This applied statistics course is a successor of STAT 34300 Applied Linear Statistical Methods and covers the foundations of
generalized linear models (GLM). We aim to cover the following topics:
- GLM model estimation, computation and inference
- Specific GLM models for binary, multinomial and count data
- Linear and generalized linear mixed effect models
- Survival analysis
This course will make a balance between practical real data analysis with examples and a deeper understanding of the models with mathematical derivations.
- Foundations of Linear and Generalized Linear Models by Alan Agresti (required)
- Extending the Linear Model with R (second edition) by Julian J. Faraway (suggested)
- Homework assignments: 25%
- There will be 5 assignments in total.
- Late homework will not be accepted for grading (medical emergencies excepted with proof).
- Homework will be submitted through Canvas and is due at 11:59pm the due date.
- Midterm: 35%
- Final exam: 40%