Kevin Shoemaker
Office: FA 220E
Phone: (775)682-7449
Email: kevinshoemaker@unr.edu
Office hours: Mondays at 1pm in FA 220e
Lecture & Discussion: M, W at noon in XXX (50
mins)
Lab: Tuesday at 3pm in XXX (2 hours 45 mins)
Modern computers have reduced many of the barriers to advanced data analysis, and powerful (and free!) computational tools are now available that enable “ordinary” researchers like you and me to answer questions that would have been considered impossible in previous generations! Armed with basic concepts of probability and statistics, some computer programming skills - and a deep appreciation and curiosity about the natural world- natural resources scientists can tease more out of their data than ever before.
In this course, we embrace mathematics!. Many of us do not consider ourselves very math-savvy, and some of us shudder when we see a mathematical equation. But there’s no getting around the importance of mathematics to science, and there is no under-estimating the value of mathematics for thinking logically and unambiguously communicating that logic to others. There are no mathematics prerequisites for this course, but we will be using equations, including some concepts from multivariable calculus and linear algebra.
In this course, we embrace computer programming!. A focus on computational methods for statistical inference allows us to focus on our study systems first and foremost, instead of being constrained by the ‘canned’ statistical tools available to us. Where appropriate, we standard analysis tools are generally your best bet because these methods have been rigorously tested over the years. But sometimes, our research questions or intricacies of the data generating process lead us to places no statistician has gone before, and we have the tools at our fingertips that let us build our own algorithms. When we build our own algorithms, we are entering uncharted territory. And exploring these territories can be dangerous… and your inner voices (quite rationally) might tell you not to go there, just play it safe. Don’t listen to those voices! In this class, you are allowed - and encouraged - to be dangerous! Just remember not to apply this motto uncritically outside the classroom!! It can be exciting to apply novel models to data! But remember: science, and the statistical methods that support it, require integrity and rigor above all. Therefore, we need to move slowly and cautiously- especially when they are trying something totally new!!
By the end of this course, students should have the ability to (1) simulate data under realistic scenarios, (2) make inferences from data using maximum likelihood estimation (MLE) and Bayesian techniques, (3) assess model adequacy, goodness-of-fit and predictive performance, (4) understand where and when to use a wide variety of additional advanced data analysis methods, and most importantly (5) have the competencies needed to learn about and try other methods that we won’t have time to get to in this course. The goal is for students to emerge from this course as creative data analysts with the tools and intuition needed to draw inferences from complex and messy data.
Our general focus will be on model-based inference, including linear and non-linear regression, multi-level models, and multi-model inference. Additional student-led modules will cover advanced analysis topics that build upon these core concepts (we’ll discuss this more in class). In all cases, our primary focus will be on practical implementation- the mathematical theory comes second, and we leave much of the nitty gritty details to statisticians.
The laboratory portion of the class will provide students the opportunity to try out some of the data analysis methods we discuss in lecture. The first 8-9 lab periods will be led by the instructor, and the remainder (second half of the semester) will be student-led. In the student-led portion of lab, students will work in groups to lead a hands-on demonstration of an advanced analysis method, and lead a short ‘mini-lab’ to give their classmates a chance to practice test their skills with the method.
Students will be able to:
Curious scientific mind, broad research interests, and willingness to engage with equations and computer code. Students are expected to already have a basic foundation in mathematical and statistical concepts and methods, obtained through other coursework. If this is not the case, they should be prepared to work harder to develop the necessary prerequisite knowledge.
We will use the book, Ecological Models and Data in R, by Ben Bolker, as a general class reference.
A recommended text (which I will draw some examples from and may serve as a primary text in the future) is Statistical Rethinking by Richard McElreath.
Additional readings may be assigned -please check the course schedule.
Other related books you may find useful include “A Primer of Ecological Statistics” by Gotelli and Ellison, “Bayesian Models: a Statistical Primer for Ecologists” by Hobbs and Hooten, and the textbooks on hierarchical models by Kery and Royle.
Student-led lectures and mini-labs: Each student
will work with a small group (2-3) to lead a 50 minute
lecture/demonstration and a 1-hour hands-on ‘mini-lab’ activity that
introduces a relevant data analysis method (for this course, must be
likelihood-based, and implemented in R). The lecture component will
provide an overview of the method and the underlying theory (with
equations!) along with some published real-world applications of the
method. The mini-lab will consist of a worked example (clear, concise,
informative tutorial) and additional activities that provide classmates
with an opportunity to test their knowledge of the method. Presenters
are encouraged to work with the instructor (and other faculty, graduate
students!) to develop their lectures and mini-lab activities.
