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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 to part 1 using the Unit 6 Assignment template (see attached). You still need to submit the Excel file you used to generate your answers, in addition to the report in Word. Part 2, requires you to analyze a case, 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 1202 — — 2191 202 3173 192 4169 181 5171 174 6175 172 7182 174 8196 179 9204 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? Which forecasting model that you calculated is better? Why?What is the average rating for all CBC movies? How about ABN movies and BBS movies? 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. What are the null and alternative hypotheses (state in full sentences)? What is your recommendation to the executives? Justify your answer referring to the relevant figures. 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? How well does this regression analysis explain the ratings? Justify your answers referring to the relevant figures. 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. 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 Orders1 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 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 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: 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. 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: b.Run the test using Excel and include the output table. Use a t-test assuming equal variances. 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: Directions for Submitting your Assignment: Be sure to complete the Unit 6 Assignment template. Submit your assignment to the Unit 6 AssignmentDropbox. Unit 6 Assignment ContentPoints 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 Total300 ch_07.pptx u6_assignment_rubric.docx u6__assignment_part_2.xlsx u6_assignment_template.docx ch13.pptx Unformatted Attachment Preview Business Statistics Tenth Edition Ken Black Chapter 7 Sampling and Sampling Distributions Copyright ©2020 John Wiley & Sons, Inc. Learning Objectives 1. Contrast sampling to census and differentiate among different methods of sampling by assessing the advantages associated with each. 2. Describe the distribution of a sample’s mean using the central limit theorem, correcting for a finite population if necessary. 3. Describe the distribution of a sample’s proportion using the z formula for sample proportions. Copyright ©2020 John Wiley & Sons, Inc. 2 7.1 Sampling (1 of 16) Reasons for Sampling • The sample can save money • The sample can save time • For given resources, the sample can broaden the scope of the study • Because the research process is sometimes destructive, the sample can save product • If accessing the population is impossible, the sample is the only option Copyright ©2020 John Wiley & Sons, Inc. 3 7.1 Sampling (2 of 16) Reasons for Taking a Census • Eliminate the possibility that by chance a randomly selected sample may not be representative of the population • For the safety of the consumer • To benchmark data for future studies Frame • List, map, directory, or some other source used in the sampling process to represent the population • Also called the working population Copyright ©2020 John Wiley & Sons, Inc. 4 7.1 Sampling (3 of 16) Frame • Overregistered: contains units that are not in the target population • Underregistered: does not contain some units that are in the target population Copyright ©2020 John Wiley & Sons, Inc. 5 7.1 Sampling (4 of 16) Random Versus Nonrandom Sampling • Random sampling: every unit of the population has the same probability of being selected into the sample • Nonrandom sampling: not every unit of the population has the same probability of being selected into the sample o Generally NOT an appropriate technique for gathering data for statistical analysis Simple Random Sampling • Each unit in the frame is numbered from 1 to N (the size of the population) • A random number table or generator is used to select n items into the sample Copyright ©2020 John Wiley & Sons, Inc. 6 7.1 Sampling (5 of 16) Simple Random Sampling Example: From the population frame of companies in Table 7.3, select a simple random sample of six companies • First, the companies were numbered from 1 to 30 TABLE 7.3: Numbered population of 30 Companies 01 Alaska Airlines 11 DuPont 21 Lubrizol 02 Alcoa 12 ExxonMobil 22 Mattel 03 Ashland 13 General Dynamics 23 Merck 04 Bank of America 14 General Electric 24 