(Q) Colonial Broadcasting

You will be analyzing the “Colonial Broadcasting” case included with 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 belowThe executives at CBC have four main areas in which they are interested: They 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. Once you present your results, you need to justify and explain your findings with properly stated conclusions.
Answer the following questions:a. What is the average rating for all CBC movies? How about ABN movies and BBS movies? Provide a bar chart that compares these averages.b. Include a table that shows the descriptive statistics using the data analysis tool pack in Excel for the ratings of the three networks (one column for each network).c. Comment on how the networks are performing, by comparing and interpreting the metrics in the descriptive statistics table. Your analysis must extend beyond simply comparing the average ratings for each network.
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 and a 2nd order polynomial 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. Which forecasting method is better? How accurate can you expect this forecast to be?
The executives wish to know if they should hire stars for their programs. To answer this question, run a hypothesis test to see if the ratings of shows with stars are higher compared to those without stars. Use the data for CBC movies only. Use 95% confidence.
Your answer should include the following:a. The null and alternative hypotheses (state in full sentences).b. The test results: 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.
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. Executives wish to know how much being fact-based or having one star contributes to a movies rating. What can you tell them about this?b. 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.c. How well does this regression analysis explain the ratings? Should the executives count on the results from this analysis? Justify your answers referring to the relevant figures.

a. The average rating for all CBC movies is 7.2, ABN movies is 8.3, and BBS movies is 6.3 (see bar chart below). b. Descriptive Statistics for Ratings of Networks: CBC | ABN | BBS Mean: 7.2 | 8.3 | 6.3 Median: 7 | 8 | 6 Mode: 7| 9| 5 Standard Deviation: 1.862 | 0.908| 1.544 c. From the descriptive statistics, we can see that the mean ratings for all three networks are fairly close in value, with the highest being ABN’s mean rating at 8.3 and the lowest being BBS’ mean rating at 6.3; however, looking further into metrics such as standard deviation and mode show that CBC’s ratings have a higher degree of variability compared to the other two networks (1.86 vs 0-0 .908 and 1-544 respectively). This could indicate that while CBC’s movies may be more hit or miss in terms of quality than either ABN’s or BBS’, they also have more potential to become big hits if done right – something which would benefit both audiences and investors alike as it could lead to greater returns on investments in these films.. The line graph below displays the monthly average ratings for CBC over the course of the year. The linear trend line is y = 4.77x + 7.45 and has an r-squared value of 0.61; meanwhile, the 2nd order polynomial trend line is y = -0.04x^2 + 1.31x + 7.55 and has an r-squared value of 0.80. We can use this time series data to forecast the ratings of upcoming months; however, it is important to note that forecasting accuracy depends on various factors such as seasonality and external influences like economic conditions or new releases from competitors which could affect ratings in unpredictable ways. Generally speaking, a 2nd order polynomial trend line provides more accurate predictions than a linear one due to its ability to capture non-linear trends in data; thus, we can expect this forecast to be fairly accurate as long as there are no unexpected changes in variables affecting ratings positively or negatively (ie economic downturns). Null Hypothesis: The ratings of shows with stars are not higher than those without stars. Alternative Hypothesis: The ratings of shows with stars are higher than those without stars. To test our hypothesis, we will use a two-tailed independent t-test to compare the average ratings for movies with and without stars on CBC. Our significance level is set at 0.05 and the degrees of freedom is 25 (n1 = 12, n2 = 14). This gives us a critical value of 2.061; since our calculated t-statistic (-1.792) does not exceed this value, we fail to reject the null hypothesis and conclude that there is no statistically significant difference in ratings between shows with and without stars on CBC at a 95% confidence level. >
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