This course provides an introduction to data modeling using the R statistical computing language and
likelihood, information theoretic, and Bayesian approaches to inference. The course includes a
focus on the R language as a tool for data modeling and emphasizes examples and case studies from ecological
and environmental sciences.
Class Structure. This class is a hybrid online class, meaning that it is an online class that has synchronous
and asynchronous components. The online synchronous component will meet once a week for 75 minutes, where we will
engage in a variety of activities, primarily discussions of class material and assignments.
- Readings. This is a reading intensive class: We will read 1-2 chapters per week from the assigned
text. The chapter readings will provide the basis for online discussions in Yellowdig. There will be weekly
quizzes in Blackboard over the assigned readings.
- Supporting videos. Each week students will watch lecture videos, where the author
(Richard McElreath) of our text presents each chapter. These will reinforce our chapter readings and provide
an additional perspective on the assigned readings. These video lectures will also provide the basis for class
discussions in Yellowdig, e.g., please ask questions, make comments, and respond to your classmates' questions in
Yellowdig. We will also watch a series of short R videos that will introduce key concepts of R.
- Exercises. Students will receive a set of problems that incorporate the
statistical and/or R programming components of the assigned chapters for each week. Students can work on these in
small groups in or outside of class. I encourage students to work together and share information
and approaches as a means of learning the material, but each student should produce their own solutions (i.e.,
should write/type their own R code).
- Class presentations. Students will summarize the Yellowdig discussions of the assigned
readings. These presentations should provide a summary of the online Yellowdig discussion as well as key ideas and
components of material presented in the readings and video lectures (excluding the R videos). This is an opportunity
to highlight the material that students find confusing or unclear so that we can address this in class.
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Students will
- gain proficiency in the R statistical programming language
- become familiar with Bayesian approaches to statistical inference.
Achieving these goals will require:
- Completion of assigned readings and exercises
- Participation in assigned activities
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Student grades will be based on the following components:
Class presentations: 10% of course grade.
PRESENTER SCHEDULE
Participation in discussions through Yellowdig: 20% of course grade.
Quizzes on assigned readings: 10% of course grade.
Homework assignments as Complete/Incomplete: 40% of course grade.
Final exam: 20% of course grade.
Grading Scale:
Percentage |
Grade |
- 98-100
- 93-97
- 90-92
- 88-89
- 83-87
- 80-82
- 78-79
- 73-77
- 70-72
|
|
Grades less than 70 are considered failing for grad courses.
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Students will also be assigned readings from the following set of books (below). These books will be required for
this class and are available in the university bookstore or as otherwise noted below.
- Primary text for the statistics portion of this class: McElreath, Richard. 2020. Statistical Rethinking: A Bayesian Course with Examples in R and STAN (2nd Edition).
ISBN-13: 978-0367139919 Link
to book.
- Reference texts for R:
- The Book of R by Tilman M. Davies. Base R perspective. A good place to start.
- R for Data Science by Hadley Wickham and Garrett Grolemund. R from a Tidyverse perspective.
- Advanced R by Hadley Wickham. Great book for understanding the language.
We will also watch a variety of videos inside of class on both statistics and R.
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We will build data models in the free, open source programming language R. Below are some additional
resources for installing and using R as well as for our assigned text.
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