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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||Semester 1 (On campus)|
|Domestic Tuition Fees (NZD)||$1,610.82|
|International Tuition Fees||Tuition Fees for international students are elsewhere on this website.|
- 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. Your analysis will be written up 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 for Primary Industries (formerly 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!