Simon Fraser University
Engaging the World

Course Outlines

Fall 2024 - EVSC 445 D100

Environmental Data Analysis (4)

Class Number: 4845

Delivery Method: In Person

Overview

  • Course Times + Location:

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

  • Prerequisites:

    GEOG 251, or one of STAT 100, 201, 203, 205 or 270 or permission of the instructor.

Description

CALENDAR DESCRIPTION:

Introduces environmental scientists to application of modern data analysis methods. This course covers sampling, experimental design, and the analysis of quantitative data collected in the course of environmental monitoring, assessment and restoration programs. Students will be introduced and gain experience with the statistical programming language R.

COURSE DETAILS:

The content of EVSC 445 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 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-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, ineciency and confounding.
  •  Demonstrate an understanding probability and random variables.
  •  Understand the concept of the sampling distribution of a statistic.
  •  Understand the foundations for con dence 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, mixed e ect models and principle
component analysis.

Grading

  • Homework 30%
  • Mini-Project 10%
  • Mid-Term 1 20%
  • Mid-Term 2 20%
  • Mid-Term 3 20%

NOTES:

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. Lectures and Laboratories will be in person synchronously. Any missed lectures or labs are the responsibility of the student. All slides, lab assignments and other relevant course material will be posted to the ENV 445 Canvas site. Students are expected to participate and follow along on a laptop computer. Computers are available on loan from the SFU Bennett library.

REQUIREMENTS:

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/

Materials

REQUIRED READING:

We will be loosly following the textbook "Learning Statistics Using R" by Randall E. Schumacker. This is o ffered through SFU Library as an e-book.
ISBN: 9781483313320

The course is based on several additional sources which will be assigned throughout the semester and added to the SFU 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.

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.