Microsoft 05 Boeing 15 General Mills 25 Occidental Petroleum 06 Chevron 16 Halliburton 26 JCPenney 07 Citigroup 17 IBM 27 Procter & Gamble 08 Clorox 18 Kellogg 28 Ryder 09 Delta Air Lines 19 Kroger 29 Sears 10 Disney 20 Lowe’s 30 Time Warner Copyright ©2020 John Wiley & Sons, Inc. 7 7.1 Sampling (6 of 16) Example • From the table of random numbers (Table 7.1), two-digit numbers are selected, discarding any that are over 30 • In the table of random numbers, the first two digits are 91, which is unusable • The second two digits are 56, also unusable, as is 74, the next two digits • The fourth set of two digits is 25, which corresponds with Occidental Petroleum Copyright ©2020 John Wiley & Sons, Inc. 8 7.1 Sampling (7 of 16) TABLE 7.1: A Brief Table of Random Numbers 91567 42595 27958 30134 04024 86385 29880 99730 46503 18584 18845 49618 02304 51038 20655 58727 34914 63974 88720 82765 34476 17032 87589 40836 57491 16703 23167 49323 45021 33132 12544 41035 30405 83946 23792 14422 15059 45799 22716 19792 09983 74353 68668 30429 70735 25499 16631 35006 85900 07119 97336 71048 08178 77233 13916 47564 Copyright ©2020 John Wiley & Sons, Inc. 9 7.1 Sampling (8 of 16) Example • Continue moving across the rows until 6 two-digit numbers are selected • Sample will be: o (25) Occidental Petroleum o (27) Procter & Gamble o (01) Alaska Airlines o (04) Bank of America o (02) Alcoa o (29) Sears Copyright ©2020 John Wiley & Sons, Inc. 10 7.1 Sampling (9 of 16) Stratified Random Sampling • Population is divided into nonoverlapping subpopulations (strata) • Analyst selects a random sample from each o o o Can reduce sampling error, because sample will more closely match the population More costly than a simple random sample Strata are usually chosen based on available information about the population • Within each group, there should be homogeneity • Between each group, there should be heterogeneity Copyright ©2020 John Wiley & Sons, Inc. 11 7.1 Sampling (10 of 16) Stratified Random Sampling • Proportionate stratified random sampling occurs when the percentage of the sample taken from each stratum is proportionate to the percentage that each stratum is within the whole population o o Example: suppose voters are being surveyed in Boston and the sample is being stratified by religion as Catholic, Protestant, Jewish, and others If Boston’s population is 90\% Catholic and if a sample of 1,000 voters is taken, the sample would require inclusion of 900 Catholics to achieve proportionate stratification • Disproportionate stratified random sampling occurs when the percentage of each stratum in the sample is different from the percentage that each stratum is within the whole population Copyright ©2020 John Wiley & Sons, Inc. 12 7.1 Sampling (11 of 16) Systematic Sampling • Every kth item is selected to produce a sample of size n from a population of size N k= N n Example: A business analyst wanted to sample Texas manufacturers as part of a management study. • Wanted to sample 1,000 companies • Frame – most recent edition of the Texas Manufacturers Register® which listed 22,000 manufacturing companies in alphabetic order  22, 000  • The value of k is   = 22 1, 000   • Use random number table to choose the first element in the study Copyright ©2020 John Wiley & Sons, Inc. 13 7.1 Sampling (12 of 16) Cluster (or Area) Sampling • Dividing population into nonoverlapping areas • Clusters that are internally heterogeneous o Example: states, cities • If clusters are too large, a second set of clusters can be taken from the initial cluster (two-stage sampling) • Advantages: convenience, cost • Disadvantages: may be less efficient than simple random sampling if the elements of the cluster are similar Copyright ©2020 John Wiley & Sons, Inc. 14 7.1 Sampling (13 of 16) Nonrandom Sampling • Any method that does not involve a random selection process Convenience Sampling • Selected for the convenience of the analyst Judgment Sampling • Chosen by the judgement of the analyst o o Since the probability of an element being selected cannot be determined, cannot determine sampling error Can be biased due to systematic errors in judgment Copyright ©2020 John Wiley & Sons, Inc. 15 7.1 Sampling (14 of 16) Quota Sampling • Population subclasses, such as age or gender, are used as strata • Can be useful if no frame is available for the population • Can be less costly • Nonrandom, and thus probabilities cannot be calculated Copyright ©2020 John Wiley & Sons, Inc. 16 7.1 Sampling (15 of 16) TABLE 7.4: Using