Dobrowski, Solomon Z.

English

University of Montana

University of Montana ScholarWorks at University of Montana University of Montana Course Syllabi, 2021-2025 Fall 9-1-2021 FORS 538.01: Statistical Models for Ecological Data Analysis Solomon Z. Dobrowski University of Montana, Missoula, solomon.dobrowski@umontana.edu Follow this and additional works at: https://scholarworks.umt.edu/syllabi2021-2025 Let us know how access to this document benefits you. Recommended Citation Dobrowski, Solomon Z., "FORS 538.01: Statistical Models for Ecological Data Analysis" (2021). University of Montana Course Syllabi, 2021-2025. 1131. https://scholarworks.umt.edu/syllabi2021-2025/1131 This Syllabus is brought to you for free and open access by ScholarWorks at University of Montana. It has been accepted for inclusion in University of Montana Course Syllabi, 2021-2025 by an authorized administrator of ScholarWorks at University of Montana. For more information, please contact scholarworks@mso.umt.edu. FORS 538 Statistical Models for Ecological Data Analysis  Lab periods scheduled for Tuesday, Thursday 11:00-12:30; Old Journalism (Stone Hall) 107  Instructors: Solomon Dobrowski Office: CHCB 438 Phone: 6068 Email: solomon.dobrowski@umontana.edu Office hours: by appointment   Course Description: This is an applied course covering advanced statistical modeling techniques using examples from forestry, ecology, and the environmental sciences. I will cover data management, visualization, and scripting with R, an open source data analysis and statistics platform which is rapidly becoming the standard in many scientific disciplines. After reviewing the linear regression model and associated diagnostics, I will explore various parametric and non- parametric modeling strategies that allow for non-linear response functions and/or non- Gaussian response distributions. Estimation and inference in the context of generalized linear models, generalized additive models, and classification and regression trees will be discussed using examples from the scientific literature. This course will lay the foundation for subsequent graduate-level analytic coursework that is offered.  Prerequisites: Students must have previous coursework in statistics to the level of STAT 451 and 452 (formerly MATH 444 and 445) or equivalent (i.e., linear regression and ANOVA).  Course Material: Course materials will principally be comprised of primary research articles, online primers, and tutorials. All course materials will be distributed in class, are available online, or will be made available via the course Moodle website.  There is no required textbook although I recommend the following texts, both for gaining traction with R and for further reference on advanced techniques:  Faraway – Linear Models with R and Extending the Linear Model with R Venables and Ripley - Modern Applied Statistics with S Murrell - R Graphics  Objectives: 1) Gain familiarity with data analysis and visualization techniques using R 2) Explore the utility and limitations of standard linear regression models 3) Apply and interpret generalized linear models and generalized additive models 4) Implement classification and regression trees for exploratory data analysis 5) Examine and apply different model diagnostics to help choose an appropriate modeling strategy for a given analysis  Procedures: Lecture/Lab/Discussion The course will be a mixture of lectures, interactive labs, and discussion of primary literature. Grading will be based on written assignments.  • Course Assignments  At the end of lectures you will be assigned short problems to develop practical skills using R. Also, at the end of specific sections in the course I will hand out synthesis assignments designed to assess your knowledge of the statistical material presented.  Synthesis assignments will be required to have a scientific format with an introduction, methods, results, and discussion. For most of these assignments, I ask that you break into groups of two students. Students are likely to have varying backgrounds and experience with statistics and R. Consequently, I want to emphasize the importance of working collaboratively on these assignments. If you have a strong background in stats, partner with someone with less experience. If you have experience scripting in R, partner with someone who has less. This will facilitate the learning experience for the course as a whole. Also, I require that you rotate through partners over the course of the semester.  • Grades (100 total points)  There will be 4 major assignments given out over the course of the semester. The first three will be group projects worth 20 points each. Each group of two students will turn in a single assignment and will receive the same grade for that assignment. The final assignment is worth 40 points and will be a synthesis project that allows students to use their own data or to work with a sample dataset given out by the instructor.  • Software  R – a language and environment for statistical computing and visualization. For those of you with previous coding experience, scripting in R should be nothing new. To those of you with no previous experience in command-line environments, this will be your first introduction to scripting. R is open-source software so it is free. It has become the standard used in the biological sciences for data analysis and visualization. If you plan to continue in this line of work knowledge of R may be the single most important practical skill you take away from this course. There are copious amounts of documentation available for R. In addition to the handouts we provide, we suggest the intro manual located at:  R Intro Manual  Data: A number of datasets will be made available to you for use in labs and for use in the assignments. Some of these datasets are public domain, and some are proprietary or have value added and represent the work of many individuals. You do not have open access permission to these data beyond work conducted in this course. In other words, you cannot use the data for any other purpose without permission from the instructor or persons or agencies that steward the respective datasets. We will provide as much information as possible as to the provenance and availability of datasets.  Schedule (subject to change): week date Lecture/discussion topic Instructor Synthesis Assignment 1 Aug 30 Introduction Introduction to R Dobrowski  2 Sep 6 R Syntax and scripting, Univariate data analysis, Simple Linear Regression Dobrowski  3 Sep 13 Bootstrapping, resampling, empirical distributions Dobrowski 1.Univariate data analysis and LM 4 Sept 20 Factors/ Factor response Exploratory analysis Dobrowski  5 Sept 27 Classification and regression trees Dobrowski 2. CART and exploratory data analysis 6 Oct 4 Multiple linear regression & regression diagnostics Dobrowski  7 Oct 11 Development and applications of linear regression models Dobrowski  8 Oct 18 Generalized Linear Models (GLMs) – motivations and mechanics Dobrowski  9 Oct 25 GLMs – deviance and diagnostics Dobrowski  10 Nov 1 GLMs – logistic models Dobrowski 3. GLM 11 Nov 8 GLM – overdispersion Dobrowski  12 Nov 15 Smoothers/ Generalized Additive Models TBD 4. Synthesis assignment 13 Nov 22 Smoothers/ Generalized Additive Models Dobrowski  14 Nov 29 Model selection/ validation Dobrowski  15 Dec 6 Final presentations Dobrowski  COVID related information   • Mask use is required within the classroom or laboratory. • If you feel sick and/or are exhibiting COVID-19 symptoms, please don’t come to class and contact the Curry Health Center at (406) 243-4330. • If you are required to isolate or quarantine, I will do my best to ensure you can continue with academic progress. • UM recommends students get the COVID-19 vaccine. Please direct your questions or concerns about vaccines to Curry Health Center. • Where social distancing (maintaining consistent 6 feet between individuals) is not possible, specific seating arrangements will be used to support contact tracing efforts. • Class attendance and seating will be recorded to support contact tracing efforts. • Drinking liquids and eating food is discouraged within the classroom.   Accessibility Statement: The University of Montana assures equal access to instruction through collaboration between students with disabilities, instructors, and the Office for Disability Equity (ODE). If you anticipate or experience barriers based on disability, please contact the ODE at: (406) 243- 2243, ode@umontana.edu, or visit www.umt.edu/disability for more information. Retroactive accommodation requests will not be honored, so please, do not delay. As your instructor, I will work with you and the ODE to implement an effective accommodation, and you are welcome to contact me privately if you wish.  Student Conduct Code o All students must practice academic honesty. Academic misconduct is subject to an academic penalty by the course instructor and/or a disciplinary sanction by the University. All students need to be familiar with the Student Conduct Code. 

FORS 538.01: Statistical Models for Ecological Data Analysis

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FORS 538.01: Statistical Models for Ecological Data Analysis

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