學門類別
政大
哈佛
- General Management
- Marketing
- Entrepreneurship
- International Business
- Accounting
- Finance
- Operations Management
- Strategy
- Human Resource Management
- Social Enterprise
- Business Ethics
- Organizational Behavior
- Information Technology
- Negotiation
- Business & Government Relations
- Service Management
- Sales
- Economics
- Teaching & the Case Method
最新個案
- Leadership Imperatives in an AI World
- Vodafone Idea Merger - Unpacking IS Integration Strategies
- Predicting the Future Impacts of AI: McLuhan’s Tetrad Framework
- Snapchat’s Dilemma: Growth or Financial Sustainability
- V21 Landmarks Pvt. Ltd: Scaling Newer Heights in Real Estate Entrepreneurship
- Did I Just Cross the Line and Harass a Colleague?
- Winsol: An Opportunity For Solar Expansion
- Porsche Drive (B): Vehicle Subscription Strategy
- Porsche Drive (A) and (B): Student Spreadsheet
- TNT Assignment: Financial Ratio Code Cracker
-
Logistic Regression: Modeling with StatTools
Logistic regression is a modeling technique often used to predict a binary variable-a variable coded as 1 if an event of interest occurs (e.g., a borrower defaults on a loan) and coded as 0 otherwise. This note details how logistic regression applies the logistic function to generate a probability forecast for a binary event. It also includes an example of how to fit a logistic regression model to loan default data using StatTools (an Excel add-in). The StatTools output is then used to predict a loan's default as a function of the borrower's credit score.