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最新個案
- A practical guide to SEC ï¬nancial reporting and disclosures for successful regulatory crowdfunding
- Quality shareholders versus transient investors: The alarming case of product recalls
- The Health Equity Accelerator at Boston Medical Center
- Monosha Biotech: Growth Challenges of a Social Enterprise Brand
- Assessing the Value of Unifying and De-duplicating Customer Data, Spreadsheet Supplement
- Building an AI First Snack Company: A Hands-on Generative AI Exercise, Data Supplement
- Building an AI First Snack Company: A Hands-on Generative AI Exercise
- Board Director Dilemmas: The Tradeoffs of Board Selection
- Barbie: Reviving a Cultural Icon at Mattel (Abridged)
- Happiness Capital: A Hundred-Year-Old Family Business's Quest to Create Happiness
Segmenting Clinton and Obama Voters
內容大綱
The purpose of this case is to introduce data visualization, advanced regression techniques, and supervised learning. Students are asked to visualize data geographically and in scatterplots. They will use stepwise regression and regression trees to select a predictive model for forecasting data in a holdout sample. In a forecasting competition, they will submit their models to be tested for accuracy. Supervised learning techniques-such as training, validation, and testing-are introduced. Regression trees serve as both predictive and graphical tools for communicating insights from data analysis to a decision maker.