STATS522-19B (HAM)

Statistical Inference

30 Points

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

Staff

Edit Staff Content

Convenor(s)

Lecturer(s)

Administrator(s)

: rachael.foote@waikato.ac.nz

Placement Coordinator(s)

Tutor(s)

Student Representative(s)

Lab Technician(s)

Librarian(s)

: debby.dada@waikato.ac.nz

You can contact staff by:

  • Calling +64 7 838 4466 select option 1, then enter the extension.
  • Extensions starting with 4, 5, 9 or 3 can also be direct dialled:
    • For extensions starting with 4: dial +64 7 838 extension.
    • For extensions starting with 5: dial +64 7 858 extension.
    • For extensions starting with 9: dial +64 7 837 extension.
    • For extensions starting with 3: dial +64 7 2620 + the last 3 digits of the extension e.g. 3123 = +64 7 262 0123.
Edit Staff Content

Paper Description

Edit Paper Description Content
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.
Edit Paper Description Content

Paper Structure

Edit Paper Structure Content

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.

Participation

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.


Edit Paper Structure Content

Learning Outcomes

Edit Learning Outcomes Content

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:
Edit Learning Outcomes Content
Edit Learning Outcomes Content

Assessment

Edit Assessments Content
  • 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.
Edit Additional Assessment Information Content

Assessment Components

Edit Assessments Content

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
33
  • Hand-in: In Lecture
2. Assignment 2
17 Sep 2019
1:00 PM
33
3. Assignment 3
15 Oct 2019
1:00 PM
34
Assessment Total:     100    
Failing to complete a compulsory assessment component of a paper will result in an IC grade
Edit Assessments Content

Required and Recommended Readings

Edit Required Readings Content

Recommended Readings

Edit Recommended Readings Content

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.

Edit Recommended Readings Content

Online Support

Edit Online Support Content
Moodle
Edit Online Support Content

Workload

Edit Workload Content
Students should expect to spend around 15 hours per week on this paper, including 3 contact hours
Edit Workload Content

Linkages to Other Papers

Edit Linkages Content

Prerequisite(s)

Admission is at the discretion of the Chairperson of Department.

Corequisite(s)

Equivalent(s)

Restriction(s)

Restricted papers: STAT422, STAT522

Edit Linkages Content