STATS 7059 - Mathematical Statistics
North Terrace Campus - Semester 1 - 2024
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General Course Information
Course Details
Course Code STATS 7059 Course Mathematical Statistics Coordinating Unit Mathematical Sciences Term Semester 1 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Contact Up to 3 hours per week Available for Study Abroad and Exchange Y Assumed Knowledge (MATHS 1012 and STATS 2107) or (MATHS 2201 and MATHS 2202 and STATS 2107) or (MATHS 2106 and MATHS 2107 and STATS 2107). Assessment Ongoing assessment, exam Course Staff
Course Coordinator: Professor Dino Sejdinovic
Course Timetable
The full timetable of all activities for this course can be accessed from .
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Learning Outcomes
Course Learning Outcomes
On successful completion of this course students will be able to:
1. demonstrate knowledge of, and properties of, statistical models in common use,
2. understand the basic principles underlying statistical inference (estimation and hypothesis testing),
3. be able to construct tests and estimators, and derive their properties,
4. demonstrate knowledge of applicable large sample theory of estimators and tests.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) Attribute 1: Deep discipline knowledge and intellectual breadth
Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.
All Attribute 2: Creative and critical thinking, and problem solving
Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.
All Attribute 3: Teamwork and communication skills
Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.
3 Attribute 4: Professionalism and leadership readiness
Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.
1,2,3 Attribute 8: Self-awareness and emotional intelligence
Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.
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Learning Resources
Required Resources
A set of lecture notes will be provided.Recommended Resources
Recommended textbooks:
Mathematical Statistics with Applications (7th ed.), by D.D. Wackerly, W. Mendenhall, and R.L. Scheaffer, Duxbury Press.
Mathematical Statistics and Data Analysis (3rd ed.), by J.A. Rice, Duxbury Press.
Useful textbooks:
Statistical Inference (2nd ed.), by G. Casella and R. L. Berger, Duxbury Press.
Modern Mathematical Statistics with Applications (2nd ed.), by J.L. Devore and K.N. Berk, Springer.Online Learning
This course uses MyUni exclusively for providing electronic resources: lecture notes, assignments, solutions, etc. Students are advised to make extensive use of these resources. -
Learning & Teaching Activities
Learning & Teaching Modes
This course relies on lectures as the primary delivery mechanism for the material. Tutorials supplement the lectures by providing exercises and example problems to enhance the understanding obtained through lectures. A sequence of written assignments provides assessment opportunities for students to gauge their progress and understanding.
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 Tutorials 5 18 Assessments 14 48 Total 156
Learning Activities Summary
Lecture outline
1-3: Review of probability, random variables, density and mass functions, expectation, mean, variance
4-6: Standard probability distributions (statistical models) and their properties
6-7: Exponential families of distributions; distribution and expectation of a function of a random variable
8-11: Joint distributions, covariance, correlation, independence of random variables, distributions of functions of jointly distributed random variables, conditional distributions, conditional means and variances
12-14: Sums of independent random variables, transformations of two or more jointly distributed random variables
14-15: Random vectors, the multivariate normal distribution and properties
16-19: Modes of convergence, laws of large numbers, central limit theorem, Jensen's inequality
20-22: Random samples, the chi-square, t, and F distributions and their roles in normal sampling, basic concepts of statistical inference, the likelihood principle, sufficient statistics
23-25: Basic concepts of estimation; method of moments, maximum likelhood, large sample properties (consistency, asymptotic normality), mean square eror, Rao-Blackwell theorem
26-27: Fisher information, the Cramer-Rao inequality, confidence intervals and properties
28-30: Hypothesis testing, types of errors, p-value, power, Neyman-Pearson lemma, uniformly most powerful tests, likelihood ratio tests, Wald tests, score tests
Tutorial outline: Tutorial material will be integrated into the lecture and assignment material -
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
Component Weighting Objective Assessment Assignments 30% all Quizzes 10% all Mid-Semester Test 20% all Exam 40% all Assessment Related Requirements
An aggregate score of at least 50% is required to pass the course.Assessment Detail
There are three assignments in this course (each contribute 10% of final grade).
There are also 10 quizzes througout the course (1% each).
There will be a mid-semester test (20%) and a final exam (40% of final grade).Submission
1. All written assignments are to be submitted online via MyUni.
2. Late assignments will not be accepted unless an extension has been arranged prior to the due date.Course Grading
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 1-49 Fail P 50-64 Pass C 65-74 Credit D 75-84 Distinction HD 85-100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending 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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