6 points, SCA Band 2, 0.125 EFTSL
Postgraduate - Unit
Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered.
- First semester 2019 (On-campus)
Enrolment in the Master of Science
The unit will discuss some basic statistical methods for analysing climate dynamics with the aim of understanding the physical mechanisms driving the observed structures (statistics). The unit will start with a discussion on the basics of probability theory, time series analysis, stochastic models and multi-variate data (pattern) analysis. It will then focus on the principles of decision making in statistical analysis (significance tests), which is followed by a discussion of the pitfalls and general strategies in statistical analysis. The unit will not focus on deriving statistical parameters, but rather will emphasise how these methods can be applied and will discuss the potential pitfalls in interpreting statistical results.
On completion of this unit students will be able to:
- Complete a statistical analysis on probability distributions, time series, and multi-variate data.
- Apply standard statistical methods in climate dynamics data analysis.
- Interpret the outcomes of the statistical analysis in the context of climate dynamics.
- Read, understand and critically analyse the scientific literature on data analysis in climate dynamics.
Examination (2 hours): 50%
This unit is offered at both Level 4 and Level 5, differentiated by the level of the assessment. Students enrolled in EAE5020 will be expected to demonstrate a higher level of learning in this subject than those enrolled in. The assignments and exam in this unit will use some common items from the assessment tasks, in combination with several higher level questions and tasks.
A total of 12 hours per week comprising:
- Two 1-hour lectures per week
- One 1-hour laboratory per week
- 3 hours working on assignments
- 6 hours of independent study
See also Unit timetable information
This unit applies to the following area(s) of study
Master of Science in Atmospheric Science