Course search
2022 unit information
Unit delivery will be in line with the most current COVIDSafe health guidelines. We continue to tailor learning experiences for each unit to achieve the best possible mix of online and on-campus activities that successfully blend our approaches to learning, working and research. Please check your unit sites for announcements and updates.
Last updated: 4 March 2022
Nil
SIT718
For students enrolled in S577: Nil
Students will on average spend 150 hours over the teaching period undertaking the teaching, learning and assessment activities for this unit.
1 x 2 hour online class per week, 1 x 2 hour workshop per week. weekly drop-in sessions.
Online independent and collaborative learning including optional scheduled activities as detailed in the unit site.
The aim of this unit is to provide students with the opportunity to develop advanced working knowledge in statistical modelling and statistical programming. Students will learn how to apply advanced statistical theories such as generalised additive modelling to model real-world data problems. They will also learn about advanced statistical programming using the R language, to perform simulation, model development, model checking, and result interpretation.
Upon successful completion of this unit, students will be able to apply the right statistical models, including generalised linear models and generalised additive models, to solve problems of real-world complexity. They will know how to use R to transform untidy data to tidy data, to perform exploratory data analysis, to develop and check models, and to communicate the analysis results.
Apply statistical thinking to analyse problems of real-world complexity and formulate corresponding statistical inference questions.
GLO1: Discipline-specific knowledge and capabilitiesGLO4: Critical thinkingGLO5: Problem solving
ULO2
Demonstrate in-depth knowledge in advanced statistical models including generalized linear models and generalized additive models.
GLO1: Discipline-specific knowledge and capabilitiesGLO2: CommunicationGLO4: Critical thinking
ULO3
Apply the statistical language R to transform untidy data into tidy data, to perform exploratory data analysis, and to develop and check models.
GLO1: Discipline-specific knowledge and capabilitiesGLO3: Digital literacyGLO4: Critical thinkingGLO5: Problem solvingGLO6: Self-management
ULO4
Apply modern statistical computing tools to report analyses to the wider communities, with effective use of advanced interactive information visualisation methods.
GLO1: Discipline-specific knowledge and capabilitiesGLO2: CommunicationGLO3: Digital literacyGLO4: Critical thinkingGLO5: Problem solving
ULO5
These Unit Learning Outcomes are applicable for all teaching periods throughout the year
Two problem solving tasks.
Research, case analysis, evaluations and presentations
The assessment due weeks provided may change. The Unit Chair will clarify the exact assessment requirements, including the due date, at the start of the teaching period.
There is no prescribed textbook for this unit.
The texts and reading list for the unit can be found on the University Library via the link below: SIT741 Note: Select the relevant trimester reading list. Please note that a future teaching period's reading list may not be available until a month prior to the start of that teaching period so you may wish to use the relevant trimester's prior year reading list as a guide only.
Click on the fee link below which describes you:
