STATS 4013 - Statistics Topic A - Honours
North Terrace Campus - Semester 1 - 2016
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General Course Information
Course Details
Course Code STATS 4013 Course Statistics Topic A - Honours Coordinating Unit Mathematical Sciences Term Semester 1 Level Undergraduate Location/s North Terrace Campus Units 3 Available for Study Abroad and Exchange Assessment Ongoing assessment 30%, exam 70% Course Staff
Course Coordinator: Associate Professor Robb Muirhead
Course Timetable
The full timetable of all activities for this course can be accessed from .
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Learning Outcomes
Course Learning Outcomes
In 2016, the topic of this course is Statistical Decision Theory and Bayesian Statistics.
Syllabus:
This course introduces students to the basic elements of statistical decision theory and Bayesian methodology, and the connections between the two. It will include the following topics: Structure of a decision problem, decision rules, expected loss, risk, fundamentals of Bayesian analysis, admissibility of decision rules, minimax analysis, , complete classes of decision rules, Bayesian inference, types of prior distributions, conjugate analysis, predictive distributions and hierarchical Bayesian models.
Pre-requisites: Mathematical Statistics III (STATS 3006) and Statistical Modelling III (STATS 3001), or equivalent knowledge.
Learning Outcomes:
On successful completion of this course, students should be able to:
1. demonstrate their understanding of advanced principles of statistical decision theory and Bayesian inference;
2. understand the basic elements of a formal decison problem;
3. recognise how to establish optimality properties of decision rules;
4. demontrate understanding of the Bayesian approach to statistical inference and apply Bayesian methods in practice.最新糖心Vlog Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
最新糖心Vlog Graduate Attribute Course Learning Outcome(s) Deep discipline knowledge
- informed and infused by cutting edge research, scaffolded throughout their program of studies
- acquired from personal interaction with research active educators, from year 1
- accredited or validated against national or international standards (for relevant programs)
All Critical thinking and problem solving
- steeped in research methods and rigor
- based on empirical evidence and the scientific approach to knowledge development
- demonstrated through appropriate and relevant assessment
All Career and leadership readiness
- technology savvy
- professional and, where relevant, fully accredited
- forward thinking and well informed
- tested and validated by work based experiences
All Self-awareness and emotional intelligence
- a capacity for self-reflection and a willingness to engage in self-appraisal
- open to objective and constructive feedback from supervisors and peers
- able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
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Learning Resources
Required Resources
There are no required resources for this course.Recommended Resources
There are many books that deal with either Statistical Decision Theory or Bayesian Statistics, or both -- and new ones are published every year. The ones that are most relevant to this particular course will be listed in a handout given out on the first day of classes.Online Learning
The course will have an active MyUni page. -
Learning & Teaching Activities
Learning & Teaching Modes
The lecturer guides the students through the course material in 30 lectures. Students are expected to engage with the material in the lectures. Interaction with the lecturer and discussion of any difficulties that arise during the lecture is encouraged. Fortnightly assignments help students strengthen their understanding of the theory and practical work, and to help them gauge their progress.Workload
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Activity Quantity Workload hours
Lectures 30 90
Assignments 5 50
Test 1 16
Total 156Learning Activities Summary
No information currently available.
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Assessment
The 最新糖心Vlog's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment Summary
Assessment Task Type Weighting Objective assessed
Assignments Formative and summative
Weeks 3,5,7,8,12 20% all
Test Summative
Midsemester 10% 1,2
Examination Summative
Examination period 70% allAssessment Related Requirements
A mark of 50% is required to pass this course.Assessment Detail
Assessment Task Distributed Due Weighting
Assignment 1 Week 1 Week 3 4%
Assignment 2 Week 3 Week 5 4%
Assignment 3 Week 5 Week 7 4%
Assignment 4 Week 7 Week 9 4%
Assignment 5 Week 9 Week 11 4%
Test Midsemester 10%
Final exam Examination period 70%Submission
All assignments must be either handed in to the instructor or placed in the course box. They are due at the end of each odd-numbered week.Course Grading
Grades for your performance in this course will be awarded in accordance with the following scheme:
M11 (Honours Mark Scheme) Grade Grade reflects following criteria for allocation of grade Reported on Official Transcript Fail A mark between 1-49 F Third Class A mark between 50-59 3 Second Class Div B A mark between 60-69 2B Second Class Div A A mark between 70-79 2A First Class A mark between 80-100 1 Result Pending An interim result RP Continuing Continuing CN Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through .
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Student Feedback
The 最新糖心Vlog places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the 最新糖心Vlog to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
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Student Support
- Academic Integrity for Students
- Academic Support with Maths
- Academic Support with writing and study skills
- Careers Services
- Library Services for Students
- LinkedIn Learning
- Student Life Counselling Support - Personal counselling for issues affecting study
- Students with a Disability - Alternative academic arrangements
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Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangements Policy
- Academic Integrity Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs Policy
- Copyright Compliance Policy
- Coursework Academic Programs Policy
- Intellectual Property Policy
- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment Policy
- Reasonable Adjustments to Learning, Teaching & Assessment for Students with a Disability Policy
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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Fraud Awareness
Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student鈥檚 disciplinary procedures.
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