 1 4 BBS 1 1 16.8 1 1 5 BBS 2 1 14.3 1 1 6 BBS 2 1 17.1 1 1 7 BBS 3 1 15.8 0 0 8 BBS 3 7 16.2 1 0 9 BBS 4 7 12.6 0 1 10 BBS 5 1 13.5 0 1 11 BBS 5 7 15.6 0 0 12 BBS 5 1 12.1 0 1 13 BBS 5 1 15.8 1 0 14 BBS 9 7 15 1 0 15 BBS 9 7 16.3 0 0 16 BBS 9 7 13.3 0 1 17 BBS 10 7 10.8 0 1 18 BBS 10 7 14.4 1 0 19 BBS 11 7 14.4 1 1 20 BBS 11 7 13.6 1 0 21 ABN 1 7 14.6 0 0 22 ABN 1 2 10.8 0 1 23 ABN 1 7 16.2 0 0 24 ABN 1 2 12.8 0 0 25 ABN 1 7 16 0 1 26 ABN 2 7 18.9 0 1 27 ABN 2 2 17.6 1 1 28 ABN 3 7 19.5 1 1 29 ABN 3 2 16.9 1 0 30 ABN 3 7 16.3 0 1 31 ABN 3 7 15.8 1 0 32 ABN 3 7 17.1 0 1 33 ABN 3 2 15.8 0 0 34 ABN 3 7 16 1 0 35 ABN 3 2 11.7 0 1 36 ABN 4 2 14.2 0 0 37 ABN 4 7 18.1 0 0 38 ABN 4 2 15.2 0 0 39 ABN 4 7 13.3 0 1 40 ABN 4 7 15.5 1 0 41 ABN 4 2 16.6 1 0 42 ABN 5 7 16.3 1 0 43 ABN 5 7 16.5 0 1 44 ABN 5 2 16.8 1 0 45 ABN 9 7 15.4 0 1 46 ABN 9 2 14.7 0 0 47 ABN 9 7 15.5 0 0 48 ABN 9 2 14.7 1 0 49 ABN 10 7 15.9 1 0 50 ABN 10 7 13.8 1 0 51 ABN 10 2 14.9 0 1 52 ABN 11 7 12.9 0 1 53 ABN 11 2 15.4 1 0 54 ABN 11 7 14.5 0 2 55 ABN 12 7 12.6 0 2 56 ABN 12 2 11.8 0 0 57 ABN 12 2 12.8 0 0 58 ABN 12 7 16.8 0 1 59 CBC 1 7 14.7 0 1 60 CBC 1 1 11.3 1 0 61 CBC 2 1 13.5 0 1 62 CBC 2 7 12.9 1 0 63 CBC 3 1 13.2 1 0 64 CBC 3 7 12.8 1 0 65 CBC 4 1 13.2 1 1 66 CBC 4 7 13.5 0 1 67 CBC 5 1 17.5 1 0 68 CBC 5 7 11.6 0 0 69 CBC 5 7 12.1 0 0 70 CBC 6 1 15.6 0 0 71 CBC 6 7 12.6 0 1 72 CBC 6 1 11.8 0 0 73 CBC 7 7 12.3 0 0 74 CBC 7 1 14.2 1 1 75 CBC 8 7 11.6 0 0 76 CBC 8 1 13.3 1 0 77 CBC 8 1 13.6 1 0 78 CBC 9 1 12.4 0 1 79 CBC 9 1 13.8 1 0 80 CBC 9 7 11.9 1 0 81 CBC 10 1 14.6 0 0 82 CBC 10 1 15.8 1 1 83 CBC 10 1 15.4 0 1 84 CBC 11 1 13.1 0 1 85 CBC 11 7 12.8 0 0 86 CBC 12 1 15.1 0 0 87 CBC 12 1 12.4 0 1 88 CBC 12 1 19.1 1 0

This template is only for the first part of the Assignment. See specific instructions in the Assignment for part 2. In the summary tables below, insert only the answers. You will show work after the summary section. Question 1 MAD MSE Question 2 a) Moving average forecast for year 13 b) Weighted moving average forecast for year 13 c) MAD for part a d) MAD for part b e) Recommended forecast method (justify): Question 3 R-squared for Linear model R-squared for polynomial model Regression formula for linear model Regression formula for polynomial model Recommended forecast method (justify): Work Show all your work for the questions below. Make sure to also attach your Excel file to avoid losing points. Question 1 Show the errors you calculated. Question 2 Show the two forecasts and the errors Copy and paste the entire forecast table from Excel. Question 3 Show the polynomial and linear trendline charts from Excel charting. Do not use multiple regression analysis for this question or you will lose points.

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