Students
STAT710 – Statistical Theory
2018 – S1 Evening
General Information
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| Unit convenor and teaching staff |
Unit convenor and teaching staff
Unit Convenor
Ian Marschner
Contact via Email
Room 5.25, Level 5, 12 Wally's Walk (E7A)
10am Wed
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| Credit points |
Credit points
4
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| Prerequisites |
Prerequisites
Admission to MRes
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| Corequisites |
Corequisites
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| Co-badged status |
Co-badged status
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| Unit description |
Unit description
This unit introduces the fundamental principles of statistical inference and estimation theory. The unit begins with a discussion of probability concepts, including relative frequency, random variables, distributions and large sample theory. A discussion of estimation concepts is provided, particularly unbiasedness, consistency and efficiency. Likelihood theory is then developed, including the concept of sufficiency and the maximum likelihood approach to estimation. Hypothesis testing concepts and methods are discussed with a particular focus on likelihood ratio, score and Wald tests. An introduction to Bayesian inference principles is also provided.
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Important Academic Dates
Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates
Learning Outcomes
On successful completion of this unit, you will be able to:
- be familiar with probability concepts, including random variables and probability distributions for discrete, continuous and multivariate situtations and know how to apply these concepts in the context of statistical inference and sampling
- understand fundamental large sample concepts in probability, including modes of convergence and the central limit theorem and be able to apply these concepts to practical problems
- understand the principles and theory of estimation, including unbiasedness, consistency and relative efficiency, as well as the likelihood function and maximum likelihood estimation
- understand the principles and theory of statistical hypothesis testing, including likelihood ratio tests, score tests and Wald tests
- understand the principles of Bayesian inference
General Assessment Information
ASSIGNMENT SUBMISSION
Students must submit assignments via the iLearn website.
LATE SUBMISSION OF ASSIGNMENTS
In case of late submission of an assignment, if no special consideration has been granted, 10% of the earned mark will be deducted for each day that the assignment is late, up to a maximum of 50%. After 5 days, including weekends and public holidays, a mark of 0% will be awarded for the assignment.
SUPPLEMENTARY EXAMS
If you receive special consideration for the final exam, a supplementary exam will be scheduled in the interval between the regular exam period and the start of the next session. By making a special consideration application for the final exam you are declaring yourself available for a resit during the supplementary examination period and will not be eligible for a second special consideration approval based on pre-existing commitments. Please ensure you are familiar with the policy prior to submitting an application. You can check the supplementary exam information page on FSE101 in iLearn (bit.ly/FSESupp) for dates, and approved applicants will receive an individual notification one week prior to the exam with the exact date and time of their supplementary examination.
SPECIAL CONSIDERATION
The only excuse for not sitting an examination at the designated time is because of documented illness of unavoidable disruption. In these special circumstances you may apply for special consideration via ask.mq.edu.au.
Assessment Tasks
| Name | Weighting | Hurdle | Due |
|---|---|---|---|
| Assignment 1 | 15% | No | 03/04/2018 |
| Assignment 2 | 15% | No | 15/5/2018 |
| Assignment 3 | 15% | No | 05/06/2018 |
| Final examination | 55% | No | To be determined |
Assignment 1
Due: 03/04/2018
Weighting: 15%
Assignment 1
On successful completion you will be able to:
- be familiar with probability concepts, including random variables and probability distributions for discrete, continuous and multivariate situtations and know how to apply these concepts in the context of statistical inference and sampling
- understand fundamental large sample concepts in probability, including modes of convergence and the central limit theorem and be able to apply these concepts to practical problems
Assignment 2
Due: 15/5/2018
Weighting: 15%
Assignment 2
On successful completion you will be able to:
- understand the principles and theory of estimation, including unbiasedness, consistency and relative efficiency, as well as the likelihood function and maximum likelihood estimation
Assignment 3
Due: 05/06/2018
Weighting: 15%
Assignment 3
On successful completion you will be able to:
- understand the principles and theory of statistical hypothesis testing, including likelihood ratio tests, score tests and Wald tests
- understand the principles of Bayesian inference
Final examination
Due: To be determined
Weighting: 55%
Final examination
On successful completion you will be able to:
- be familiar with probability concepts, including random variables and probability distributions for discrete, continuous and multivariate situtations and know how to apply these concepts in the context of statistical inference and sampling
- understand fundamental large sample concepts in probability, including modes of convergence and the central limit theorem and be able to apply these concepts to practical problems
- understand the principles and theory of estimation, including unbiasedness, consistency and relative efficiency, as well as the likelihood function and maximum likelihood estimation
- understand the principles and theory of statistical hypothesis testing, including likelihood ratio tests, score tests and Wald tests
- understand the principles of Bayesian inference
Delivery and Resources
LECTURES
Monday 6-8 p.m. in Room G240, EMC Building (3 Innovation Road).
