Key facts

UNE unit code: STAT320

*You are viewing the 2024 version of this unit which may be subject to change in future.

Start
  • Trimester 2 - On Campus
  • Trimester 2 - Online
Campus
  • Armidale Campus
24/7 online support
  • Yes
Intensive schools
  • No
Supervised exam
  • No
Credit points
  • 6

Unit information

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In an era of rapid advances in data collection and computational methods, complex data sets are increasingly prevalent. Professionals who can undertake sophisticated statistical modelling to work with these data are vital in many settings.

Build on your statistics foundation and develop skills in advanced regression techniques that will help you to stand out, with this unit.

You will go beyond the linear model, exploring Generalised Linear Models (GLMs), non-linear regression and mixed effects models.

Learning not only how to analyse and interpret complex data, but also how to effectively communicate your results and conclusions, the unit will help you to fine tune invaluable applied skills.

With an approachable blend of theory and hands-on applied exercises, this unit is ideal if you are interested in further developing statistical modelling skills for use in the workplace or academic research.

Offerings

For further information about UNE's teaching periods, please go to Principal Dates.

Teaching period
Mode/location
Trimester 2On Campus, Armidale Campus
Trimester 2Online

*Offering is subject to availability

Intensive schools

There are no intensive schools required for this unit.

Enrolment rules

Pre-requisites
STAT200 or STAT210 or STAT261 or STAT270 or STAT470; or candidature in a postgraduate award
Restrictions
STAT356 or STAT420 or STAT456
Combined units

Notes

Students must have access to the statistical software R (open source).

Please refer to the student handbook for current details on this unit.

Unit coordinator(s)

To be advised

Learning outcomes

Upon completion of this unit, students will be able to:

  1. analyse data, and interpret and communicate results and conclusions, from a wide range of experimental designs;
  2. use exploratory data analysis to gauge systematic and random components in a data set;
  3. demonstrate well-developed judgement in the application of advanced models to complex problems;
  4. fit, assess and interpret advanced statistical models; and
  5. build on and broaden their theoretical and technical knowledge of statistical terminology, concepts and methodology, which will enable them to read and critically appraise scientific literature with some confidence.

Assessment information

Assessments are subject to change up to 8 weeks prior to the start of the teaching period in which you are undertaking the unit.

TitleMust CompleteWeightOfferingsAssessment Notes
Assessment 1Yes10%All offerings

Problem solving and computational analysis. Must obtain at least 40% for the assignments overall.

Assessment 2Yes10%All offerings

Problem solving and computational analysis. Must obtain at least 40% for the assignments overall.

Assessment 3Yes10%All offerings

Problem solving and computational analysis. Must obtain at least 40% for the assignments overall.

Assessment 4Yes10%All offerings

Problem solving and computational analysis. Must obtain at least 40% for the assignments overall.

Final ExaminationYes60%All offerings

Take home examination.Must obtain at least 40% in the take home examination and obtain an overall pass grade.

Learning resources

Textbooks are subject to change up to 8 weeks prior to the start of the teaching period in which you are undertaking the unit.

Textbook information will be displayed approximately 8 weeks prior to the commencement of the teaching period. Please note that textbook requirements may vary from one teaching period to the next.

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