Regression Analysis Fundamentals

📋 Course Outline

1. Unit goals and structure
2. Assessment tasks and weekly exercises
3. Regression concepts and notation
4. Simple linear regression example
5. Model fit and prediction intervals
6. Regression assumptions in simple models
7. Multiple regression workflow
8. Data screening and descriptives
9. Collinearity and predictor relations
10. Multiple regression diagnostics

📖 1. Unit goals and structure

🔑 Key Concepts & Definitions

• Generalized Linear Model GLM : A generalized linear framework for statistical analyses that underpins the unit’s approach.
• Unit instructors : The teaching team for PSYU3349, including the Lecturer & Convenor and Co-Convenor who oversee instruction and coordination.
• Unit schedule by weeks : A week-by-week progression that pairs readings with topics leading from regression basics toward broader regression-related methods.
• Live lectures and Echo360 : A delivery setup where lectures run in person, are live-streamed, and are later available as video recordings via Echo360.

📝 Essential Points