| Semester 1, 2022 Online | |
| Units : | 1 |
| Faculty or Section : | Faculty of Health, Engineering and Sciences |
| School or Department : | School of Mathematics, Physics & Computing |
| Grading basis : | Graded |
| Course fee schedule : | /current-students/administration/fees/fee-schedules |
Staffing
Examiner:
Overview
This course provides flexibility in honours and postgraduate programs to cater for the widely varying interests and chosen specialisations of students. Statisticians need to be proficient in a wide range of statistical concepts and techniques. Many of these are either only touched on or omitted from undergraduate programs. An opportunity to broaden the students' knowledge-base with more advanced statistical techniques is provided in this course.
This course provides the opportunity for a student to pursue an area of study that will complement the other studies in the student's program. Typically, the course will consist of specialised investigations extending knowledge and skills in one of the areas listed in the Topics section below, or another Topic where appropriate and where a supervisor is available.
Students should nominate the topic they wish to study and then email the Course Examiner to enquire whether the topic and a suitable supervisor will be available in their semester of study, and for formal approval to enrol. As only one of the listed topics is chosen by each student the content of the course may vary from student to student. The weighting of the sub-topics within this unit depends on the topic chosen and will be discussed with the supervisor.
Course learning outcomes
On completion of this course students should be able to
- apply relevant advanced knowledge and skills in the study area chosen.
Topics
| Description | Weighting(%) | |
|---|---|---|
| 1. |
Survey Design and Analysis: this topic covers the principles and practice of designing surveys, and the analysis of data from them. This includes: Questionnaire design, Measurement: types of data, measurement scales, reliability; missing data, data cleaning and analysis of categorical and ordinal data. Time Series Analysis: This course consists of advanced studies in time series analysis. Topics will include: identification, estimation, testing and forecasting for univariate and multivariate models of time series; the spectral representation of a time series; non-linear models, including identification, estimation, testing and forecasting; cointegrated models. |
100.00 |
Text and materials required to be purchased or accessed
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Student workload expectations
To do well in this subject, students are expected to commit approximately 10 hours per week including class contact hours, independent study, and all assessment tasks. If you are undertaking additional activities, which may include placements and residential schools, the weekly workload hours may vary.
Assessment details
| Description | Group Assessment |
Weighting (%) | Course learning outcomes |
|---|---|---|---|
| Problem Solving 1 | No | 50 | 1 |
| Problem Solving 2 | No | 50 | 1 |