Hobart
Introduction
Data Handling and Statistics 3 is the third 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 upper-level statistical methodology. Emphasis is also placed on the presentation of statistical analyses in a written format that promotes reproducible research. Topics covered in the course include: hypothesis testing, experimental design, inference, analysis presentation, generalised linear modelling; mixed-effects modelling, multinomial regression, and model selection. Expertise with the statistical computing language R and RStudio will be extended, including the application of R Markdown for promoting reproducible research. Examples will be drawn from the biological, physical and social sciences.
Summary 2020
| Unit name | Data Handling and Statistics 3 |
|---|---|
| Unit code | KMA353 |
| Credit points | 12.5 |
| Faculty/School | College of Sciences and Engineering School of Natural Sciences |
| Discipline | Mathematics |
| Coordinator | |
| Teaching staff | |
| Level | Advanced |
| Available as student elective? | Yes |
| Breadth Unit? | No |
Availability
Note
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Units are offered in attending mode unless otherwise indicated (that is attendance is required at the campus identified). A unit identified as offered by distance, that is there is no requirement for attendance, is identified with a nominal enrolment campus. A unit offered to both attending students and by distance from the same campus is identified as having both modes of study.
Special approval is required for enrolment into TNE Program units.
TNE Program units special approval requirements.
* 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).
Fees
Requisites
Prerequisites
Co-requisites
Mutual Exclusions
You cannot enrol in this unit as well as the following:
Teaching
| Teaching Pattern | 3 x 1hr face-to-face lectures, 1x1hr tutorial, 1x1-hr computer lab sessions. |
|---|---|
| Assessment | 3 class tests (46%), projects (54%) |
| Timetable | View the lecture timetable | View the full unit timetable |
Textbooks
| Required | |
|---|---|
| Recommended | Fränzi Korner-Nievergelt, Tobias Roth, Stefanie von Felten, Jérôme Guélat, Bettina Almasi, and Pius Korner-Nievergelt (2015) Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan. Academic Press. Claus Thorn Ekstrom (2017) The R primer. Second edition. CRC Press. |
The University reserves the right to amend or remove courses and unit availabilities, as appropriate.