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STA8190 Advanced Statistics B

Semester 2, 2019 Online
Short Description: Advanced Statistics B
Units : 1
Faculty or Section : Faculty of Health, Engineering and Sciences
School or Department : School of Agric, Comp and Environ Sciences
Student contribution band : Band 2
ASCED code : 010103 - Statistics
Grading basis : Graded


Examiner: Rachel King

Other requisites

Enrolment in this course requires the approval of the Course Examiner and appropriate Program Coordinator.
Within this course students can choose one of a number of topics, each of which has different prerequisites (or equivalent) as outlined below, or on approval of the examiner.

Applied Statistical Models: (prerequisite STA3300) This topic is not available to students who have already undertaken or intend to undertake STA3301.

Bayesian Statistics: (prerequisites STA2301 and STA2302)

Non-parametric Statistics: (prerequisite STA2300)

Survey Design and Analysis: (prerequisite STA2300)

Statistical Literacy in Middle Schooling: (no prerequisite) This topic is not available to students who have already undertaken or intend to undertake STA2300, STA3100 or the Data Analysis and Research Reporting topic in STA8180.


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 a certain area. The studies could involve, for example, directed readings, a project (where appropriate), or some other approved activity which would complement the student's studies in the program.


On completion of this course students will be able to

  1. demonstrate advanced knowledge and skills in the study area chosen.


Description Weighting(%)
1. The study area chosen will be approved after the student consults with the Course Examiner and the appropriate Program Coordinator. Students should nominate the topic they wish to study when contacting 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. One of the following topics can be chosen. 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

Applied Statistical Models (see STA3301 for topics)

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.

Survey Design and Analysis: this topic covers the principles and practice of designing surveys, and the analysis of data from them. This includes: Questionnaire design, Measurement: types of data, measurement scales, reliability; missing data, data cleaning and analysis of categorical and ordinal data.

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

ALL textbooks and materials available to be purchased can be sourced from USQ's Online Bookshop (unless otherwise stated). (

Please contact us for alternative purchase options from USQ Bookshop. (

Applied Statistical Models: see STA3301 for materials.
Bayesian Statistics: McElreath, Richard. 2015 Statistical Rethinking: A Bayesian Course with examples in R and Stan, Chapman and Hall/CRC.
Non-parametric Statistics: to be advised by supervisor on enrolment.
Statistical Literacy in Middle Schooling: see STA3100 for text.
Survey Design and Analysis: to be advised by supervisor on enrolment.

Reference materials

Reference materials are materials that, if accessed by students, may improve their knowledge and understanding of the material in the course and enrich their learning experience.

Student workload expectations

Activity Hours
Assessments 20.00
Private Study 90.00
Project Work 40.00
Supervisor Consultation 15.00

Assessment details

Description Marks out of Wtg (%) Due Date Notes
Assignment 1 40 40 24 Oct 2019
Assignment 2 (Project) 60 60 24 Oct 2019

Important assessment information

  1. Attendance requirements:
    Attendance requirements: There are no attendance requirements for this course. It is the students' responsibility to study all material provided to them or required to be accessed by them to maximise their chance of meeting the objectives of the course and to be informed of course-related activities and administration.

  2. Requirements for students to complete each assessment item satisfactorily:
    To satisfactorily complete an individual assessment item a student must achieve at least 50% of the marks for that item.

  3. Penalties for late submission of required work:
    Students should refer to the Assessment Procedure (point 4.2.4)

  4. Requirements for student to be awarded a passing grade in the course:
    To be assured of receiving a passing grade a student achieve at least 50% of the total weighted marks available for the course.

  5. Method used to combine assessment results to attain final grade:
    The final grades for students will be assigned on the basis of the weighted aggregate of the marks obtained for each of the summative assessment items in the course.

  6. Examination information:
    There is no examination in this course.

  7. Examination period when Deferred/Supplementary examinations will be held:
    There will be no Deferred or Supplementary examinations in this course.

  8. University Student Policies:
    Students should read the USQ policies: Definitions, Assessment and Student Academic Misconduct to avoid actions which might contravene University policies and practices. These policies can be found at In particular, students must familiarise themselves with the USQ Assessment Procedures (

Other requirements

  1. Computer, e-mail and Internet access:
    Students are required to have access to a personal computer, e-mail capabilities and Internet access to UConnect. Current details of computer requirements can be found at .

  2. Students can expect that questions in assessment items in this course may draw upon knowledge and skills that they can reasonably be expected to have acquired before enrolling in this course. This includes knowledge contained in pre-requisite courses and appropriate communication, information literacy, analytical, critical thinking, problem solving or numeracy skills. Students who do not possess such knowledge and skills should not expect the same grades as those students who do possess them.