Class Participation: Students are expected to actively
participate in the classroom. Don’t be afraid to ask questions- this is
difficult material, and this classroom is a safe space for exploring and
making mistakes*
Laboratory Reports: Students will submit (1) an R
script (‘.R’ file); here, a set of R functions, each of which performs a
specific assigned task, and (2) a brief written report (in Word,
submitted via WebCampus) succinctly answering any questions, and stating
any questions or points of confusion with the lab exercises. While
students are encouraged to work on the labs in small groups, all lab
submissions must be made individually.
| Course component | Weight |
|---|---|
| Student-led topics | 40% |
| Participation | 20% |
| Laboratory exercises | 40% |
NOTE: the course schedule is subject to change, so please check back frequently!
If you miss a class meeting or lab period, it is your responsibility to talk to one of your classmates about what you missed. If you miss a lab meeting, you are still responsible for completing the lab activities and write-up by the stated due date. Please let your instructor know in advance if you need to miss class or lab.
Any student with a disability needing academic adjustments or accommodations is requested to speak with the instructor or the Disability Resource Center (Pennington Achievement Center Suite 230) as soon as possible to arrange for appropriate accommodations. This course may leverage 3rd party web/multimedia content, if you experience any issues accessing this content, please notify your instructor.
Surreptitious or covert video-taping of class or unauthorized audio recording of class is prohibited by law and by Board of Regents policy. This class may be videotaped or audio recorded only with the written permission of the instructor. In order to accommodate students with disabilities, some students may have been given permission to record class lectures and discussions. Therefore, students should understand that their comments during class may be recorded.
The University of Nevada, Reno is committed to providing a safe learning and work environment for all. If you believe you have experienced discrimination, sexual harassment, sexual assault, domestic/dating violence, or stalking, whether on or off campus, or need information related to immigration concerns, please contact the University’s Equal Opportunity & Title IX office at 775-784-1547. Resources and interim measures are available to assist you. For more information, please visit the Equal Opportunity and Title IX page.
The University Academic Standards Policy defines academic dishonesty, and mandates specific sanctions for violations. See the University Academic Standards policy: UAM 6,502
Nevada’s Recovery and Prevention (NRAP) is a student-focused, peer-driven, collegiate recovery program and open to anyone in the University who wants recovery support, interested in being part of a recovery community, living a substance-free lifestyle, and/or seeking wellness services. For more information, visit nvrap.com, call 775.784.6224, email nrap@casat.org, or drop by the NRAP Lounge located at WRB 1001 (open M–F 9am-5pm).
NevadaCARES provides prevention opportunities campus-wide and confidential advocacy services to those impacted by sexual assault, relationship violence, and stalking. Confidential Advocates are available to provide support to students who have experienced any form of power-based violence. For more information or to request an appointment, call 775.682.8006 or email nvcares@unr.edu.”
Pursuant to Nevada law, NSHE employees, students and members of the public are no longer required to wear face coverings while inside NSHE buildings irrespective of vaccination status.
Disinfecting supplies are provided for you to disinfect your learning space. You may also use your own disinfecting supplies.
Students testing positive for COVID 19 or exhibiting COVID 19 symptoms regardless of vaccination status will not be allowed to attend in-person instructional activities and must leave the venue immediately. Students should contact the Student Health Center or their health care provider to receive care and who can provide the latest direction on quarantine and self-isolation. Contact your instructor immediately to make instructional and learning arrangements.
For students who are required to quarantine or self-isolate due to 1) COVID 19 infection or 2) exposure while not vaccinated, instructors must provide opportunities to make-up missed course work, including assignments, quizzes or exams. In courses with mandatory attendance policies, instructors must not penalize students for missing classes while quarantined.
In accordance with section 6,502 of the University Administrative Manual, a student may receive academic and disciplinary sanctions for failure to comply with policy, including this syllabus, for failure to comply with the directions of a University Official, for disruptive behavior in the classroom, or any other prohibited action. “Disruptive behavior” is defined in part as behavior, including but not limited to failure to follow course, laboratory or safety rules, or endangering the health of others. A student may be dropped from class at any time for misconduct or disruptive behavior in the classroom upon recommendation of the instructor and with approval of the college dean. A student may also receive disciplinary sanctions through the Office of Student Conduct for misconduct or disruptive behavior, including endangering the health of others, in the classroom. The student shall not receive a refund for course fees or tuition.