35393 Seminar (Statistics)
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particular session, location and mode of offering is the authoritative source
of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.
Subject handbook information prior to 2024 is available in the Archives.
Credit points: 6 cp
Result type: Grade and marks
Requisite(s): 35252 Mathematical Statistics
These requisites may not apply to students in certain courses. See access conditions.
Description
The subject involves group studies in statistics. The topics vary from year to year and are chosen in accordance with the interests of students and staff, and the availability of staff.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Identify, estimate and then check time series models; |
|---|---|
| 2. | Use these models for forecasting; |
| 3. | Explain in simple terms the reasoning behind the methodology; |
| 4. | Apply the techniques on real and simulated data sets using Minitab and Splus/R; |
| 5. | Further studies in statistics. |
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the development of following course intended learning outcomes:
- Demonstrate theoretical and technical knowledge of mathematical sciences including calculus, discrete mathematics, linear algebra, probability, statistics and quantitative management. (1.1)
Contribution to the development of graduate attributes
This subject will contribute to the recognition of your attainment of the following Faculty of Science graduate attributes:
1. Disciplinary knowledge and its appropriate application
2. An Inquiry-oriented approach
3. Professional skills and their appropriate application
6. Communication skills
7. Initiative and innovative ability
Teaching and learning strategies
One 2-hour lecture in Weeks 1,2,4,5,7,8,11 and 13 and one 1-hour lecture and one 1-hour computing laboratory in Weeks 3,6,10,12
Content (topics)
This subject will cover topics selected from: probalistic graphs, Bayesian statistical inference, generalised linear models, longitudinal and multi-level data analysis.
Assessment
Assessment task 1: Labs
| Objective(s): | This assessment task contributes to the development of course intended learning outcome(s): 1.1 |
|---|---|
| Weight: | 12% |
| Length: | To be determined. |
| Criteria: | Correct use of terminology; correct choice and use of problem solving strategies and procedures |
Assessment task 2: Assignments
| Objective(s): | This assessment task contributes to the development of course intended learning outcome(s): 1.1 |
|---|---|
| Weight: | 28% |
| Length: | To be determined. |
| Criteria: | Correct use of terminology; correct choice and use of problem solving strategies and procedures; presentation in required format; careful reasoning |
Assessment task 3: Examination
| Objective(s): | This assessment task contributes to the development of course intended learning outcome(s): 1.1 |
|---|---|
| Weight: | 60% |
| Length: | Six questions. |
| Criteria: | Correct use of terminology; correct choice and use of problem solving strategies and procedures; careful reasoning |
Minimum requirements
Any assessment task worth 40% or more requires the student to gain at least 40% of the mark for that task. If 40% is not reached, an X grade fail may be awarded for the subject, irrespective of an overall mark greater than 50.
References
None.
Other resources
None.