Course Schedule

All .qmd files for lectures can be found on GitHub: lecture folder

All data used in lecture can also be found on GitHub: data folder

Week 1: Review of Linear Regression

• Lecture Schedule:

  1. Review of Linear Regression slides qmd
  2. Visualizing the Model slides qmd
  3. Model Assumptions slides qmd
  4. Model Diagnostics slides qmd

Week 2: More on Linear Regression

• Lecture Schedule:

  1. Categorical Predictors: Data Management slides qmd
  2. Categorical Predictors: Modeling & Interpretation slides qmd
  3. Categorical Predictors: Statistical Inference slides qmd
  4. Visualizing the Model with Categorical Predictors slides qmd

Week 3: More on Linear Regression

• Lecture Schedule:

  1. Interactions: Continuous \(\times\) Continuous slides qmd
  2. Interactions: Categorical \(\times\) Categorical slides qmd
  3. Interactions: Categorical \(\times\) Continuous slides qmd
  4. Visualizing the Model: Interaction Terms slides qmd

Week 4: Gamma and Beta Regressions

• Lecture Schedule:

  1. Introduction to Generalized Linear Models slides qmd
  2. Gamma Regression slides qmd
  3. Visualizing the Model: Gamma Regression slides qmd
  4. Beta Regression slides qmd
  5. Visualizing the Model: Beta Regression slides qmd

Week 5: Logistic Regressions

• Lecture Schedule:

  1. Binary Logistic Regression slides qmd
  2. Multinomial (Nominal) Logistic Regression slides qmd
  3. Visualizing the Model: Logistic Regressions slides qmd

Week 6: Poisson and Negative Binomial Regressions

• Lecture Schedule:

  1. Poisson Regression slides qmd
  2. Negative Binomial Regression slides qmd
  3. Visualizing the Model: Poisson and Negative Binomial Regressions slides qmd

Week 7: Choosing a Distribution

• Lecture Schedule:

  1. Choosing the Right Modeling Approach reading qmd

Week 8: Final Exam

• Final exam opens on Friday morning at 12 am and is due by Friday evening at 11:59 pm, Central.

  • This is a timed exam; once you open the exam on Canvas, you will have 3 hours for completion.
  • The exam is cumulative and will cover all material from the course.
  • The exam is conceptual and applied; none of the questions will require/ask for coding.
  • No make up exams will be given.