Due to COVID-19 restrictions, a selection of on-campus papers will be made available via distance and online learning for eligible students.
Find out which papers are available and how to apply on our COVID-19 website
Uses real biological examples and computers, and deals with types of data and their acquisition; graphical and exploratory analysis; estimation and hypothesis testing; experimental design; computer-intensive methods and simulation.
This paper covers experimental design and data analysis techniques widely used in the biological sciences, taught using the free software R.
|Paper title||Biological Data Analysis and Computing|
|Teaching period||Semester 1 (On campus)|
|Domestic Tuition Fees (NZD)||$1,110.75|
|International Tuition Fees||Tuition Fees for international students are elsewhere on this website.|
- (STAT 110 or STAT 115) and 54 200-level points from Science Schedule C
- WILM 404
- Schedule C
- Teaching staff
- Paper Structure
- 24 lectures (30-45 minutes), with 24 corresponding tutorials (60-75 minutes) involving hands-on programming and data analysis using R, which are taught in computer labs (assisted by student demonstrators).
- Teaching Arrangements
- The first course module (3 lecture/tutorial sessions) provides an in-depth training
in experimental design.
The second course module (9 sessions in total) covers fundamental statistical issues (4 sessions), simple analyses (2 sessions) and complex analyses (3 sessions).
Modules 3 and 4 (12 sessions in total) cover mainly intermediate and advanced techniques complementing Module 2.
- Quinn, G.P. and Keough, M.J. (2002) Experimental Design and Data Analysis for Biologists.
Cambridge University Press, Cambridge, UK.
Whitlock, M.C. and Schluter, D. (2009) The Analysis of Biological Data. Roberts & Co. Publishers, Colorado, USA.
Kruschke, J.K. (2011) Doing Bayesian Data Analysis: A tutorial with R and BUGS. Elsevier.
- Graduate Attributes Emphasised
- Interdisciplinary perspective, Lifelong learning, Scholarship, Critical thinking,
Information literacy, Research.
View more information about Otago's graduate attributes.
- Learning Outcomes
- Students will gain an understanding of key issues related to experimental design and
data analysis. They will also learn to use the free software R to conduct a range
of analyses (from basic to complex).
Specific aims of the paper include:
- Helping you design field or laboratory experiments
- Helping you gather, present and interpret biological data
- Enabling you to make recommendations and decisions about biological systems
- Giving you a foundation for understanding, critically evaluating and using statistical data
- Building up your knowledge slowly and thoroughly
- Hopefully, helping reduce a possible fear or dislike of stats!