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 present to postgraduate students, that are early in their research studies, a broad range of applied 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, Bayesian methods, and model 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. Students will benefit by having taken an introductory statistics unit as part of an earlier degree.
Summary
| Unit name | Statistical Analysis Using R |
| Unit code | KMA711 |
| Credit points | 12.5 |
| College/School | Sciences and Engineering School of Natural Sciences |
| Discipline | Mathematics |
| Coordinator | Doctor Shane Richards |
| Delivered By | University of Tasmania |
| Level | Postgraduate |
Availability
Specific information on 2027 unit availability will be available in August
Learning Outcomes
- Determine appropriate statistical analyses given the data at hand and the research question posed.
- Conduct statistical analyses using the computer software R, RStudio, and R markdown.
- Assess the validity of the statistical analyses using graphical and advanced techniques.
- Interpret and present statistical results in a written format appropriate for a scientific publication.
Fee Information
2027 fee information will be available in August.
Requisites
Mutual Exclusions
You cannot enrol in this unit as well as the following:
KMA353Teaching
| Teaching Pattern | 2 week x Monday, Tuesday, Thursday, and Friday lectorial sessions |
|---|---|
| Assessment | Report (40%)|Portfolio (60%) |
| 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. |
|---|
The University reserves the right to amend or remove courses and unit availabilities, as appropriate.