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.
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.
|Unit name||Statistical Analysis Using R|
|Faculty/School||College of Sciences and Engineering
School of Natural Sciences
Barbara Holland, Michael Charleston
|Available as student elective?||Yes|
|Location||Study period||Attendance options||Available to|
|Hobart||Intensive Session Jun||On-Campus||International||Domestic|
- International students
- Domestic students
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.
|Study Period||Start date||Census date||WW date||End date|
|Intensive Session Jun||28/6/2021||2/7/2021||8/7/2021||18/7/2021|
* 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 2021 are indicative and subject to change. Finalised census dates for 2021 will be available from the 1st October 2021.
|Band||CSP Student Contribution||Full Fee Paying (domestic)||Field of Education|
|2||2020: $1,190.00||2020: $2,402.00||010103|
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.
Students must be enrolled in a PhD, Masters or Honours
You cannot enrol in this unit as well as the following:
On-campus component - 8 days (over 3 weeks) 2-3hr lecture & 2-3hr practical, self study before and after on-campus sessions.
2 assignments worth 100%: portfolio exercises (60%), project report (40%)
|Timetable||View the lecture timetable | View the full unit timetable|
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.
Booktopia textbook links
The University reserves the right to amend or remove courses and unit availabilities, as appropriate.