INF511: Modern Regression I

Spring 2024

Author

Affiliation

Joseph Mihaljevic, PhD

Northern Arizona University

Preface

Welcome to INF511: Modern Regression I. In this course, we will do a deep dive into three fundamental methods for estimating the linear relationships between random variables (i.e., linear regression analysis): 4  Ordinary Least Squares, 6  Maximum Likelihood, and 9  Bayesian Inference. We will also explore 5  Hypothesis Testing, and linear models with categorical covariates via 8  ANOVA. This online book serves as a living document of resources for our class. The chapters are complimentary to the lecture handouts. Note that handouts should be downloaded (from Canvas) and printed prior to class. The coded examples on this website will be helpful for solving problem set and homework assignments. Problem sets will have dedicated in-class time, whereas homework assignments will be conducted entirely outside of class time.

Please refer to the Appendix A: Syllabus for the course schedule, learning objectives, grading structure, course policies, etc.

Footnotes

• This is a Quarto book. To learn more about Quarto books visit https://quarto.org/docs/books.
• This website is published using Github Pages.
• See Knuth (1984) for additional discussion of literate programming.

Knuth, Donald E. 1984. “Literate Programming.” Comput. J. 27 (2): 97–111. https://doi.org/10.1093/comjnl/27.2.97.