FRST 231 (3 cr): Introduction to Biometrics

FRST 231 - Introductory Biometrics for Forestry

Course Description

Basic theories of probability and statistics. Sampling distribution, methods of estimation and hypothesis testing; goodness of fit and tests for independence; analysis of variance, regression and correlation.

Co-reqs: One of MATH 100, MATH 102, MATH 104, MATH 180, MATH 184, MATH 190.

Intended Student

  1. Students who need a good introductory applied statistics course with biological examples and who are unable to take the on-campus version of this course.
  2. Students in the various programs offered by the Faculties of Forestry and Land and Food Systems.
  3. (a) Students with a general interest in statistics; (b) Access Studies students improving addressing deficiencies in their academic backgrounds prior to professional registration; and (c) students in natural resource programs at other universities interested in completing a good, introductory applied statistics course.

Course Content

  • Lesson 1: Statistical Measures And Descriptions Of Data
  • Lesson 2: Probability
  • Lesson 3: Distribution Of Random Variables
  • Lesson 4: Discrete And Continuous Distributions
  • Lesson 5: Sampling Distributions
  • Lesson 6: Estimation Of Parameters
  • Lesson 7: Hypothesis Testing On Means And Proportions
  • Lesson 8: Tests Concerning Variances, Goodness-Of-Fit, And
 Independence
  • Lesson 9: Analysis Of Variance
  • Lesson 10: Simple Linear Regression

Evaluation

  • Quiz: To test the key concepts of each topic in the lessons a number of quizzes should be answered on Canvas. Check the Canvas page whether you have access to those quizzes. Your instructor will post a practice quiz in the first week of the course.
  • Assignments: There will be five assignments that could be completed after reading the associated book chapters and lessons. The examples are very useful and the instructor will use the examples while discussing any topic with students. The due date for delivering lab assignments is mentioned on Canvas unless otherwise announced.
  • Midterm Exam: The midterm exam syllabus covers “Lesson 1” to “Lesson 4”: i) Statistical Measures and Descriptions of Data of this course; ii) Probability; and iii) Distribution Of Random Variables.
  • Final Exam: In order to write the final examination, all other components must be completed within due dates and submitted to the instructor for marking. The final exam syllabus covers all topics of this course.

In this course, students have to write the final and midterm exams online (from home) using Proctorio (an online remote proctoring service); this online invigilation tool monitors students activities during the online exam. Contact your instructor if you have any questions about using Proctorio. Proctorio will record your webcam, your computer screen, or other actions during the assessment session and share that information with your instructor. Some assignments and practice exam are designed on Canvas to let students get prepared ahead of time for this method of online exam.

Proctorio requires the use of the Google Chrome web browser on a laptop or desktop computer. You will need to use/install the Proctorio extension. All recordings made by the auto-proctoring service are stored securely within Canadian servers. In line with British Columbia’s Freedom of Information and Protection of Privacy Act, these recordings are kept for a year and a day. After this time, the recordings are deleted.

In this course, learning objectives will be assessed based on four components (assignment, quiz, midterm and final exam), with the marks distribution shown in the following table:

Component Weight
Assignment 25%
Quiz 10%
Midterm Exam 25%
Final Exam 40%

Textbook

  • Kozak, A., R.A. Kozak, C.L. Staudhammer and S.B.Watts, Introductory Probability and Statistics: Applications for Forestry & the Natural Sciences, CAB International, Wallingford, Oxfordshire, UK, reprint 2012

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