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STAT306 – Statistical Inference

2017 – S1 Day

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff Unit Convenor
Barry Quinn
Contact via barry.quinn@mq.edu.au
E7A TBA
TBA
Lecturer
Thomas Fung
Contact via thomas.fung@mq.edu.au
E7A TBA
TBA
Credit points Credit points
3
Prerequisites Prerequisites
6cp at 200 level including (STAT272 or STAT273)
Corequisites Corequisites
Co-badged status Co-badged status
Unit description Unit description
This unit provides an introduction to likelihood-based statistical inference. After a brief discussion of the multivariable calculus concepts needed, students will study (multivariate) change of variable, the likelihood function and maximum likelihood estimation, using examples of distributions from STAT272 and STAT273. The theory of estimation and hypothesis testing will be discussed, including most powerful tests, large sample theory, the sufficiency principle, the likelihood ratio principle, and sequential probability ratio tests.

Important Academic Dates

Information about important academic dates including deadlines for withdrawing from units are available at http://students.mq.edu.au/student_admin/enrolmentguide/academicdates/

Learning Outcomes

  1. Be able to understand that there are theoretical reasons why various estimators and tests are used.
  2. Be familiar with the derivations of estimators and tests.
  3. Be able to derive estimators and their theoretical properties.
  4. Be able to generate tests for various statistical hypotheses.

Assessment Tasks

Name Weighting Due
Assignment 1 10% 17th March
Assignment 2 10% 11th April
Assignment 3 10% 23rd May
Tutorial Participation 10% Weeks 2 to 13
Final Exam 60% TBA

Assignment 1

Due: 17th March
Weighting: 10%

Submit to your lecturer by 4pm on the due date. There is no “group work” assessment in this unit. All work is to be the student’s own. No extensions will be granted.  Students who have not submitted the assignment prior to the deadline will be awarded a mark of 0 for the assignment, except for cases in which an application for disruption to studies is made and approved.


This Assessment Task relates to the following Learning Outcomes:
  • Be able to understand that there are theoretical reasons why various estimators and tests are used.
  • Be familiar with the derivations of estimators and tests.
  • Be able to derive estimators and their theoretical properties.

Assignment 2

Due: 11th April
Weighting: 10%

Submit to your lecturer by 4pm on the due date. There is no “group work” assessment in this unit. All work is to be the student’s own. No extensions will be granted.  Students who have not submitted the assignment prior to the deadline will be awarded a mark of 0 for the assignment, except for cases in which an application for disruption to studies is made and approved.


This Assessment Task relates to the following Learning Outcomes:
  • Be able to understand that there are theoretical reasons why various estimators and tests are used.
  • Be familiar with the derivations of estimators and tests.
  • Be able to derive estimators and their theoretical properties.
  • Be able to generate tests for various statistical hypotheses.

Assignment 3

Due: 23rd May
Weighting: 10%

Submit to your lecturer by 4pm on the due date. There is no “group work” assessment in this unit. All work is to be the student’s own. No extensions will be granted.  Students who have not submitted the assignment prior to the deadline will be awarded a mark of 0 for the assignment, except for cases in which an application for disruption to studies is made and approved.


This Assessment Task relates to the following Learning Outcomes:
  • Be able to understand that there are theoretical reasons why various estimators and tests are used.
  • Be familiar with the derivations of estimators and tests.
  • Be able to derive estimators and their theoretical properties.
  • Be able to generate tests for various statistical hypotheses.

Tutorial Participation

Due: Weeks 2 to 13
Weighting: 10%

Students will contribute to discussions and hand in at least one handwritten page of tutorial problem solutions per tutorial.


This Assessment Task relates to the following Learning Outcomes:
  • Be able to understand that there are theoretical reasons why various estimators and tests are used.
  • Be familiar with the derivations of estimators and tests.
  • Be able to derive estimators and their theoretical properties.
  • Be able to generate tests for various statistical hypotheses.

Final Exam

Due: TBA
Weighting: 60%

The final Examination will be held during the mid-year Examination period. The final Examination is 3 hours long (with an additional 10 minutes’ reading time). It will cover all topics in the unit. The final examination is closed book. Students may take into the final Exam TWO A4 pages of notes handwritten (not typed) on BOTH sides. Calculators will be needed but must not be of the text/programmable type.

The University Examination timetable will be available in Draft form approximately 8 weeks before the commencement of the examinations and in Final form approximately 4 weeks before the commencement of the examinations at: http://www.timetables.mq.edu.au/exam

The only exception to not sitting an examination on the designated date is because of documented illness or unavoidable disruption. In these circumstances you may wish to consider applying for disruption to studies.

If you notify the University of your disruption to studies for your final examination, you must make yourself available for the week of July 24 – 28, 2017.  If you are not available at that time, there is no guarantee an additional examination time will be offered. Specific examination dates and times will be determined at a later date.


This Assessment Task relates to the following Learning Outcomes:
  • Be able to understand that there are theoretical reasons why various estimators and tests are used.
  • Be familiar with the derivations of estimators and tests.
  • Be able to derive estimators and their theoretical properties.
  • Be able to generate tests for various statistical hypotheses.

Delivery and Resources

There are four contact hours per week, comprised of three lectures and one tutorial. Check the timetable for classes.

Please consult iLearn or the Unit webpage for details of consultation hours. 

Technologies used and required

Lecture material will be placed on iLearn.

Students will need to use a calculator for the final examination and some of the other assessments.

