Reference no: EM133813626

Assignment: Building Decision Analysis Models on Excel

You will be required to APPLY a minimum of three of the four concepts learned (forecasting, decision analysis, regression, linear programming) in an integrated fashion to SOLVE a real-world opportunity (topic is provided below). Dissect the issue or opportunity you are exploring, and bring transformative changes through the lens of concepts you have learned in this course.

I need data that can give me a high correlation or R2 of over 0.80. I prefer around 0.85 R2.

I am looking for someone to build me all three models based on the topic provided above. This is a master level course, so Quantitative Analysis for Business and data science knowledge is required.

I have provided an example of an excel sheet, but that is NOT related to my topic. It should just be used to understand what I am looking for.

For Forecasting, use the following techniques: Exponential Smoothing, Double Moving Average, Holt's Method, and Holt's Winter Method.

For Regression, make sure you have a variety of dependent variables (ie, age, gender, opening weekend revenue, audience ratings, critic ratings on rotten tomatos, etc.)

Elements:

A. Originality of work. (Do not simply download models from the internet; you will need to find data and design and build your own models

B. Exemplifies master's-level work and mastery of concepts.

C. Interpretation and application of key concepts (e.g., MAD, MAPE, R2, coefficientvalues).

D. Ability to communicate data successfully (data storytelling).

E. Good mixture of descriptive analytics and predictive analytics. Use Tableau to understand key patterns in your business. Use predictive analytics to bring insights and recommendations.

F. Interpretation and application of key concepts in the course, in simple-to-understand terms for business setting (MAD, MAPE, R2, 95% prediction interval, sensitivity analysis).

G. Appropriate modeling techniques applied.

Topic

Movie Box Office Revenue Prediction: Use regression analysis to predict box office revenues based on various factors such as genre, budget, cast popularity, release timing, and competitor releases. You could apply forecasting to predict future trends in movie revenue and use decision analysis to evaluate investment decisions in new projects.

Helpful Resources/Websites that may have relevant data

A. Box Office Mojo (IMDb Pro): This is one of the most comprehensive sources for box office data, including domestic and international revenues, opening weekend numbers, and weekly box office performance.

B. The Numbers: Provides detailed movie financial analysis, including production budgets, domestic and international box office gross, and DVD sales.

C. Rotten Tomatoes: Offers critic and audience reviews, ratings, and potentially useful metadata on movies that can be correlated with box office performance.

D. IMDb (Internet Movie Database): Besides box office data, IMDb provides extensive information on movies, including cast and crew details, user ratings, genres, and more, which can be valuable for predictive modeling.

E. Social Media Platforms (Twitter, Facebook, Instagram): Social media buzz and sentiment analysis can be useful predictors of a movie's box office success. You might need to use APIs or web scraping tools to gather data on mentions, likes, shares, and general sentiment towards a film pre and post-release.

F. Google Trends: This tool allows you to see the interest over time for a particular movie or genre, which can be a good indicator of its popularity and potential box office performance.

G. Trade Publications and News Sites (Variety, The Hollywood Reporter, Deadline): These sites often provide insights into the movie industry, including deals, budget information, marketing strategies, and more nuanced data that might not be available on aggregate data sites.