answers week 13 FIN FIN 600|QUESTIONS WITH ANSWERS

The Football Bowl Subdivision (FBS) level of the National Collegiate Athletic Association (NCAA) consists of over 100 schools. Most of these schools belong to one of several conferences, or collections of schools, that compete with each other on a regular basis in collegiate sports. Suppose the NCAA has commissioned a study that will propose the formation of conferences based on the similarities of the constituent schools. The file FBS contains data on schools belong to the Football Bowl Subdivision (FBS). Each row in this file contains information on a school. The variables include football stadium capacity, latitude, longitude, athletic department revenue, endowment, and undergraduate enrollment. Refer to the Appendix for instructions on how to perform a given hierarchical clustering method using the Analytic Solver Platform. (Hint: This can be done using a PivotTable in Excel to display the count of schools in each cluster as well as the minimum and maximum of the latitude and longitude within each cluster.) Wrong "Check My Work" Clicked: 1 Time  Check My Work eBook Attracted by the possible returns from a portfolio of movies, hedge funds have invested in the movie industry by financially backing individual films and/or studios. The hedge fund Star Ventures is currently conducting some research involving movies involving Adam Sandler, an American actor, screenwriter, and film producer. As a first step, Star Ventures would like to cluster Adam Sandler movies based on their gross box office returns and movie critic ratings. Using the data in the file Sandler, apply k-means clustering with k = 3 to characterize three different types of Adam Sandler movies. Base the clusters on the variables Rating and Box Office. Rating corresponds to movie ratings provided by critics (a higher score represents a movie receiving better reviews). Box Office represents the gross box office earnings in 2015 dollars. Use the resulting clusters to characterize Adam Sandler movies. Refer to the Appendix for instructions on how to perform k-means clustering method using the Analytic Solver Platform. In the k-means Clustering – Step 2 of 3 dialog box, be sure to use Normalize input data. Set the # Clusters: to 3 and the # Iterations: to 10. Choose 10 Random starts: and Set seed: to 12345. Click on the datafile logo to reference the data. Report the characteristics of each cluster using a PivotTable that includes a count of movies, the average rating of movies and the average box office earnings of movies in each cluster. How would you characterize the movies in each cluster? Round your answers to the nearest tenth. Averages Cluster Count Rating Box Office (M$) (in 2015) Characteristics 1 2 3 5 25 500 High-rated movies 2 25 500 Low-rated and low-earning movies 5 25 500 High-earning movies Hide Feedback Incorrect Post Submission Feedback Cluster 1 is characterized by low-rated movies that generated little revenue at the box office. This cluster represents half of the movies in the data. Cluster 2 represents moderate-rated movies with large box office revenue and contains 15 out of the 48 observations. Cluster 3 contains highly-rated movies that still generate relatively low box office revenue. Solution Correct Response eBook Attracted by the possible returns from a portfolio of movies, hedge funds have invested in the movie industry by financially backing individual films and/or studios. The hedge fund Star Ventures is currently conducting some research involving movies involving Adam Sandler, an American actor, screenwriter, and film producer. As a first step, Star Ventures would like to cluster Adam Sandler movies based on their gross box office returns and movie critic ratings. Using the data in the file Sandler, apply k-means clustering with k = 3 to characterize three different types of Adam Sandler movies. Base the clusters on the variables Rating and Box Office. Rating corresponds to movie ratings provided by critics (a higher score represents a movie receiving better reviews). Box Office represents the gross box office earnings in 2015 dollars. Use the resulting clusters to characterize Adam Sandler movies. Refer to the Appendix for instructions on how to perform k-means clustering method using the Analytic Solver Platform. In the k-means Clustering – Step 2 of 3 dialog box, be sure to use Normalize input data. Set the # Clusters: to 3 and the # Iterations: to 10. Choose 10 Random starts: and Set seed: to 12345. Click on the datafile logo to reference the data. Report the characteristics of each cluster using a PivotTable that includes a count of movies, the average rating of movies and the average box office earnings of movies in each cluster. How would you characterize the movies in each cluster? Round your answers to the nearest tenth. Averages Cluster Count Rating Box Office (M$) (in 2015) Characteristics 1 24 17.1 36.8 Low-rated and low-earning movies 2 3  Check My Work 15 29.2 162.3 High-earning movies 9 63.0 50.4 High-rated movies CengageNOW | Assignments | View Take Details Question: cameba03h/Problem 0