This course involves on campus teaching. For students unable to come to campus there will be a remote option. See the Class Summary for more details.
This course introduces students to the basic theory behind the development and assessment of statistical analysis techniques in the areas of point and interval estimation, as well as hypothesis testing. Topics include:
* Point estimation methods, including method of moments and maximum likelihood, bias and variance, mean-squared error, sufficiency, completeness, exponential families, the Cramer-Rao inequality, the Rao-Blackwell theorem, uniformly minimum variance unbiased estimators, and Bayesian estimation methods.
* Confidence interval construction methods, including likelihood-based intervals, inversion methods, intervals based on pivots, Bayesian credible and highest posterior density regions, and resampling based intervals.
* Hypothesis testing methods, including likelihood ratio tests, the Neymann-Pearson lemma and uniformly most powerful tests, power calculations, Bayesian approaches, and non-parametric approaches.
Upon successful completion, students will have the knowledge and skills to:
- Explain in detail the notion of a parametric model and point estimation of the parameters of those models.
- Explain in detail and demonstrate approaches to include a measure of accuracy for estimation procedures and our confidence in them by examining the area of interval estimation.
- Demonstrate the plausibility of pre-specified ideas about the parameters of the model by examining the area of hypothesis testing.
- Explain in detail and demonstrate the use of non-parametric statistical methods.
- Demonstrate in detail computational skills to implement various statistical inferential approaches.
- Typical assessment may include, but is not restricted to: exams, assignments, quizzes, presentations and other assessment as appropriate (100) [LO 1,2,3,4,5]
The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.
Students are expected to commit 130 hours of work in completing this course. This includes time spent in scheduled classes and self-directed study time.
Requisite and Incompatibility
You will need to contact the Rsch Sch of Finance, Actuarial Studies & App Stats to request a permission code to enrol in this course.
Information about the prescribed textbook will be available via the Class Summary.
Tuition fees are for the academic year indicated at the top of the page.
Commonwealth Support (CSP) Students
If you have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). More information about your student contribution amount for each course at Fees.
- Student Contribution Band:
- Unit value:
- 6 units
If you are a domestic graduate coursework student with a Domestic Tuition Fee (DTF) place or international student you will be required to pay course tuition fees (see below). Course tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
Where there is a unit range displayed for this course, not all unit options below may be available.
Offerings, Dates and Class Summary Links
ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
|Class number||Class start date||Last day to enrol||Census date||Class end date||Mode Of Delivery||Class Summary|
|2757||20 Feb 2023||27 Feb 2023||31 Mar 2023||26 May 2023||In Person||N/A|