Simon Fraser University
Engaging the World

Course Outlines

Fall 2024 - EVSC 645 G100

Environmental Data Analysis (4)

Class Number: 7390

Delivery Method: In Person

Overview

  • Course Times + Location:

    Sep 4 – Dec 3, 2024: Wed, Fri, 8:30–10:20 a.m.
    Burnaby

  • Prerequisites:

    Enrollment in the ER MSc program, or permission of the instructor.

Description

CALENDAR DESCRIPTION:

Introduce environmental scientists to sampling, experimental design, and the analysis of qualitative data collected in the course of environmental monitoring, assessment and restoration programs. Students with credit for ENV 645 under the title Statistics for Ecological Restoration may not take this course for further credit.

COURSE DETAILS:

The content of EVSC 645 is intended to introduce environmental scientists to the statistical methods that will be useful for them in their work, and provide practical experience therein. This course covers the basic and most useful methods of sampling and experimental design, and the analysis of data collected in observational studies and designed experiments. Particular emphasis will be placed on practical aspects of sampling and experimentation in environmental applications. Examples will be drawn from the literature, and from the instructor's own experience. A lab tutorial accompanies the lectures that will include practical examples of the concepts presented in lectures and will give additional support for learning the R programming language.

Students will be introduced to the principles of statistics and the course aims to:

1. Motivate an intrinsic interest in statistical thinking and understand its importance in scientific research.
2. Learn to formulate statistical hypotheses, understand assumptions and build confidence in data analysis and interpretation.
3. Provide experience and support in learning the statistical programming language R.

COURSE-LEVEL EDUCATIONAL GOALS:

  • Demonstrate the ability to apply fundamental concepts in exploratory data analysis.
  • Design studies or experiments for obtaining data while avoiding common design flaws that incur bias, ineffciency and confounding.
  • Demonstrate an understanding of probability and random variables.
  • Understand the concept of the sampling distribution of a statistic.
  • Understand the foundations for confidence intervals and hypothesis testing.
  • Interpret and analyse data using parametric methods and non-parametric methods.
  • Apply and interpret simple and multiple linear regression models.
  • Exposure to some special topics including, for example, generalised linear models and mixed effect models.

Grading

  • Homeworks 30%
  • Mini Project 10%
  • Midterm 1 20%
  • Midterm 2 20%
  • Midterm 3 20%

NOTES:

* The mini project is a graduate student requirement. The mini-project is an opportunity for the graduate student to analyse data that is specific to their field of research, and/or holds some specific interest to the student.

Materials

MATERIALS + SUPPLIES:

The course will require the widely-used programming language R for statistical computing and graphics. This will be required for both lab tutorials and for homework assignments. Students are expected to download and install R or RStudio onto their computer from this website:

https://www.r-project.org/
https://rstudio.com/products/rstudio/

REQUIRED READING:

"Learning Statistics Using R" by Randall E. Schumacker and "Applied Statistics for Environmental Science with R" by Abbas F.M. AlkarkhiWasin A.A. Alqaraghuli. Both books are offered through SFU Library as e-books.

The course is based on several additional on-line sources which will be assigned throughout the semester and added to the ENV 645 Canvas link as the course progresses.

REQUIRED READING NOTES:

Your personalized Course Material list, including digital and physical textbooks, are available through the SFU Bookstore website by simply entering your Computing ID at: shop.sfu.ca/course-materials/my-personalized-course-materials.

Graduate Studies Notes:

Important dates and deadlines for graduate students are found here: http://www.sfu.ca/dean-gradstudies/current/important_dates/guidelines.html. The deadline to drop a course with a 100% refund is the end of week 2. The deadline to drop with no notation on your transcript is the end of week 3.

Registrar Notes:

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS

SFU’s Academic Integrity website http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating. Check out the site for more information and videos that help explain the issues in plain English.

Each student is responsible for his or her conduct as it affects the university community. Academic dishonesty, in whatever form, is ultimately destructive of the values of the university. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the university. http://www.sfu.ca/policies/gazette/student/s10-01.html

RELIGIOUS ACCOMMODATION

Students with a faith background who may need accommodations during the term are encouraged to assess their needs as soon as possible and review the Multifaith religious accommodations website. The page outlines ways they begin working toward an accommodation and ensure solutions can be reached in a timely fashion.