Spring 2022 - EVSC 445 D100

STT-Environmental Data Analysis (4)

Class Number: 6826

Delivery Method: In Person


  • Course Times + Location:

    Tu 12:30 PM – 2:20 PM
    AQ 4130, Burnaby

  • Prerequisites:

    STAT 100, 201, 203, 205 or 270 or permission of the instructor.



Intended to introduce environmental scientists to application of modern data analysis methods. Covers sampling, experimental design, and the analysis of quantitative 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.


This course 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. The course will also include some special topics including, for example, the statistics of environmental impactassessment and those of assessing site reclamation. Examples will be drawn from the literature, and from the instructors 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.

Course Format
This course will consist of a weekly 2-hour lecture and a 2-hour interactive software tutorial where students will apply the concepts learned in lectures.


  • Homeworks 30%
  • Mini Project 10%
  • Midterm 1 (open-book) 20%
  • Midterm 2 (open-book) 20%
  • Final 20%


This course outline is subject to change and the instructor will share the final course outline by the end of the first week of classes.




The course will require the widely-used programming language Rf or statistical computing andgraphics. 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:



The course is based on several sources, which will be assigned throughout the semester and added to the SFU Canvas link as the course progresses.


Learning Statistics Using R by Randall E. Schumacker (2014).

Registrar Notes:


SFU’s Academic Integrity web site 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


Teaching at SFU in spring 2022 will involve primarily in-person instruction, with safety plans in place.  Some courses will still be offered through remote methods, and if so, this will be clearly identified in the schedule of classes.  You will also know at enrollment whether remote course components will be “live” (synchronous) or at your own pace (asynchronous).

Enrolling in a course acknowledges that you are able to attend in whatever format is required.  You should not enroll in a course that is in-person if you are not able to return to campus, and should be aware that remote study may entail different modes of learning, interaction with your instructor, and ways of getting feedback on your work than may be the case for in-person classes.

Students with hidden or visible disabilities who may need class or exam accommodations, including in the context of remote learning, are advised to register with the SFU Centre for Accessible Learning (caladmin@sfu.ca or 778-782-3112) as early as possible in order to prepare for the spring 2022 term.