Syllabus Stats 535: Regression Analysis - WSU Math ...

Syllabus Stats 535: Regression Analysis
                               Spring Semester 2021, 3 Credits

Time & Place: 12:05 – 1:20 pm
              Zoom

Instructor: Dr. Leslie New
            Undergraduate Building 341
            (360) 546-9309, leslie.new@wsu.edu

Office Hours: Wednesdays 1-3 pm, or by appointment
              Via Zoom.
              Contact instructor for meeting information.

Textbook: A textbook is required for the course, as it is where most of the code you will
need will be found. The book is the type you will use over and over again if you do
regression in your future research, so while expensive, the book is a good investment.

Zuur A, Ieno EN, Walker N, Saveliev AA and Smith GM (2009) Mixed Effects Models and
 Extensions in Ecology with R. Springer, New York NY.

Suggested prerequisites: Stats 423/523 or equivalent or permission of the instructor

Course description: There are many common issues in data that force complexity into the
statistical analysis. These issues include repeated measurements, nested data, zero-inflated data
and many others. The goal of this course is to help students develop the statistical skills to deal
with these issues correctly, while avoiding the technical details outside the interest of most non-
mathematically inclined individuals. As a result, this course will build upon students’ basic
knowledge of regression to introduce topics such as generalized least squares and generalized
linear, additive and mixed effects models, while focusing on how and why these tools are
applied. The course will aim to teach these tools through their application, allowing students to
see how data collection and scientific inquiry are used to help define the appropriate statistical
analysis.

Course Structure: The course will be taught as a “flipped class”, where the majority of
instruction will occur in the form of recorded lectures that can be watched asynchronously and
the in-person class time will be spent in discussion, learning R, practice applying the concepts
covered in lecture, and question-answer sessions. These in-person sessions are not the same as
office hours, particularly as there is the expectation for small group work and engagement, as
well as in-class problem solving activities separate from those assigned as homework.

Learning outcomes: Students will be able to perform statistical analyses that involve the
critical assessment of data and model outcomes to ensure that they are applying the most
appropriate statistical methods. Furthermore, students will demonstrate their ability to
disseminate the results of their analyses to the wider community through published literature.
Students’ achievement of these outcomes will be determined through the course evaluation.

Evaluation: Course evaluation will be done through continuous assessment. Bi-weekly
assignments will provide a chance for students to apply what they have recently learned to a
relevant data set, while the final will test students’ knowledge via a more complex data set.

Discussion, collaboration and assistance between students is allowed and encouraged, but all
submissions must be written and submitted by a single individual. If a significant percentage of
your code was provided by a classmate or an online resource, a citation or acknowledgement
must be included or it will be considered plagiarism. Code from lecture does not require a
citation, nor does a resource that helped you use a function (e.g., much of what is on websites
such as StackOverflow or CrossValidated).

Grades will be broken down into 2 components: Assignments and Final. Assignments will be
short submissions, while the final will require a more detailed write-up.

           Component                 Percentage               Due Dates
           Assignments                  50%          5 Feb, 19 Feb, 5 Mar, 19 Mar,
                                                             2 Apr, 16 Apr
           Final                        50%                      3 May

Grading scale:
A : 94-100 B+: 87-89.99 C+: 77-79.99 D+: 67-69.99 F:

If you wish to appeal a faculty member's decision relating to academic integrity, please use
the form available at conduct.wsu.edu.

Submission of assignments: All assignments should be submitted by 5 pm PST on the day
they are due. Due to the COVID-19 pandemic, no late penalty will be assessed for
assignments turned in after this date. However, with the exception of the final project, all
assignments must be turned in no later than 5 pm PST on Friday 23 April 2021. Unless prior
arrangements have been made with the instructor, any assignment submitted after this
deadline will have earned a grade of zero.

Students with disabilities: The Graduate School is committed to providing equal
opportunity in its services, programs, and employment for individuals with disabilities.
Reasonable accommodations are available for students with a documented disability.
Students are responsible for initiating requests for reasonable accommodations and services
that they need.

Graduate students with identified disabilities should contact the Access Center before the
semester that they plan to attend and initiate the accommodations process. Accommodations
are unique for each individual and some require a significant amount of time to prepare for,
so it is essential that students notify the Access Center as far in advance as possible. Students
with a disability that is identified during the semester should contact the Access Center as
soon as possible to arrange for an appointment and a review of their documentation by an
Access advisor. All accommodations must be approved through the Access Center located
on each campus. Contact information for the Access Center at each campus can be found at
the following websites:
 x Pullman: http://accesscenter.wsu.edu/
 x Tri-Cities: http://www.tricity.wsu.edu/disability/
 x Vancouver: http://studentaffairs.vancouver.wsu.edu/access-center
All students requesting reasonable accommodation must meet with the instructor prior to or
during the first week of the course to review all proposed accommodations in relation to
course content and requirements. Exceptions to this timeframe will be granted only upon a
showing of good cause.