TUTORIALS (Weeks 2-13)
There are three tutorials held on Monday evening that are held in the same location as the lecture Room G240, EMC Building (3 Innovation Road).
- 4-5pm
- 5-6pm
- 8:30-9:30pm (variable start time, shortly after the lecture concludes after 8pm)
TEXTBOOKS
The material and lecture notes are heavily based on the book:
Inference Principles for Biostatisticians. I.C. Marschner. Chapman and Hall / CRC Press (2015).
This book is recommended as additional reading beyond the lecture notes, but is not a compulsory text.
Additional readings and problems are also available in the following book:
Mathematical Statistics with Applications, Seventh Edition. D.D. Wackerly, W. Mendenhall, R.L. Scheaffer. Duxbury Press.
INTERNET RESOURCES / TECHNOLOGIES USED
This unit has an iLearn website available at https://ilearn.mq.edu.au/login/MQ/
Lecture notes: these will be available on the iLearn site prior to the lecture.
Audio recordings: all lectures will be recorded and will be available after the lecture.
Consult the iLearn website frequently. Other resources available include a discussion board, assignments, administrative updates etc.
CONSULTATION HOURS
Members of the Statistics Department have consultation hours each week when they are available to help students. These consultation hours are available from the Statistics Department in Level 6 of 12WW (E7A).
Unit Schedule
Weeks 1-12 will involve the study of 8 topics, each of which will be consist of 1-2 lectures.
Week 13 will involve revision.
The 8 topics to be studied in this unit are as follows:
Topic 1: Probability and random samples
Topic 2: Large sample probability concepts
Topic 3: Estimation concepts
Topic 4: Likelihood
Topic 5: Estimation methods
Topic 6: Hypothesis testing concepts
Topic 7: Hypothesis testing methods
Topic 8: Bayesian inference
Policies and Procedures
Macquarie University policies and procedures are accessible from Policy Central (https://staff.mq.edu.au/work/strategy-planning-and-governance/university-policies-and-procedures/policy-central). Students should be aware of the following policies in particular with regard to Learning and Teaching:
- Academic Appeals Policy
- Academic Integrity Policy
- Academic Progression Policy
- Assessment Policy
- Fitness to Practice Procedure
- Grade Appeal Policy
- Complaint Management Procedure for Students and Members of the Public
- Special Consideration Policy (Note: The Special Consideration Policy is effective from 4 December 2017 and replaces the Disruption to Studies Policy.)
Undergraduate students seeking more policy resources can visit the Student Policy Gateway (https://students.mq.edu.au/support/study/student-policy-gateway). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.
If you would like to see all the policies relevant to Learning and Teaching visit Policy Central (https://staff.mq.edu.au/work/strategy-planning-and-governance/university-policies-and-procedures/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/study/getting-started/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.
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 Services and 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.
Student Enquiries
For all student enquiries, visit Student Connect at ask.mq.edu.au
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
PG - Capable of Professional and Personal Judgment and Initiative
Our postgraduates will demonstrate a high standard of discernment and common sense in their professional and personal judgment. They will have the ability to make informed choices and decisions that reflect both the nature of their professional work and their personal perspectives.
