UNIV*6020 Applied Ag Stats

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The following description is for the course offering in Fall 2021 and is subject to change. It is provided for information only. The course outline distributed to the class at the beginning of the semester describes the course content and delivery, and defines the methods and criteria to be used in establishing the final grades for the course.

Experimental Design and Applied Data Analysis for the Agricultural Sciences (Applied Ag Stats)

This course focuses on statistical principles, experimental designs, and communication of findings to research peers within the agricultural field. Students apply statistical techniques and perform data analyses.

This course will be offered as a set of modules, with each module lasting 1-2 weeks. The course will be offered in the F and W semesters, with mandatory modules offered in the Fall semester, and optional modules at the end of the Fall and into the Winter semesters. The goal of this modular course is to offer flexibility to students and to provide a base knowledge of applied data analyses so that departments and faculty members can teach more advanced and specialized courses.

Students, with their advisors, must select the required number of modules, to provide 12 weeks of instruction in total, to obtain credit for the course. There are 4 modules that are MANDATORY – these will cover a review of the classical statistical inference tests, including t-test, F-test, type I and II errors, confidence intervals, a discussion on p-values; the basics of experimental designs, and regression analyses. Optional topics will include an introduction to Meta-Analysis, an introduction to Exploratory Statistics, additional experimental designs, and other topics. Additional modules may be added to meet specific needs as they arise, with faculty collaboration.

Please note that examples used throughout the course will be from the agricultural and natural science fields, to cover topic areas studied in OAC


Teaching Assistant:

Credit Weight:


Course Level:

  • Graduate

Academic Department (or campus):

Department of Plant Agriculture



Semester Offering:

  • Fall
  • Winter

Class Schedule and Location:


Monday 1:30pm - 2:20pm TEAMS until Sept 28 RICH 2529
Wednesday 1:30pm - 2:20pm TEAMS until Sept 28 RICH 2529
Friday 1:30pm - 2:20pm TEAMS until Sept 28 RICH 2529


Section 01: Tuesday 12:30pm - 2:20pm TEAMS until Sept 28 ROZ 105
Section 02: Tuesday 08:30am - 10:20am TEAMS until Sept 28 MCLN 107

Please refer to WebAdvisor for class schedule and location.

Learning outcomes:

By the end of this course, you should be able to:

1. Select and differentiate among classical inference statistical tests
2. Choose an appropriate experimental design to match research goals and available resources
3. Identify and analyze statistical models that match experimental designs and/or research goals
4. Critique, evaluate, and defend data analyses and research outputs to their peers
5. Apply research data management skills to their own research.

Lecture Content:

F21 Weeks 1-8

  • Mandatory Modules
  • Topics will range from review of statistical tests to partitioning of variation to experimental designs.

F21 Weeks 9-12 AND W22 Weeks 1-6

  • Optional Modules
  • Variety of Ag Stats topics with a mix of OAC faculty Guest lectures

W22 Weeks 7-12

  • Final Project Presentations
Labs & Seminars:


Section 01: Tuesday 12:30pm - 2:20pm TEAMS until Sept 28 ROZ 105
Section 02: Tuesday 08:30am - 10:20am TEAMS until Sept 28 MCLN 107

Course Assignments and Tests:

Data Analysis Assignment #1 (10%)
Data Analysis Assignment #2 (10%)
Data Analysis Assignment #3 (10%)


OPTIONAL MODULE #1 Data Analysis Assignment (5%)
OPTIONAL MODULE #2 Data Analysis Assignment (5%)
OPTIONAL MODULE #3 Data Analysis Assignment (5%)


On-line quiz 1 (10%)
On-line quiz 2 (5%)
On-line quiz 3 (5%)
On-line quiz 4 (5%)


Final Project Report and Presentation (30%)

To receive an "Audit" from this course, you are required to

  • complete each quiz
  • submit two data analysis reports

Final examination:

There is no final examination scheduled for this course.

Course Resources:

Recommended Texts:
Bowley, S.R. 2015. A hitchhiker’s guide to statistics in biology. Generalized Linear Mixed Model Edition. Plants et al., Kincardine ON.  ISBN 978-0-9685500-4-5. A limited number of copies will be in the bookstore.
Kuehl, R.O. 2000. Design of Experiments: Statistical Principles of Research Design and Analysis. 2nd edition.  Duxbury Press.  CA, USA. 
Both texts are available on Reserve in the Library
Lab Manual:
Not applicable.
Other Resources:
CourseLink will be used to distribute assignments, data files, information, and other materials associated with the course. You will also use this to submit the pdf copy of your SAS/R analyses for the graded reports. For each MANDATORY module, there will be three to five Self-Assessment questions available for you to assess your understanding of the material (select “Self Assessment” from the menu ribbon). There are also four graded quizzes during the course; these will be conducted on-line via CourseLink.
Marks will be collated in CourseLink throughout the course. It is your responsibility to review these grades and report if there are any discrepancies. Keep paper and/or other reliable back-up copies of all in- and out-of-class assignments: You may be asked to resubmit work at any time.
Statistical Computing Software:
To perform the statistical analyses in this course you will be required to use SAS or R. You will only need 1 of the 2 packages.
SAS: There are 2 options available. The licensed version available through CCS at a cost and ONLY available to PC users. The second option is to create an account on the SAS OnDemand Service
R: You will need to download and install both R and RStudio.