Quota Sampling to Fill Quotas of Consumers by Age Age Category Quota How Sample is Obtained 14–18 years old 70 Go to the nearest high school and survey willing students as they leave school until you have surveyed 70 students 25–39 years old 30 Go to junior soccer matches and survey parents in the stands until you have 30 surveys Over 65 years old 40 Go to the activity center of a retirement community and survey whomever will talk to you Snowball Sampling • Survey subjects are selected based on referral from other survey respondents. Copyright ©2020 John Wiley & Sons, Inc. 17 7.1 Sampling (16 of 16) Sampling Error • Occurs when the sample is not representative of the population Nonsampling Error • All other errors other than sampling error o o o o o o o Missing data Recording errors Measurement errors Input processing errors Analysis errors Response errors And many more! Copyright ©2020 John Wiley & Sons, Inc. 18 7.2 Sampling Distribution of x bar (1 of 12) Suppose that a small, finite population contains only N = 8 numbers: 54 55 59 63 64 68 69 70 • Distribution of the population data: • Suppose that all possible samples of size n = 2 are taken from this population Copyright ©2020 John Wiley & Sons, Inc. 19 7.2 Sampling Distribution of x bar (2 of 12) Population: 54 55 59 63 64 68 69 70 All possible samples of n = 2: • Then take the means of all of the samples (54, 54) (55, 54) (59, 54) (63, 54) (54, 55) (55, 55) (59, 55) (63, 55) (54, 59) (55, 59) (59, 59) (63, 59) (54, 63) (55, 63) (59, 63) (63, 63) (54, 64) (55, 64) (59, 64) (63, 64) (54, 68) (55, 68) (59, 68) (63, 68) (54, 69) (55, 69) (59, 69) (63, 69) (54, 70) (55, 70) (59, 70) (63, 70) (64, 54) (68, 54) (69, 54) (70, 54) (64, 55) (68, 55) (69, 55) (70, 55) (64, 59) (68, 59) (69, 59) (70, 59) (64, 63) (68, 63) (69, 63) (70, 63) (64, 64) (68, 64) (69, 64) (70, 64) (64, 68) (68, 68) (69, 68) (70, 68) (64, 69) (68, 69) (69, 69) (70, 69) (64, 70) (68, 70) (69, 70) (70, 70) Copyright ©2020 John Wiley & Sons, Inc. 20 7.2 Sampling Distribution of x bar (3 of 12) Means of the samples: 54 54.5 56.5 58.5 59 61 61.5 62 54.5 55 57 59 59.5 61.5 62 62.5 56.5 57 59 61 61.5 63.5 64 64.5 58.5 59 61 63 63.5 65.5 66 66.5 59 59.5 61.5 63.5 64 66 66.5 67 61 61.5 63.5 65.5 66 68 68.5 69 61.5 62 64 66 66.5 68.5 69 69.5 62 62.5 64.5 66.5 67 69 69.5 70 Copyright ©2020 John Wiley & Sons, Inc. 21 7.2 Sampling Distribution of x bar (4 of 12) Distribution of the means of the samples: Copyright ©2020 John Wiley & Sons, Inc. 22 7.2 Sampling Distribution of x bar (5 of 12) Distribution of the mean of the samples looks different from the original distribution: Copyright ©2020 John Wiley & Sons, Inc. 23 7.2 Sampling Distribution of x bar (6 of 12) Similarly, the histogram of a Poisson distribution and its samples are different: Copyright ©2020 John Wiley & Sons, Inc. 24 7.2 Sampling Distribution of x bar (7 of 12) The Central Limit Theorem • If random samples of size n are repeatedly drawn from a population that has a mean of μ and a standard deviation of σ, the sample means, x , are approximately normally distributed for sufficiently large sample sizes (n ≥ 30), regardless of the shape of the population distribution. If the population is normally distributed, the sample means are normally distributed for any size sample. • It can be shown that the mean of the sample means is the population mean: x =  • The standard deviation of the sample means (the standard error of the mean) is: x =  n Copyright ©2020 John Wiley & Sons, Inc. 25 7.2 Sampling Distribution of x bar (8 of 12) Figure 7.9 Shapes of the Distributions of Sample Means for Three Sample Sizes Drawn from Four Different Population Distributions Copyright ©2020 John Wiley & Sons, Inc. 26 7.2 Sampling Distribution of x bar (9 of 12) • If the sample size is greater than 30 or the population is normally distributed, the sample means are normally distributed, and z scores can be used to evaluate individual values x − z=  n Example: Suppose the population mean expenditure per customer at a tire store is $125 and the population standard deviation is $30. If a random sample of 40 customers is taken, what is the probability that the sample mean expenditure is more than $133? • Because the sample size is greater than 30, the central limit theorem can be used, and the sample means are normally distributed z= x −  n = 133 − 125 = 1.69 30 40 Copyright ©2020 John Wiley & Sons, Inc. 27 7.2 Sampling Distribution of x bar (10 of 12) Example, continued: • From the z distribution table, z = 1.69 gives a probability of .4545 • .5 −.4545 = .0455 • 4.55\% of the time, a random sample of 40 customers from this population would yield a sample mean expenditure of $133 or more when the population mean is $125 Copyright ©2020 John Wiley & Sons, Inc. 28 7.2 Sampling Distribution of x bar (11 of 12) Sampling from a Finite Population • In cases of a finite population, a statistical adjustment must be made to the z formula for sample means z= x −  n N −n N −1 • A rough rule of thumb is to use the finite population correction factor when n  .05 N • The correction factor reduces the size of the standard error of the mean, because when the sample is large relative to the population, the sample mean is less likely to vary from the population mean Copyright ©2020 John Wiley & Sons, Inc. 29 7.2 Sampling Distribution of x bar (12 of 12) TABLE 7.6: Finite Correction Factor for Some Sample Sizes Population Size 2000 Sample Size Value of Correction Factor 30(<5\% N) .993 2000 500 .866 500 30 .971 500 200 .775 200 30 .924 200 75 .793 Copyright ©2020 John Wiley & Sons, Inc. 30 7.3 Sampling Distribution of p cap (1 of 4) The sample proportion is computed by dividing the frequency with which a given characteristic occurs in the sample by the number of items in the sample x pˆ = n where x = the number of items in a sample that have the characteristic n = the number of items in the sample The central limit theorem applies to sample proportions in that the normal distribution approximates the shape of the distribution of sample proportions if n  p  5 and n  q  5 (p is the population proportion and q = 1 − p) • The mean of sample proportions is p (the population proportion) Copyright ©2020 John Wiley & Sons, Inc. 31 7.3 Sampling Distribution of p cap (2 of 4) • The standard error of the proportion is pq n • The z formula is z= pˆ − p pq n Where p = population proportion q = 1− p n = sample size pˆ = sample proportion Copyright ©2020 John Wiley & Sons, Inc. 32 7.3 Sampling Distribution of p cap (3 of 4) Example: Suppose 60\% of the electrical contractors in a region use a particular brand of wire. What is the probability of taking a random sample of size 120 from these electrical contractors and finding that .50 or less use that brand of wire? z= .50 − .60 = −2.24 .60  .40 120 • The z distribution table gives a value of .4875 • .5 − .4875 = .0125 Copyright ©2020 John Wiley & Sons, Inc. 33 7.3 Sampling Distribution of p cap (4 of 4) There is only a 1.25\% chance of finding that 50\% or less of a sample of 120 contractors use a given brand of wire if the population proportion is .60 Copyright ©2020 John Wiley & Sons, Inc. 34 Copyright Copyright © 2020 John Wiley & Sons, Inc. All rights reserved. Reproduction or translation of this work beyond that permitted in Section 117 of the 1976 United States Act without the express written permission of the copyright owner is unlawful. Request for further information should be addressed to the Permissions Department, John Wiley & Sons, Inc. The purchaser may make back-up copies for his/her own use only and not for distribution or resale. The Publisher assumes no responsibility for errors, omissions, or damages, caused by the use of these programs or from the use of the information contained herein. Copyright ©2020 John Wiley & Sons, Inc. 35 GB513: Business Analytics 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, covered in unit 5. You will submit your answers to part 1 using the Unit 6 Assignment template located in Course Documents. You still need to submit the Excel file you used to generate your answers, in addition to the report in Word. Part 2 covers content from units 1-4 and 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 3 4 5 6 7 8 9 191 173 169 171 175 182 196 204 202 192 181 174 172 174 179 189 — 1 of 7 10 11 219 198 227 211 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) b) c) d) e) What is the forecast for year 13 based on the 5-year moving average? What is the forecast for year 13 based on the 5-year weighted moving average? What is the MAD for the moving average forecast? What is the MAD for the weighted moving average forecast? 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 2 of 7 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 3 of 7 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 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 course pack that you bought at the beginning of the course. Begin by reading the description i ... Purchase answer to see full attachment