Courses & Units

Data Handling and Statistics 3 KMA353


Extension of the concepts, methods and tools introduced in KMA253. 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; methodology for studying data collected over time. Expertise with statistical computing packages will be extended. Examples will be drawn from the physical and social sciences and bioinformatics


Unit name Data Handling and Statistics 3
Unit code KMA353
Credit points 12.5
Faculty/School Faculty of SET, AMC, IMAS
School of Physical Sciences
Discipline Mathematics and Physics
Teaching staff

Mr D Palmer

Level Advanced
Available as student elective? Yes
Breadth Unit? No


This unit is currently unavailable.


Units are offered as On-campus where the majority of teaching will occur at the campus identified.  Units offered Off-campus generally have no requirement for attendance at a physical university campus unless the unit has practical or fieldwork components*:  the campus indicated for an Off-Campus unit is the one at which teaching is administered from.

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* 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 (see withdrawal dates explained for more information).

Unit census dates currently displaying for 2017 are indicative and subject to change. Finalised census dates for 2017 will be available from the 1st October 2016.

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Band Field of Education

Fees for next year will be published in October. The fees above only apply for the year shown.

Please note: international students should refer to this page to get an indicative course cost.




Teaching Pattern

 3 x 1hr face-to-face lectures, 1x1hr tutorial, 1x1-hr computer lab sessions.


3 class tests (45%) projects and assignments (55%)

TimetableView the lecture timetable | View the full unit timetable



There are no required textbooks. The following three freely available books are recommended:

(1) Elements of Statistical Learning, Hastie, TIbshirani, Friedman. PDF available at:

(2) An Introduction to Generalized Linear Models, Dobson, ODF available at:

(3) An Introduction to Statistical Learning, James, Witten, Haistie, Tibshirani, PDF available at:

The University reserves the right to amend or remove courses and unit availabilities, as appropriate.