Course Introduction
This course provides comprehensive training in regression analysis techniques for real-world data. You'll learn to build, interpret, and validate regression models while understanding their assumptions and limitations.
Course Content
- Week 1: Introduction to Regression Concepts
- Week 2: Simple Linear Regression
- Week 3: Multiple Regression
- Week 4: Model Diagnostics and Validation
- Week 5: Logistic Regression
- Week 6: Nonlinear Regression
- Week 7: Time Series Regression
- Week 8: Final Project and Case Studies
Assignments
Assignment 1: Housing Price Prediction
Build a regression model to predict housing prices based on various features.
Download DatasetAssignment 2: Housing Price Prediction
Build a regression model to predict housing prices based on various features.
Download DatasetAssignment 3: Customer Churn Analysis
Use logistic regression to predict customer churn probability.
Download DatasetCase Studies
Medical Research: Drug Effectiveness
Analyze clinical trial data to determine drug effectiveness across demographics.
Economic Analysis: GDP Predictors
Identify key economic indicators that best predict GDP growth.
Economic Analysis: GDP Predictors
Identify key economic indicators that best predict GDP growth.
Datasets
Recommended Textbooks
- "Applied Linear Regression" by Sanford Weisberg
- "Regression Modeling Strategies" by Frank E. Harrell
- "An Introduction to Statistical Learning" by James et al.