Courses & Units
Data Handling and Statistics 3 KMA353
Introduction
Data Handling and Statistics 3 is the third applied statistics units offered by the School of Natural Sciences (Mathematics). This unit is required in the Statistics and Decision Science major and 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, frequentist and Bayesian 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 producing reproducible research. Examples will be drawn from the biological, physical and social sciences.
Summary
| Unit name | Data Handling and Statistics 3 |
|---|---|
| Unit code | KMA353 |
| Credit points | 12.5 |
| College/School | College of Sciences and Engineering School of Natural Sciences |
| Discipline | Mathematics |
| Coordinator | Doctor Shane Richards |
| Available as an elective? | Yes |
| Delivered By | University of Tasmania |
| Level | Advanced |
Availability
| Location | Study period | Attendance options | Available to | ||
|---|---|---|---|---|---|
| Hobart | Semester 1 | On-Campus | International | Domestic | |
Key
- On-campus
- Off-Campus
- International students
- Domestic students
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.
Key Dates
| Study Period | Start date | Census date | WW date | End date |
|---|---|---|---|---|
| Semester 1 | 26/2/2024 | 22/3/2024 | 15/4/2024 | 2/6/2024 |
* 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 2024 are indicative and subject to change. Finalised census dates for 2024 will be available from the 1st October 2023. Note census date cutoff is 11.59pm AEST (AEDT during October to March).
Learning Outcomes
- Describe statistical approaches commonly adopted in the sciences.
- Apply statistical methods using the R programming language.
- Evaluate the validity of a statistical analysis.
- Interpret a statistical analysis in written format
Fee Information
The 2024 Commonwealth Supported Place (CSP) rates are still being finalised by the Government and we will update the domestic fee information as soon as we have more details.
Requisites
Prerequisites
KMA253Teaching
| Teaching Pattern | Each week students will engage in 3 x 1-hr face-to-face recorded lectorials, and a 1 x 1-hr face-face computer lab session (not recorded). |
|---|---|
| Assessment | Tests (46%)|Projects (54%) |
| Timetable | View the lecture timetable | View the full unit timetable |
Textbooks
| Required |
Required readings will be listed in the unit outline prior to the start of classes. |
|---|---|
| Recommended | Korner-Nievergelt, F., Roth, T., von Felten, S., Guélat, J., Almasi, B., Korner-Nievergelt, P. (2015) Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan. Academic Press. | Links | Booktopia textbook finder |
The University reserves the right to amend or remove courses and unit availabilities, as appropriate.