This is an intermediate level subject in statistics. It extends STAT11-111 in the topics of probability, discrete and continuous random variables, multivariate random variables, concepts of estimation, confidence interval estimation and hypothesis testing. Topics such as moment generating functions are also included.
|Academic unit:||Bond Business School|
|Subject title:||Mathematical Statistics|
Delivery & attendance
|Prescribed resources:|| |
|[email protected] & Email:||[email protected] is the online learning environment at Bond University and is used to provide access to subject materials, lecture recordings and detailed subject information regarding the subject curriculum, assessment and timing. Both iLearn and the Student Email facility are used to provide important subject notifications. Additionally, official correspondence from the University will be forwarded to students’ Bond email account and must be monitored by the student.|
To access these services, log on to the Student Portal from the Bond University website as www.bond.edu.au
Assurance of learning
Assurance of Learning means that universities take responsibility for creating, monitoring and updating curriculum, teaching and assessment so that students graduate with the knowledge, skills and attributes they need for employability and/or further study.
At Bond University, we carefully develop subject and program outcomes to ensure that student learning in each subject contributes to the whole student experience. Students are encouraged to carefully read and consider subject and program outcomes as combined elements.
Program Learning Outcomes (PLOs)
Program Learning Outcomes provide a broad and measurable set of standards that incorporate a range of knowledge and skills that will be achieved on completion of the program. If you are undertaking this subject as part of a degree program, you should refer to the relevant degree program outcomes and graduate attributes as they relate to this subject.
Subject Learning Outcomes (SLOs)
On successful completion of this subject the learner will be able to:
- Explain the concepts of probability and calculate probabilities in a variety of scenarios.
- Define and derive probability generating functions, moment generating functions, cumulant generating functions and cumulants, and use them to evaluate moments.
- Define, apply and simulate basic discrete and continuous distributions.
- Explain and apply the concepts of simple and multivariate random variables, and their probability distributions.
- Describe and apply the main methods of estimation and the main properties of estimators.
- Construct a variety of confidence intervals and test a variety of hypotheses.
- Understand simple and basic multiple regression.
|Paper-based Examination (Closed)||Mid-semester Examination||30%||Mid-Semester Examination Period||1, 2, 3, 4.|
|Skills Assignment||Assignment 1||10%||In Consultation||1, 2, 3, 4.|
|Skills Assignment||Assignment 2||10%||In Consultation||1, 2, 3, 4, 5, 6, 7.|
|Paper-based Examination (Closed)||Final Examination||50%||Final Examination Period||1, 2, 3, 4, 5, 6, 7.|
- * Assessment timing is indicative of the week that the assessment is due or begins (where conducted over multiple weeks), and is based on the standard University academic calendar
- C = Students must reach a level of competency to successfully complete this assessment.
|High Distinction||85-100||Outstanding or exemplary performance in the following areas: interpretative ability; intellectual initiative in response to questions; mastery of the skills required by the subject, general levels of knowledge and analytic ability or clear thinking.|
|Distinction||75-84||Usually awarded to students whose performance goes well beyond the minimum requirements set for tasks required in assessment, and who perform well in most of the above areas.|
|Credit||65-74||Usually awarded to students whose performance is considered to go beyond the minimum requirements for work set for assessment. Assessable work is typically characterised by a strong performance in some of the capacities listed above.|
|Pass||50-64||Usually awarded to students whose performance meets the requirements set for work provided for assessment.|
|Fail||0-49||Usually awarded to students whose performance is not considered to meet the minimum requirements set for particular tasks. The fail grade may be a result of insufficient preparation, of inattention to assignment guidelines or lack of academic ability. A frequent cause of failure is lack of attention to subject or assignment guidelines.|
For the purposes of quality assurance, Bond University conducts an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.
Students must check the [email protected] subject site for detailed assessment information and submission procedures.
Policy on late submission and extensions
A late penalty will be applied to all overdue assessment tasks unless an extension is granted by the subject coordinator. The standard penalty will be 10% of marks awarded to that assessment per day late with no assessment to be accepted seven days after the due date. Where a student is granted an extension, the penalty of 10% per day late starts from the new due date.
Policy on plagiarism
University’s Academic Integrity Policy defines plagiarism as the act of misrepresenting as one’s own original work: another’s ideas, interpretations, words, or creative works; and/or one’s own previous ideas, interpretations, words, or creative work without acknowledging that it was used previously (i.e., self-plagiarism). The University considers the act of plagiarising to be a breach of the Student Conduct Code and, therefore, subject to the Discipline Regulations which provide for a range of penalties including the reduction of marks or grades, fines and suspension from the University.
Feedback on assessment
Feedback on assessment will be provided to students within two weeks of the assessment submission due date, as per the Assessment Policy.
If you have a disability, illness, injury or health condition that impacts your capacity to complete studies, exams or assessment tasks, it is important you let us know your special requirements, early in the semester. Students will need to make an application for support and submit it with recent, comprehensive documentation at an appointment with a Disability Officer. Students with a disability are encouraged to contact the Disability Office at the earliest possible time, to meet staff and learn about the services available to meet your specific needs. Please note that late notification or failure to disclose your disability can be to your disadvantage as the University cannot guarantee support under such circumstances.
Review of exploratory data analysis, including numerical and graphical summaries of location, spread and symmetry
Review of basic probability rules, including conditional probability and Bayes theorem, joint events and independence.
Continuation or probability and introduction to random variables, including joint distributions, moments and generating functions
Properties and relationships of common discrete random variables, including binomial, Poisson, negative binomial and hypergeometric
Properties and relationships of common continuous random variables, including normal, uniform, gamma, log-normal, Weibull, beta and Burr.
Properties of joint distributions, marginal distributions, conditional distributions and convolutions
Change of variable formulas and basic distribution theory and discussion of random sums and compound distributions
Central limit theorem and asymptotic distribution approximations including t-distributions, F-distributions and chi-squared distributions and the normal approximation to the binomial with continuity correction.
Mean-squared error and optimal estimators and confidence interval construction for univariate and bivariate data structures.
Maximum likelihood theorem, sufficiency and exponential families.
Construction and properties of tests for univariate and bivariate data structure and likelihood ratio and goodness of fit tests.
Introduction to linear regression models including estimating and testing parameters, diagnostics and analysis of variance tables.