Data Handling and Statistics 3 is the third of three applied statistics units offered by
the School of Natural Sciences (Mathematics). It provides an extension of the
concepts, methods and tools introduced in KMA253. It is a 'hands-on' course in which
the emphasis is on the development of skills in the selection and application of
statistical methods and the presentation of statistical results. Statistical methodology
covered in the course will be selected from the following: analysis of variance applied
in the area of experimental designs; generalised linear methodology; multivariate
statistical methodology; methods for analysing frequency data; mixed-effects models,
and model selection. Expertise with the statistical computing language R will be
extended. Examples will be drawn from the physical, biological and social sciences.
|Unit name||Data Handling and Statistics 3|
|College/School||College of Sciences and Engineering
School of Natural Sciences
|Coordinator||Doctor Shane Richards|
|Available as student elective?||Yes|
|Delivered By||University of Tasmania|
|Location||Study period||Attendance options||Available to|
- International students
- Domestic students
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|Study Period||Start date||Census date||WW date||End date|
* The Final WW Date is the final date from which you can withdraw from the unit without academic penalty, however you will still incur a financial liability (refer to How do I withdraw from a unit? for more information).
Unit census dates currently displaying for 2022 are indicative and subject to change. Finalised census dates for 2022 will be available from the 1st October 2021. Note census date cutoff is 11.59pm AEST (AEDT during October to March).
- analyse data with single and multiple explanatory variables by apply theoretical and practical knowledge of modern statistical analysis, formulating models and applying appropriate hypothesis tests.
- to interpret results of the statistical analyses and communicate the design of the experiment, the goal of the experiment and the analytical results to statistician and non-statisticians.
- implement statistical analysis in R statistical programming language, interpret the output and provide valuable graphical representation of the results.
- apply knowledge of statistical analysis pursue research questions specific to other disciplines.
|Field of Education||Commencing Student Contribution 1||Grandfathered Student Contribution 1||Approved Pathway Course Student Contribution 2||Domestic Full Fee|
1 Please refer to more information on student contribution amounts.
2 Please refer to more information on eligibility and Approved Pathway courses.
3 Please refer to more information on eligibility for HECS-HELP.
4 Please refer to more information on eligibility for FEE-HELP.
Please note: international students should refer to What is an indicative Fee? to get an indicative course cost.
3 x 1-hr face-to-face lectures, 1 x 1-hr tutorial, 1 x 1-hr computer lab sessions.
|Assessment||Tests (46%)|Projects (6) (54%)|
|Timetable||View the lecture timetable | View the full unit timetable|
Required readings will be listed in the unit outline prior to the start of classes.
|Links||Booktopia textbook finder|
The University reserves the right to amend or remove courses and unit availabilities, as appropriate.