Courses & Units

Agrifood Research Methods KLA201

Introduction

This unit is about modern statistics, data-science and critical thinking within the context of agri-food research. Students learn the following things. Firstly, to combine statistical analysis, probability reasoning and substantive scientific hypotheses. Secondly, to use a computer to properly analyse data and present empirical results (which entails going beyond Microsoft Excel). Thirdly, to make justifiable and reproducible inferences using background knowledge and real agri-food data from TIAs current research. Fourthly, to plan for the coherent structuring and analysis of data in their own future agri-food studies and industry-based careers.

Summary

Unit name Agrifood Research Methods
Unit code KLA201
Credit points 12.5
College/School College of Sciences and Engineering
Tasmanian Institute of Agriculture
Discipline Agriculture and Food Systems
Coordinator Doctor Ian Hunt
Available as an elective? Yes
Delivered By University of Tasmania

Availability

Location Study period Attendance options Available to
Hobart Semester 1 On-Campus International Domestic

Key

On-campus
Off-Campus
International students
Domestic students
Note

Please check that your computer meets the minimum System Requirements if you are attending via Distance/Off-Campus.

Units are offered in attending mode unless otherwise indicated (that is attendance is required at the campus identified). A unit identified as offered by distance, that is there is no requirement for attendance, is identified with a nominal enrolment campus. A unit offered to both attending students and by distance from the same campus is identified as having both modes of study.

Key Dates

Study Period Start date Census date WW date End date
Semester 1 20/2/2023 21/3/2023 10/4/2023 28/5/2023

* The Final WW Date is the final date from which you can withdraw from the unit without academic penalty, however you will still incur a financial liability (refer to How do I withdraw from a unit? for more information).

Unit census dates currently displaying for 2023 are indicative and subject to change. Finalised census dates for 2023 will be available from the 1st October 2022. Note census date cutoff is 11.59pm AEST (AEDT during October to March).

About Census Dates

Learning Outcomes

  • Analyse agri-food sample sets using a computer.
  • Explain the link between statistical analysis and applied probability reasoning.
  • Make justifiable and reproducible inferences by combining data and background knowledge from real agri-food research.
  • Design, implement and communicate empirical investigations used in agri-food studies and real-world applications.
Field of Education Commencing Student Contribution 1 Grandfathered Student Contribution 1 Approved Pathway Course Student Contribution 2 Domestic Full Fee
$515.00 $515.00 not applicable $2,922.00

1 Please refer to more information on student contribution amounts.
2 Please refer to more information on eligibility and Approved Pathway courses.
3 Please refer to more information on eligibility for HECS-HELP.
4 Please refer to more information on eligibility for FEE-HELP.

If you have any questions in relation to the fees, please contact UConnect or more information is available on StudyAssist.

Please note: international students should refer to What is an indicative Fee? to get an indicative course cost.

Teaching

Teaching Pattern

Two hours of online lectures per week and two hours of in-person tutorials per week.

AssessmentGetting a handle on R (15%)|Summary of text-book chapter. (5%)|Statistical inference, probability and agri-food samples (20%)|Real data analysis and result communication (20%)|Final Exam (40%)
TimetableView the lecture timetable | View the full unit timetable

Textbooks

Required

"The Art of Statistics: Learning from Data" by David Spiegelhalter.  One chapter per week is required to be read from this book.  The contents of this book set the pace for the whole course. 

LinksBooktopia textbook finder

The University reserves the right to amend or remove courses and unit availabilities, as appropriate.