ETC2420 Statistical thinking
Faculty of Business and Economics
ETC2420 Statistical thinking is a level 2, 6-credit-point, undergraduate unit from the Faculty of Business and Economics, offered in 2027 in Semester 1 and Semester 2 at Clayton. It needs ETB1100, ETC1000, ETF1100, ETW1001, FIT1006, SCI1020, STA1010 or ETX1100 and unlocks 12 units, leading on to 23 units in all.
- Credit points
- 6
- Offered in 2027
- Semester 1, Semester 2
- Clayton
- Assessment
- Exam 40%
- and 3 other tasks
- Workload
- 144 hours
- per semester
Reviews
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Requisites
Before ETC2420
Prerequisites
Pass these before you enrol.
- ETB1100Business statisticsNo reviews yet
- ETC1000Business and economic statisticsNo reviews yet
- ETF1100Business statisticsNo reviews yet
- ETW1001Introduction to statistical analysisNo reviews yet
- FIT1006Business information analysisNo reviews yet
- SCI1020Introduction to statistical reasoningNo reviews yet
- STA1010Statistical methods for scienceNo reviews yet
- ETX1100Business statisticsNo reviews yet
After ETC2420
12 units list ETC2420 as a prerequisite or corequisite.
- ETC3250Introduction to machine learningNo reviews yet
- ETC3550Applied forecastingNo reviews yet
- ETC3580Advanced statistical modellingNo reviews yet
- ETC5250Introduction to machine learningNo reviews yet
- ETC5550Applied forecastingNo reviews yet
- ETC5580Advanced statistical modellingNo reviews yet
- ETF3231Business forecastingNo reviews yet
- ETF5231Business forecastingNo reviews yet
Enrolment rules
To be successful in this unit, background knowledge and application of maths is required at the equivalent of VCE Year 12 Higher level. You may have satisfied this by completing relevant prerequisite unit/s, or you have covered relevant topics in your final years of secondary study. You should self-assess your maths competency prior to enrolling in this unit.
Equivalent units
The same content under another code. Only one of them counts.
Overview
This unit presents data analysis, statistical modelling and decision-making in the presence of uncertainty, using a computational approach. You will use different frameworks of probability that are helpful for analysing real-world problems. Topics covered will include probability distributions, statistical inference (classical & Bayesian), simulation, permutation and randomisation methods, regression models, decision theory, and model assessment and diagnosis.
Offerings in 2027
| Teaching period | Campus | Mode |
|---|---|---|
| First semester | Clayton | On campus |
| Second semester | Clayton | On campus |
Assessment
- Exercise20%
- Quiz / Test20%
- Project20%
- Examination40%
Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.
Learning outcomes
When you finish this unit, you should be able to:
- 1
characterise and understand uncertainty using data
- 2
build statistical models to support decision-making, hypothesis testing and risk assessment
- 3
use randomisation methods in data collection and to assess causality and uncertainty
- 4
learn about and use concepts from probability
- 5
understand Bayesian and frequentist approaches to statistical modelling
- 6
further develop computational skills for statistical analysis.
Workload and teaching
- Seminars24 hours
- Workshops12 hours
- Tutorials12 hours
- Teaching approachProblem-based learning
- Teaching approachActive learning
Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled learning activities and independent study. Independent study may include associated readings, assessment and preparation for scheduled activities. You are expected to complete all pre-class activities prior to your scheduled class, and post-class activities should be completed after your scheduled class. Learning activities may include a combination of teacher directed, peer directed and online engagement activities.
This unit includes problem-based learning approaches, where you engage in research, integrate theory and practice and apply knowledge and skills to develop viable solutions in response to a problem or set of problems.
This unit engages you in actively applying your knowledge, skills and attributes in interactive, collaborative and reflective activities.
Learning resources
Technology resources
There may be an additional cost associated with purchasing a physical and/or virtual calculator. Specific details will be provided in the Learning Management System by commencement of Orientation week.
Where it fits
ETC2420 is part of 6 areas of study in the 2027 handbook.
- ACAN-USPECSpecialisation, Core unitsActuarial analyticsNo reviews yet
- BUAN-MAJMajor, Core unitsBusiness analyticsNo reviews yet
- BUAN-MINMinor, Core unitsBusiness analyticsNo reviews yet
- BALE-USPECSpecialisation, Core unitsBusiness analytics for economicsNo reviews yet
- ECNM-MAJMajor, Specified discipline unitsEconometricsNo reviews yet
- ECNM-MINMinor, Core unitsEconometricsNo reviews yet
Contacts
- Chief Examiners
- Dr Minh Huynh
- Associate Professor Damjan Vukcevic
Common questions
What are the prerequisites for ETC2420?
You need ETB1100, ETC1000, ETF1100, ETW1001, FIT1006, SCI1020, STA1010 or ETX1100 before you enrol. Enrolment rules also apply.
What can I take after ETC2420?
ETC2420 is a prerequisite or corequisite for 12 units, including ETC3250, ETC3550, ETC3580, ETC5250, ETC5550 and ETC5580. Those lead on to 23 units in all.
When is ETC2420 offered?
In 2027, ETC2420 runs in Semester 1 and Semester 2 at Clayton.
How much work is ETC2420?
The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.
Does ETC2420 have an exam?
Yes. The exam is worth 40% of the final mark, alongside 3 other tasks.
Which majors and minors include ETC2420?
ETC2420 is part of Actuarial analytics, Business analytics, Business analytics for economics and Econometrics.