Hobart
You are expected to have access to a laptop with sufficient system requirements. It is recommended that you have a recent version of the R programming language (https://www.r-project.org) and RStudio (https://rstudio.com) installed before the unit begins.
Introduction
Statistics is the science of decision making and forms a key foundation of scientific research. This unit will introduce students to a broad range of quantitative data analysis techniques. Students will learn aspects of collecting, processing, analysing, and presenting, quantitative information. Topics include: experimental design, data exploration and presentation, fitting linear models and their extensions (e.g. generalised linear modelling, and mixed effects modelling), model selection, and inference. Students will gain hands-on experience conducting statistical analyses using the R programming language within the RStudio environment, including the use of R Markdown for promoting reproducible research. Examples will be drawn from the biological, physical and social sciences.
Summary 2021
| Unit name | Statistical Analysis Using R |
|---|---|
| Unit code | KMA711 |
| Credit points | 12.5 |
| Faculty/School | College of Sciences and Engineering School of Natural Sciences |
| Discipline | Mathematics |
| Coordinator | Shane Richards |
| Teaching staff | Barbara Holland, Michael Charleston |
| Level | Postgraduate |
| Available as student elective? | Yes |
| Breadth Unit? | No |
Availability
Note
Please check that your computer meets the minimum System Requirements if you are attending via Distance/Off-Campus.
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
Students must be enrolled in a PhD, Masters or Honours
Prerequisites
Mutual Exclusions
You cannot enrol in this unit as well as the following:
KMA253
Teaching
| Teaching Pattern | On-campus component - 8 days (over 3 weeks) 2-3hr lecture & 2-3hr practical, self study before and after on-campus sessions. |
|---|---|
| Assessment | 2 assignments worth 100%: portfolio exercises (60%), project report (40%) |
| 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.