Course Policies:

There is a ban on the use of writing services and statistical analysis services for all reports submitted in this course.
Quizzes will be available on-line through CourseLink between the time-period: 6 pm on the last day of the Module to 11:59 pm 2 week days later. To receive a mark, you must complete the quiz during this time interval. You are allowed up to two attempts to write each quiz. Your
recorded grade will be the average of all attempts. You do not require access to SAS or R in order to complete the quiz. 
The data analysis reports must be submitted electronically as PDF files via the CourseLink Dropbox by 8:30 am on the due date. To be submitted electronically via the CourseLink Dropbox by 8:30am on the due date. Late reports will not be accepted after the dropbox closes and these will receive a 0 grade.
If a due date for an assignment / quiz conflicts with a scheduled academic activity such as attendance at a conference, an activity involving a research project, or personal issues such as medical procedures, jury duty, etc., email me ahead of time so we can make alternate
It is anticipated that you will work on the assignments in study groups, however, the assignments and reports you submit for grading must be independent and document only your work.
Plagiarism will be strongly suspected if two or more students submit individual projects that have identical or substantially identical components. Tables, figures, paragraphs in reports should reflect your own efforts, not be copies or essential derivatives of work
performed by someone else. See “Academic Misconduct” below for the procedures that are followed if this arises.
Electronic recording of classes is expressly forbidden without the prior consent of the Instructor. This prohibition extends to all components of the course, including, but not limited to, lectures, seminars, and lab instruction. When recordings are permitted they are solely for the use of the authorized student and may not be reproduced, or transmitted to others, without the express written consent of the Instructor. I will record both classes and make these available through the University of Guelph Streaming service.
Tables and figures should be suitable for submission to one of the refereed journals of the Agricultural Institute of Canada (AIC) such as the Can. J. Plant Sci. or the Can. J. Anim. Sci. The citation format is Council of Science Editors (CSE) Name-year. Title, figures, tables and
footnotes (if any) should be self-explanatory so that one can interpret what is being presented without one having to refer to the methods/results. Titles need to be brief but sufficiently detailed to explain the data and statistical analysis. 
See pages 7 through 10, and 12 of the style guide pdf. Don’t rely on a current article as a reference guide for the tables to submit to UNIV*6020. Focus on this phrase: Figures and table should be self-explanatory so that one can interpret what is being presented without having to refer to the methods/results. Unfortunately, many published papers contain tables that have lots of wasted real estate in which one could incorporate one or two words or phrases, or a brief footnote to provide greater clarity, especially related to the statistics.
Figures & tables are to be suitable for submission for review of a manuscript. There are two notable exceptions for the assignments for this course: 1) Do not turn on line numbering; and, 2) Place the title of a Figure on the same page as the figure image – do not place the Figure title on a separate page (as you would typically do for a manuscript).

Other Course Information:

University Policies

Academic Consideration

When you find yourself unable to meet an in-course requirement because of illness or compassionate reasons, please advise the course instructor in writing, with your name, id#, and e-mail contact. See the academic calendar for information on regulations and procedures for Academic Consideration:

Academic Misconduct

The University of Guelph is committed to upholding the highest standards of academic integrity and it is the responsibility of all members of the University community, faculty, staff, and students  to be aware of what constitutes academic misconduct and to do as much as possible to prevent academic offences from occurring.

University of Guelph students have the responsibility of abiding by the University's policy on academic misconduct regardless of their location of study; faculty, staff and students have the responsibility of supporting an environment that discourages misconduct. Students need to remain aware that instructors have access to and the right to use electronic and other means of detection. Please note: Whether or not a student intended to commit academic misconduct is not relevant for a finding of guilt. Hurried or careless submission of assignments does not excuse students from responsibility for verifying the academic integrity of their work before submitting it. Students who are in any doubt as to whether an action on their part could be construed as an academic offence should consult with a faculty member or faculty advisor.

The Academic Misconduct Policy is detailed in the University Calenders:


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