This graduate capability is supported by:
Learning outcomes
- understand the principles and theory of estimation, including unbiasedness, consistency and relative efficiency, as well as the likelihood function and maximum likelihood estimation
- understand the principles and theory of statistical hypothesis testing, including likelihood ratio tests, score tests and Wald tests
- understand the principles of Bayesian inference
Assessment tasks
- Assignment 3
- Final examination
PG - Discipline Knowledge and Skills
Our postgraduates will be able to demonstrate a significantly enhanced depth and breadth of knowledge, scholarly understanding, and specific subject content knowledge in their chosen fields.
This graduate capability is supported by:
Learning outcomes
- be familiar with probability concepts, including random variables and probability distributions for discrete, continuous and multivariate situtations and know how to apply these concepts in the context of statistical inference and sampling
- understand fundamental large sample concepts in probability, including modes of convergence and the central limit theorem and be able to apply these concepts to practical problems
- understand the principles and theory of estimation, including unbiasedness, consistency and relative efficiency, as well as the likelihood function and maximum likelihood estimation
- understand the principles and theory of statistical hypothesis testing, including likelihood ratio tests, score tests and Wald tests
- understand the principles of Bayesian inference
Assessment tasks
- Assignment 1
- Assignment 2
- Assignment 3
- Final examination
PG - Critical, Analytical and Integrative Thinking
Our postgraduates will be capable of utilising and reflecting on prior knowledge and experience, of applying higher level critical thinking skills, and of integrating and synthesising learning and knowledge from a range of sources and environments. A characteristic of this form of thinking is the generation of new, professionally oriented knowledge through personal or group-based critique of practice and theory.
This graduate capability is supported by:
Learning outcomes
- understand the principles and theory of estimation, including unbiasedness, consistency and relative efficiency, as well as the likelihood function and maximum likelihood estimation
- understand the principles and theory of statistical hypothesis testing, including likelihood ratio tests, score tests and Wald tests
- understand the principles of Bayesian inference
Assessment tasks
- Assignment 3
- Final examination
PG - Research and Problem Solving Capability
Our postgraduates will be capable of systematic enquiry; able to use research skills to create new knowledge that can be applied to real world issues, or contribute to a field of study or practice to enhance society. They will be capable of creative questioning, problem finding and problem solving.
This graduate capability is supported by:
Learning outcomes
- be familiar with probability concepts, including random variables and probability distributions for discrete, continuous and multivariate situtations and know how to apply these concepts in the context of statistical inference and sampling
- understand fundamental large sample concepts in probability, including modes of convergence and the central limit theorem and be able to apply these concepts to practical problems
Assessment tasks
- Assignment 1
- Assignment 2
- Final examination
PG - Effective Communication
Our postgraduates will be able to communicate effectively and convey their views to different social, cultural, and professional audiences. They will be able to use a variety of technologically supported media to communicate with empathy using a range of written, spoken or visual formats.
This graduate capability is supported by:
Learning outcomes
- understand the principles and theory of estimation, including unbiasedness, consistency and relative efficiency, as well as the likelihood function and maximum likelihood estimation
- understand the principles and theory of statistical hypothesis testing, including likelihood ratio tests, score tests and Wald tests
- understand the principles of Bayesian inference
Assessment tasks
- Assignment 3
- Final examination
PG - Engaged and Responsible, Active and Ethical Citizens
Our postgraduates will be ethically aware and capable of confident transformative action in relation to their professional responsibilities and the wider community. They will have a sense of connectedness with others and country and have a sense of mutual obligation. They will be able to appreciate the impact of their professional roles for social justice and inclusion related to national and global issues
This graduate capability is supported by:
Learning outcomes
- understand the principles and theory of estimation, including unbiasedness, consistency and relative efficiency, as well as the likelihood function and maximum likelihood estimation
- understand the principles and theory of statistical hypothesis testing, including likelihood ratio tests, score tests and Wald tests
- understand the principles of Bayesian inference
Assessment tasks
- Assignment 3
- Final examination
Changes from Previous Offering
The 2018 offering of STAT710 has similar content to the 2017 offering.
Changes since First Published
| Date | Description |
|---|---|
| 26/04/2018 | Unit convenor details updated |
| 02/03/2018 | Extra tutorial and times updated. |
| 27/02/2018 | Unit Convenor details updated Tutorial times updated |