Course Form
I. Summary of Proposed Changes
Dept / Program
Forest Management
Course Title
Prefix and Course
#
FORS 538
Applied Statistical Modeling in Ecology
Short Title (max. 26 characters incl. spaces)
Ecological Statistics
Summarize the change(s) proposed
Change experimental course to permanent status
II. Endorsement/Approvals
Complete the form and obtain signatures before submitting to Faculty Senate Office
Please type / print name Signature
Date
Requestor:
Solomon Dobrowski
Phone/ email :
6068
solomon.dobrowski@cfc.umt.edu
Program Chair/Director:
LLoyd Queen
Other affected programs
Dean:
James Burchfield
Are other departments/programs affected by this modification
Please obtain signature(s) from the
because of
Chair/Director of any such department/
(a) required courses incl. prerequisites or corequisites,
program (above) before submission
(b) perceived overlap in content areas
(c) cross-listing of coursework
III: To Add a New Course Syllabus and assessment information is required (paste syllabus into
section V or attach). Course should have internal coherence and clear focus.
Common Course Numbering Review (Department Chair Must Initial):
YES
NO
Does an equivalent course exist elsewhere in the MUS? Check all relevant disciplines if
X
course is interdisciplinary. (http://mus.edu/transfer/CCN/ccn_default.asp)
If YES: Do the proposed abbreviation, number, title and credits align with existing course(s)? Please indicate
equivalent course/campus. 
If NO: Course may be unique, but is subject to common course review. Be sure to include learning outcomes
on syllabus or paste below. The course number may be changed at the system level.
Graduate courses are not subject to common course review.
Exact entry to appear in the next catalog (Specify course abbreviation, level, number, title, credits,
repeatability (if applicable), frequency of offering, prerequisites, and a brief description.) 
G FORS 538 Applied Statistical Modeling in Ecology 3cr. Offered in the Fall. Prerequisites:
STAT451/452 or equivalent. This is an applied course covering advanced statistical modeling
techniques using examples from forestry, ecology, and the environmental sciences. Covers data
management, visualization, and scripting with R, an open source data analysis and statistics
platform. Explores various parametric and semi-parametric modeling strategies that allow for nonlinear 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 are discussed using examples from the scientific literature. Lays the foundation for
subsequent graduate-level analytic coursework.
Justification: How does the course fit with the existing curriculum? Why is it needed?
There is an immense need for graduate level analytical coursework in CFC as attested to the robust
enrollment in this course over the last 3 years. We offer no similar classes. Additionally, this class lays the
groundwork for subsequent analytical coursework offered in CFC at the graduate level and is intended to be a
prerequisite for students moving through the curriculum.
Are there curricular adjustments to accommodate teaching this course?
No
Complete for UG courses (UG courses should be assigned a 400 number).
Describe graduate increment - see procedure 301.30
http://umt.edu/facultysenate/committees/grad_council/procedures/default.aspx
Complete for Co-convented courses
Companion course number, title, and description (include syllabus of companion course in section V)
See procedure 301.20 http://umt.edu/facultysenate/committees/grad_council/procedures/default.aspx.
New fees and changes to existing fees are only approved once each biennium by the
Board of Regents. The coordination of fee submission is administered by Administration
and Finance. Fees may be requested only for courses meeting specific conditions
according to Policy 940.12.1 http://mus.edu/borpol/bor900/940-12-1.pdf . Please
indicate whether this course will be considered for a fee.
If YES, what is the proposed amount of the fee?
Justification:
YES
NO
X
IV. To Delete or Change an Existing Course – check X all that apply
Deletion
Title
Course Number Change
From:
Level U, UG,
From:
G
To:
To:
Co-convened
Description Change
Repeatability
Change in Credits
From:
Cross Listing
(primary
To:
program
initiates form)
Prerequisites
Is there a fee associated with the
course?
1. Current course information at it appears in catalog
2. Full and exact entry (as proposed) 
(http://www.umt.edu/catalog) 
3. If cross-listed course: secondary program & course number
4. If co-convened course: companion course number, title, and description
(include syllabus of companion course in section V) See procedure 301.20
http://umt.edu/facultysenate/committees/grad_council/procedures/default.aspx.
