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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. 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. 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. 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

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