Introduction
Quantitative Methods in Biology builds on second-year biological data analysis units by deepening and extending your knowledge, understanding and application of common statistical approaches used to analyse a range of biological datasets. Topics covered include: more advanced Analysis of Variance (ANOVA), linear regression and mixed effects models; Generalised Linear Models; and multivariate data visualisation and analysis. Through hands-on application, you will develop the ability to analyse and interpret complex biological data, draw insights, make informed conclusions and communicate your results and their implications. You will also enhance your proficiency in the statistical computing language R and its integrated learning environment, RStudio. This unit offers essential skills for biological and ecological research, benefiting anyone who works with complex biological data or will interpret analysis outputs. This unit is a core unit for the Marine Biology major within the Bachelor of Marine and Antarctic Studies.
Summary
| Unit name | Quantitative Methods in Biology |
| Unit code | KSM309 |
| Credit points | 12.5 |
| College/School | Sciences and Engineering Institute for Marine & Antarctic Studies |
| Discipline | Office of Sciences and Engineering|Ecology and Biodiversity |
| Coordinator | Associate Professor Nicole Hill |
| Available as an elective? | Yes |
| Delivered By | University of Tasmania |
| Level | Advanced |
Sustainable Development Goals
The Unit Coordinator has identified that this unit aligns with the following UN Sustainable Development Goals. We welcome your thoughts and feedback on the alignment of the unit with these goals.
Availability
Specific information on 2027 unit availability will be available in August
Learning Outcomes
- Identify and discuss robust experimental designs for analysing biological and ecological data.
- Critically evaluate and apply appropriate statistical analyses to address complex biological and ecological research questions.
- Implement statistical methods using the statistical software language R.
- Interpret statistical analysis outputs in the context of the original scientific hypotheses.
- Communicate research questions, chosen statistical methods, results and their implementation to both technical and non-technical audiences.
Fee Information
2027 fee information will be available in August.
Requisites
Prerequisites
(JFA207 OR KMA253) AND (KSM202 OR KZA161 OR KPZ163)Teaching
| Teaching Pattern | Up to 1.5-hr online lecture material, 2-hr face to face tutorial, 3-hr face to face practical weekly |
|---|---|
| Assessment | Data analysis and Report (20%)|In-person Quizzes (20%)|Analysis Assignment (30%)|Open book exam (30%) |
| Timetable | View the lecture timetable | View the full unit timetable |
Textbooks
| Required |
There is no compulsory reading for the unit.
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| Recommended | The recommended text is Quinn & Keough (2023), but it is not an essential requirement for the unit: Quinn GP, Keough MK (2023) Experimental design and data analysis for biologists. 2nd Edition. Cambridge Univ. Press, UK. Note there have been some substantial updates between the first and second editions of this text. There is no prescribed reading list. Useful texts and papers will be added to relevant weeks' website on MyLO.
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