The statistical analysis of real biological data. Graphical and exploratory analysis, estimation and hypothesis testing, experimental design, simulation. Project work consisting of analysis of a real data set.
For Wildlife Management students who would like to improve and broaden their skills in statistics and data analysis.
|Paper title||Data Analysis for Wildlife Management|
|Teaching period||First Semester|
|Domestic Tuition Fees (NZD)||$1,566.65|
|International Tuition Fees (NZD)||$5,793.66|
- ZOOL 316
- Limited to
- PGDipWLM, MWLM
- Approval from the Head of Department of Zoology is required for non-PGDipWLM students.
- Teaching staff
- Paper Structure
Lectures and tutorials: 24 lectures (30-45 minutes), with 24 corresponding tutorials (60-75 minutes) involving hands-on programming and data analysis using R taught in computer labs (assisted by student demonstrators).
WILM 404 involves taking the ZOOL 316 course (attending all lectures/tutorials and handing in four assignments) plus doing an additional project in which you analyse a biological dataset. You write up your analysis in a report, formatted like a manuscript to be submitted to a wildlife management journal. To begin this project, you will need a suitable dataset and some research question(s). These questions should be about wildlife management and the data should be relevant to these questions. You may bring a dataset of your own or obtain data from a researcher in the Department of Zoology, another department at this university, or another organisation such as the Department of Conservation or the Ministry of Fisheries.
- 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 to:
- Help you design field or laboratory experiments
- Help you gather, present and interpret biological data
- Enable you to make recommendations and decisions about biological systems
- Give you a foundation for understanding, critically evaluating and using statistical data
- Build up your knowledge slowly and thoroughly
- Hopefully, help reduce a possible fear or dislike of stats!