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.
|Unit name||Agrifood Research Methods|
|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|
|Location||Study period||Attendance options||Available to|
- International students
- Domestic students
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|Study Period||Start date||Census date||WW date||End date|
* 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).
- 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|
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.
Please note: international students should refer to What is an indicative Fee? to get an indicative course cost.
Two hours of online lectures per week and two hours of in-person tutorials per week.
|Assessment||Getting 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%)|
|Timetable||View the lecture timetable | View the full unit timetable|
"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.
|Links||Booktopia textbook finder|
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