Safety and Emergency: WSU has made an emergency notification system available for
faculty, students, and staff. Please register at myWSU with emergency contact information
(cell, email, text, etc.). You may have been prompted to complete emergency contact
information when registering for classes at myWSU. In the event of a building evacuation, a
map at each classroom entrance shows the evacuation point for each building. Please refer to
it. Finally, in case of class cancellation campus-wide, please check local media, the WSU
Vancouver web page (https://www.vancouver.wsu.edu) and/or http://www.flashalert.net/.
Individual class cancellations may be made at the discretion of the instructor.

Inclement weather policy:
University Official Policy: In the event that an adverse weather event (e.g., snow or ice) or
natural hazard that poses a safety risk occurs, you should take personal safety into account
when deciding whether you can travel safely to and from campus, taking local conditions into
account. If campus remains open and your instructor decides to cancel the face-to-face
meeting and substitute an alternative learning activity, you will be notified by your instructor

via email or through Blackboard within a reasonable time after the decision to open or close
campus has been made. Instructions regarding any alternative learning options or assignments
will be communicated in a timely manner. If travel to campus is not possible due to adverse
regional conditions, allowances to course attendance policy and scheduled assignments,
including exams and quizzes, will be made. Students who attempt to gain advantage through
abuse of this policy (e.g., by providing an instructor with false information) may be referred
to the Office of Student Conduct for disciplinary action. If a student encounters an issue with
an instructor, the student should first talk with the instructor. If the issue cannot be resolved,
the student should follow the reporting violations of policies outlined on the student affairs
website.

Class Policy: If Vancouver’s campus is closed, then lectures that day will be cancelled. If
Vancouver’s campus is open, and the instructor decides to cancel class, you will be informed
via email and Blackboard as soon as possible. In addition, the class this year is spread over
four locations, which can all experience very different weather. Regardless of what is
occurring at Vancouver, if you are experiencing weather conditions that make it unsafe for
you to come to campus, please do not do so. Your health and safety should always come first.
The lectures for this class will be recorded, so you will not miss any material for which you
will be responsible. For emergency alerts, including those on weather, see:
http://alert.wsu.edu/

Sexual Harassment and Discrimination: Discrimination at Washington State University, on
the basis of race, sex, sexual orientation, gender identity/expression, religion, age, color,
creed, national or ethnic origin, physical, mental or sensory disability, marital status, genetic
information, and/or status as a veteran, is prohibited by federal law, state law and WSU
policy. All WSU employees who have information regarding an incident or situation
involving sexual harassment or sexual misconduct are required to promptly report the
incident to the Office for Equal Opportunity (OEO) or to one of the designated Title IX Co-
Coordinators. Students who are the victim of and/or witness sexual harassment or sexual
misconduct should also report to OEO or their Title IX Coordinator.

Tentative lecture outline
The schedule of topics is not fixed. Enough time will be spent on each to ensure students
have a solid grounding in one topic until we move to another. I will be keeping an eye on our
overall progress and we will cover at least through GLMMs. Time permitting we may also
cover GAMs and GAMMs.
 Week              Date            Subject                                          Due
   1          19-22 Jan 2021       Introduction

   2         25-29 Jan 2021       Regression refresher

   3          1-5 Feb 2021        Model selection                             Assignment 1

   4         8-12 Feb 2021        Generalized least squares

   5        15-19 Feb 2021        Introduction to generalized linear models   Assignment 2
                                  Class Holiday Mon. 15 Feb

   6        22-26 Feb 2021        GLM for binomial data
                                  Class Holiday Thurs. 25 Feb

   7          1-5 Mar 2021        GLM for Bernoulli data                      Assignment 3

   8         8-12 Mar 2021        GLM for count data

   9        15-19 Mar 2021        Zero-inflated models                        Assignment 4
                                  Class Holiday Wed 17 Mar

  10        22-26 Mar 2021        GLM model selection and model
                                  averaging

  11      29 Mar – 2 Apr 2021     Introduction to mixed models                Assignment 5

  12          5-9 Apr 2021        Linear mixed models

  13        12-16 Apr 2021        GLMMs in practice                           Assignment 6
                                  Class Holiday Tues 13 Apr

  14        19-23 Apr 2021        Analysis of fit for GLMMs

  15        26-30 Apr 2021        Introduction to generalized additive
                                  models

 Finals      3-7 May 2021                                                          Final