Unit Schedule

Topic Material covered
1 Probability, expectation, change of variable, moment generating functions, multivariate distributions, conditional expectation.
2 Estimation, the likelihood function, the maximum likelihood principle, properties of estimators, asymptotic properties of maximum likelihood estimators, the Cramér-Rao lower bound.
3 Statistics, sufficient statistics, completeness, minimum variance unbiased estimators, Rao-Blackwell theorem.
4 Hypothesis testing: simple, composite hypotheses, the Neyman-Pearson lemma, asymptotic properties.
5 The Sequential Probability Ratio Test
6 Confidence intervals and regions

Learning and Teaching Activities

Lecture

Three hours a week.

Tutorial

One hour a week.

Policies and Procedures

Macquarie University policies and procedures are accessible from Policy Central. Students should be aware of the following policies in particular with regard to Learning and Teaching:

Academic Honesty Policy http://mq.edu.au/policy/docs/academic_honesty/policy.html

Assessment Policy http://mq.edu.au/policy/docs/assessment/policy_2016.html

Grade Appeal Policy http://mq.edu.au/policy/docs/gradeappeal/policy.html

Complaint Management Procedure for Students and Members of the Public http://www.mq.edu.au/policy/docs/complaint_management/procedure.html​

Disruption to Studies Policy http://www.mq.edu.au/policy/docs/disruption_studies/policy.html The Disruption to Studies Policy is effective from March 3 2014 and replaces the Special Consideration Policy.

In addition, a number of other policies can be found in the Learning and Teaching Category of Policy Central.

Student Code of Conduct

Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/support/student_conduct/

Results

Results shown in iLearn, or released directly by your Unit Convenor, are not confirmed as they are subject to final approval by the University. Once approved, final results will be sent to your student email address and will be made available in eStudent. For more information visit ask.mq.edu.au.

Grading Policy http://www.mq.edu.au/policy/docs/grading/policy.html

Student Support

Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/

Learning Skills

Learning Skills (mq.edu.au/learningskills) provides academic writing resources and study strategies to improve your marks and take control of your study.

Student Enquiry Service

For all student enquiries, visit Student Connect at ask.mq.edu.au

Equity Support

Students with a disability are encouraged to contact the Disability Service who can provide appropriate help with any issues that arise during their studies.

IT Help

For help with University computer systems and technology, visit http://www.mq.edu.au/about_us/offices_and_units/information_technology/help/

When using the University's IT, you must adhere to the Acceptable Use of IT Resources Policy. The policy applies to all who connect to the MQ network including students.

Graduate Capabilities

Critical, Analytical and Integrative Thinking

We want our graduates to be capable of reasoning, questioning and analysing, and to integrate and synthesise learning and knowledge from a range of sources and environments; to be able to critique constraints, assumptions and limitations; to be able to think independently and systemically in relation to scholarly activity, in the workplace, and in the world. We want them to have a level of scientific and information technology literacy.

This graduate capability is supported by:

Learning outcomes

  • Be familiar with the derivations of estimators and tests.
  • Be able to derive estimators and their theoretical properties.
  • Be able to generate tests for various statistical hypotheses.

Assessment tasks

  • Assignment 1
  • Assignment 2
  • Assignment 3
  • Tutorial Participation
  • Final Exam

Learning and teaching activities

  • Three hours a week.
  • One hour a week.

Effective Communication

We want to develop in our students the ability to communicate and convey their views in forms effective with different audiences. We want our graduates to take with them the capability to read, listen, question, gather and evaluate information resources in a variety of formats, assess, write clearly, speak effectively, and to use visual communication and communication technologies as appropriate.

This graduate capability is supported by:

Assessment tasks

  • Tutorial Participation
  • Final Exam

Learning and teaching activities

  • Three hours a week.
  • One hour a week.

Discipline Specific Knowledge and Skills

Our graduates will take with them the intellectual development, depth and breadth of knowledge, scholarly understanding, and specific subject content in their chosen fields to make them competent and confident in their subject or profession. They will be able to demonstrate, where relevant, professional technical competence and meet professional standards. They will be able to articulate the structure of knowledge of their discipline, be able to adapt discipline-specific knowledge to novel situations, and be able to contribute from their discipline to inter-disciplinary solutions to problems.

This graduate capability is supported by:

Learning outcomes

  • Be able to understand that there are theoretical reasons why various estimators and tests are used.
  • Be familiar with the derivations of estimators and tests.
  • Be able to derive estimators and their theoretical properties.
  • Be able to generate tests for various statistical hypotheses.

Assessment tasks

  • Assignment 1
  • Assignment 2
  • Assignment 3
  • Tutorial Participation
  • Final Exam

Learning and teaching activities

  • Three hours a week.
  • One hour a week.

Problem Solving and Research Capability

Our graduates should be capable of researching; of analysing, and interpreting and assessing data and information in various forms; of drawing connections across fields of knowledge; and they should be able to relate their knowledge to complex situations at work or in the world, in order to diagnose and solve problems. We want them to have the confidence to take the initiative in doing so, within an awareness of their own limitations.

This graduate capability is supported by:

Learning outcomes

  • Be familiar with the derivations of estimators and tests.
  • Be able to derive estimators and their theoretical properties.
  • Be able to generate tests for various statistical hypotheses.

Assessment tasks

  • Assignment 1
  • Assignment 2
  • Assignment 3
  • Tutorial Participation
  • Final Exam

Learning and teaching activities

  • Three hours a week.
  • One hour a week.

Textbooks and other reference material

There is no prescribed textbook for the Unit. Any book with a title such as “Introduction to Mathematical Statistics” will be suitable as a reference. The reference for STAT273, Wackerly, D., Mendenhall W., and Scheaffer, R.L. Mathematical Statistics with Applications (4th, 5th or 6th Editions), would be useful. The lecture notes will be extensive and fairly self-contained.

Changes since First Published

Date Description
02/02/2017 Added the requested info on supplementary exams - Anne Macmillan 27 January.