STATS522-19B (HAM)

Statistical Inference

30 Points

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Division of Health Engineering Computing & Science
School of Computing and Mathematical Sciences
Department of Mathematics and Statistics


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Paper Description

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Statistical inference will be considered from both the classical and Bayesian perspectives. It covers maximum likelihood estimation, the properties of estimators, confidence intervals, and hypothesis tests. Bayes’ theorem is used to revise beliefs about the parameters given the data.
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Paper Structure

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The methods of instruction will include lecturing, self-study, discussions and co-operative learning. In addition to directed reading, students will be expected to find additional material on the topic (books in the library, internet, etc) by themselves. Lecturers will be available (by appointment) for guidance or help.


As a practising statistician it is important to be able to give constructive criticism or to explain what one knows to others, therefore as part of this paper students may be asked to take part in discussions or short presentations. Students will be expected to critically evaluate topics e.g. by posing questions, making comments, and/or examining examples or framing thought experiments during these presentations and discussions, although there will be no formal assessment or course credit for such participation.

In order to develop a fuller understanding of statistical inference, students are also encouraged to initiate discussions and to question and critique aspects of statistical methodology under consideration (and the assumptions behind them) both by themselves and during contact hours.

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Learning Outcomes

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Students who successfully complete the course should be able to:

  • A student who successfully completes this paper is expected to be able to:
    • Derive and implement statistical inference on a standard model using the methods of inference taught in this course.
    • Appreciate the strengths and the possible pitfalls of the various approaches and decide the appropriateness of a particular approach to a given problem.
    • Understand and appreciate core advanced theoretical concepts in Statistics.
    Linked to the following assessments:
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  • There are three assignments to be completed, each worth 1/3 of the final grade.
  • Due dates are approximate and may change depending on progress with the course material.
  • Assignments are to be handed to the lecturer at the start of the lecture on the day they are due.
  • Expected turnaround for marking is no more than two weeks.
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Assessment Components

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The internal assessment/exam ratio (as stated in the University Calendar) is 100:0. There is no final exam. The final exam makes up 0% of the overall mark.

The internal assessment/exam ratio (as stated in the University Calendar) is 100:0 or 0:0, whichever is more favourable for the student. The final exam makes up either 0% or 0% of the overall mark.

Component DescriptionDue Date TimePercentage of overall markSubmission MethodCompulsory
1. Assignment 1
13 Aug 2019
1:00 PM
  • Hand-in: In Lecture
2. Assignment 2
17 Sep 2019
1:00 PM
3. Assignment 3
15 Oct 2019
1:00 PM
Assessment Total:     100    
Failing to complete a compulsory assessment component of a paper will result in an IC grade
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Required and Recommended Readings

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Recommended Readings

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Probability and Random Processes - Grimmett and Stirzaker, Third edition, Oxford University Press.

Multivariate Analysis - Mardia, Kent and Bibby, Academic Press.

We will dip into these texts from time to time, but they are not mandatory and the appropriate material will be covered in lectures. Both are available from the university library.

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Online Support

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Students should expect to spend around 15 hours per week on this paper, including 3 contact hours
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Linkages to Other Papers

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Admission is at the discretion of the Chairperson of Department.




Restricted papers: STAT422, STAT522

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