Hobart
Introduction
Quantitative skills are fundamental to many areas of biology and ecology. With massive increases in the availability of ecological and other kinds of biological data, skill in developing models to describe and understand relationships in data and extrapolate beyond existing data, and an understanding of the nuances of particular kinds of data, is fundamental in modern biological research. This unit builds on the foundation provided in Quantitative Methods in Biology to familiarise students with more advanced model building techniques including generalised linear, mixed and additive models, regression trees, bootstrapping methods, and non-linear regression. The unit will also introduce time series analysis, Bayesian approaches to modelling data, and simulation models including explicitly spatial and individual-based models. It will equip students wanting to understand and/or pursue modern and relevant research in biology and ecology.
Summary 2021
| Unit name | Modelling Biological Data |
|---|---|
| Unit code | KSM304 |
| Credit points | 12.5 |
| Faculty/School | College of Sciences and Engineering Institute for Marine & Antarctic Studies |
| Discipline | College Office - CSE|Ecology and Biodiversity |
| Coordinator | Simon Wotherspoon |
| Teaching staff | |
| Level | Advanced |
| Available as student elective? | Yes |
| Breadth Unit? | No |
Availability
Note
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* 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 (see withdrawal dates explained for more information).
Fees
Requisites
Prerequisites
Co-requisites
Mutual Exclusions
You cannot enrol in this unit as well as the following:
Teaching
| Teaching Pattern | 1 semester, 2 x 1 hr lectures and 4 hr practical weekly (13 weeks) |
|---|---|
| Assessment | Minor assignment (10%), 2 x major assignment (20% each), 3 hr open-book exam (50%) |
| Timetable | View the lecture timetable | View the full unit timetable |
Textbooks
| Required | |
|---|---|
| Recommended |
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