Description

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

This course will make a balance between practical real data analysis with examples and a deeper understanding of the models with mathematical derivations.

Textbook

• Foundations of Linear and Generalized Linear Models by Alan Agresti (required)
• Extending the Linear Model with R (second edition) by Julian J. Faraway (suggested)

Grading

• Homework assignments: 20%
  • There will be 4 assignments in total.
  • Late homework will not be accepted for grading (medical emergencies excepted with proof).
  • Homework will be submitted through Gradescope and is due at 11:59pm the due date.
• Midterm: 40%
• Final exam: 40%