USQ Logo
The current and official versions of the course specifications are available on the web at
Please consult the web for updates that may occur during the year.

STA8190 Advanced Statistics B

Semester 2, 2022 Online
Units : 1
Faculty or Section : Faculty of Health, Engineering and Sciences
School or Department : School of Mathematics, Physics & Computing
Student contribution band : Band 1
Grading basis : Graded
Version produced : 2 July 2022


Examiner: Enamul Kabir


This course provides flexibility in honours and postgraduate programs to cater for the widely varying interests and chosen specialisations of students. Statisticians need to be proficient in a wide range of statistical concepts and techniques. Many of these are either only touched on or omitted from undergraduate programs. An opportunity to broaden the students' knowledge-base with more advanced statistical techniques is provided in this course.

This course provides the opportunity for a student to pursue an area of study that will complement the other studies in the student's program. Typically, the course will consist of specialised investigations extending knowledge and skills in one of the areas listed in the Topics section below, or another Topic where appropriate and where a supervisor is available.

Students should nominate the topic they wish to study and then email the Course Examiner to enquire whether the topic and a suitable supervisor will be available in their semester of study, and for formal approval to enrol. As only one of the listed topics is chosen by each student the content of the course may vary from student to student. The weighting of the sub-topics within this unit depends on the topic chosen and will be discussed with the supervisor.

Course learning outcomes

On completion of this course students should be able to

  1. apply relevant advanced knowledge and skills in the study area chosen.


Description Weighting(%)
1. Bayesian Statistics: The fundamental idea of Bayesian Statistics comes from reverend Thomas Bayes the eighteenth century. This has been extended to create the quickly expanding field of Bayesian estimation and inferential methods, quickly extending into every quantitative area of research. Bayesian analyses mix the observed data with the prior distribution of the relevant parameters of the underlying model. It allows inference about population parameters in any statistical model in a slightly different way than the classical statistics because of incorporating prior or expert information. The course covers various estimation and test of hypothesis methods for different models using Bayesian approach. It also considers applications of Bayesian methods in diverse areas and uses R package for computational purposes.

Non-parametric Statistics: The nonparametric models do not make any assumptions about the functional form of the joint distribution of the sample observations. The only assumption made about the observations is that they are independent identically distributed (i.i.d.) from an arbitrary continuous distribution. As such the nonparametric statistics is also called distribution free statistics. There are no parameters in a nonparametric model. Although the nonparametric tests are less powerful than their parametric counterparts, when the distributional assumptions of the parametric models are not met or the data are not measured in the scale form, nonparametric methods are the only options. In this course, various nonparametric methods including test of hypotheses for one, two or multiple samples cases are covered.

Statistical Literacy in Middle Schooling: Students are introduced to basic concepts and tools commonly involved in collecting, managing, summarizing, analysing, interpreting, and presenting quantitative data. No prior statistical or mathematical knowledge is assumed. Methods of descriptive and inferential statistics are introduced. Issues related to causation and confounding; the nature of variability, the reliability of summary statistics, the limitations and assumptions underpinning statistical techniques; the appropriate use of language in interpreting an analysis; and the use of computer output in understanding data summary and analysis are explored. The emphasis is on the concepts, interpretations, and applications of statistics as used in the analysis of data, rather than on mathematical or computational aspects. The use of case studies is emphasised and writing of reports facilitated. Students are required to apply knowledge gained through this course to the development of statistical literacy in the middle school.

Text and materials required to be purchased or accessed

Bayesian Statistics: McElreath, Richard 2020, Statistical Rethinking: A Bayesian Course with examples in R and Stan, 2nd edn, Chapman and Hall/CRC.
Non-parametric Statistics: Corder, G.W., & Foreman, D.I., (2014), Nonparametric Statistics: A step-by-step approach, 2nd edn, John Wiley & Sons, Hoboken, New Jersey,
(This text can be accessed online through the Library’s website – see link provided.)
Statistical Literacy in Middle Schooling: Utts, J.M (2015), Seeing Through Statistics, 4th edn, Cengage Learning, Stanford, CT, USA.

Student workload expectations

To do well in this subject, students are expected to commit approximately 10 hours per week including class contact hours, independent study, and all assessment tasks. If you are undertaking additional activities, which may include placements and residential schools, the weekly workload hours may vary.

Assessment details

Approach Type Description Group
Weighting (%) Course learning outcomes
Assignments Written Problem Solving 1 No 50 1
Assignments Written Problem Solving 2 No 50 1
Date printed 2 July 2022