Statistical Practice

Undergraduate

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Mode icon
Mode
Mode
Your studies will be on-campus, and may include some online delivery
On campus
area/catalogue icon
Area/Catalogue
STAT X290
Course ID icon
Course ID
208402
Campus icon
Campus
Adelaide City Campus East, Mawson Lakes
Level of study
Level of study
Undergraduate
Unit value icon
Unit value
6
Course owner
Course owner
School of Mathematical Science
Course level icon
Course level
2
Work Integrated Learning course
Work Integrated Learning course
Yes
Work Integrated Learning hours
Work Integrated Learning hours
75 hours
Study abroad and student exchange icon
Inbound study abroad and exchange
Inbound study abroad and exchange
The fee you pay will depend on the number and type of courses you study.
No
University-wide elective icon
University-wide elective course
No
Single course enrollment
Single course enrolment
No

Course overview

To equip learners with practical experience in applying Generalized Linear Modelling (GLM) methods to real-world industry problems, fostering skills in data analysis using R programming language, teamwork, and communication. This course builds on the foundational linear modelling skills developed in probability and statistics and serves as a bridge to more advanced topics in statistical modelling.

  • Working with Data
  • Generalised Linear Models
  • Communicating Project Outcomes

Course learning outcomes

  • Apply statistical techniques to analyse real-world problems
  • Apply and interpret results from Generalised Linear Models (GLMS)
  • Perform data analysis using R programming language
  • Present statistical findings effectively through written reports and data visualisations
  • Evaluate project outcomes, reflecting on the assumptions, strengths and limitations of the statistical analyses that were applied to project data
  • Demonstrate ability to work collaboratively in a team to deliver project outcomes

Prerequisite(s)

  • Must have completed all of MATHX104 Linear Algebra/STATX100 Probability and Statistics

Corequisite(s)

N/A

Antirequisite(s)

N/A

Assumed knowledge

N/A

Textbook(s)

N/A

Inherent requirements

N/A

Other resources

N/A

Assessment

  • Data Collection and/or Analysis, Report, Skills Demonstration – 50%
  • Peer Review, Peer Review, Peer Review – 20%
  • Problem Solving Task – 20%
  • Discussion, Discussion, Discussion, Discussion, Discussion, Discussion, Discussion, Discussion, Discussion, Discussion – 10%
Degree list
The following degrees include this course
Bachelor of Mathematics
Bachelor of Mathematics (Honours)