5. Is this a course with MUS Common Course Numbering?
http://mus.edu/transfer/CCN/ccn_default.asp
If yes, please explain below whether this change will eliminate the course’s common course
status.
6. Graduate increment if level of course is changed to UG.
Reference procedure 301.30:
http://umt.edu/facultysenate/committees/
grad_council/procedures/default.aspx
(syllabus required in section V)
7. Other programs affected by the change
8. Justification for proposed change
YES NO
Have you reviewed the graduate
increment guidelines? Please check
(X) space provided.
V. Syllabus/Assessment Information
Required for new courses and course change from U to UG. Paste syllabus in field below or attach and send
digital copy with form.
Applied Statistical Modeling in Ecology
Fall 2010: Monday 3:30-5:00; Wed 3:30-5:00, Old Journalism 107
Instructors:
Solomon Dobrowski
Office: FOR 212
Phone: 6068
Email: solomon.dobrowski@cfc.umt.edu
Office hours: Wed 1:00-2:00; Thursday 1:00-2:00; or by appointment
David Affleck
Office: Clapp 405
Phone: 4186
Email: david.affleck@umontana.edu
Office hours: Monday 3:00-4:00; Tuesday 11:00-12:00
Course Description:
This is an applied course covering advanced statistical modeling techniques using examples from
forestry, ecology, and the environmental sciences. We 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, we will explore various parametric and semi-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 will be
offered in the future.
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., multiple 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 WIKI at http://currents.cfc.umt.edu:8080/dashboard.action
There is no required textbook although we 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
We will make some of these texts available on reserve in the library.
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 with using R. Also, at
the end of specific sections in the course we will hand out synthesis assignments designed at assessing 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, we ask that
you break into groups of two students. Students are likely to have varying backgrounds and experience with
statistics and R. Consequently, we 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, we ask that you rotate through partners over the course of the
semester.
Grades (120 total points)
There will be 5 synthesis assignments given out over the course of the semester. Each is worth 20 points.
There will also be short problems handed out weekly worth 1-2 points each. Each group of two students
will turn in a single assignment and will receive the same grade for that assignment.
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 is quickly becoming the standard that is being 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:
http://www.r-project.org/
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:
week
date
1
Aug 29-Sept 2
Lecture/discussion topic
Introduction
Introduction to R
2
3
4
Sept 5-9
Labor Day 9/5, No Class
R Syntax and scripting,
Univariate data analysis,
Simple Linear Regression
Instructor
Synthesis
Assignment
Dobrowski/
Affleck
Dobrowski
Sept 12-16
Bootstrapping, resampling,
empirical distributions
Dobrowski
Multiple linear regression &
regression diagnostics
Affleck
Sept 26-Oct 1
Development and
applications of linear
regression models
Affleck
Oct 3-Oct 7
Factors/ Factor response
Exploratory analysis
Dobrowski
Oct 10-14
Classification and regression
trees
Dobrowski
Oct 17-21
Generalized Linear Models
(GLMs) – motivations and
mechanics
Affleck
Oct 24-28
GLMs – deviance and
diagnostics
Affleck
Oct31-Nov4
GLMs – logistic models
Affleck
Nov 7-11
GLM – overdispersion
Affleck
Nov 14 -18
Linear mixed effects models
Wood
Nov 21-25
Thanksgiving (no classes)
Nov 28-Dec 2
Smoothers/ Generalized
1.Univariate data
analysis and LM
Sept 19-23
5
2. Multiple linear
regression and
diagnostics
6
7
3. CART and
exploratory data
analysis
8
9
10
11
4. GLM
12
13
14
Dobrowski
5. Synthesis
Additive Models
assignment
15
Dec 5-10
Model selection/ validation
Dobrowski
VI Department Summary (Required if several forms are submitted) In a separate document list course
number, title, and proposed change for all proposals.
VII Copies and Electronic Submission. After approval, submit original, one copy, summary of
proposals and electronic file to the Faculty Senate Office, UH 221, camie.foos@mso.umt.edu.
Revised 5-4-11