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WILM404 Data Analysis for Wildlife Management

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 lack an appropriate background in statistics and data analysis.

Paper title Data Analysis for Wildlife Management
Paper code WILM404
Subject Wildlife Management
EFTS 0.1667
Points 20 points
Teaching period First Semester
Domestic Tuition Fees (NZD) $1,476.30
International Tuition Fees (NZD) $5,151.03

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Restriction
ZOOL 316
Limited to
PGDipWLM, MWLM
Notes
Approval from the Head of Department of Zoology is required for non-PGDipWLM students.
Contact
zoology@otago.ac.nz
Teaching staff
Dr Christoph Matthaei
Dr Travis Ingram
Associate Professor Michael Paulin
Paper Structure
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)
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.
Textbooks
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.
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!

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Timetable

First Semester

Location
Dunedin
Teaching method
This paper is taught On Campus
Learning management system
None

Computer Lab

Stream Days Times Weeks
Attend one stream from
X1 Thursday 12:00-12:50 9-15, 17-22
X2 Thursday 12:00-12:50 9-15, 17-22
AND one stream from
Z1 Monday 11:00-11:50 9-15, 17-22
Z2 Monday 11:00-11:50 9-15, 17-22

Lecture

Stream Days Times Weeks
Attend
L1 Monday 10:00-10:50 9-15, 17-22
Thursday 11:00-11:50 9-15, 17-22

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
Paper code WILM404
Subject Wildlife Management
EFTS 0.1667
Points 20 points
Teaching period First Semester
Domestic Tuition Fees Tuition Fees for 2018 have not yet been set
International Tuition Fees Tuition Fees for international students are elsewhere on this website.

^ Top of page

Restriction
ZOOL 316
Limited to
PGDipWLM, MWLM
Notes
Approval from the Head of Department of Zoology is required for non-PGDipWLM students.
Contact
zoology@otago.ac.nz
Teaching staff
Dr Christoph Matthaei
Dr Stephanie Godfrey
Associate Professor Michael Paulin
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 need some data and you need 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.
Textbooks
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!

^ Top of page

Timetable

First Semester

Location
Dunedin
Teaching method
This paper is taught On Campus
Learning management system
None

Computer Lab

Stream Days Times Weeks
Attend one stream from
X1 Thursday 12:00-12:50 9-13, 15-22
X2 Thursday 12:00-12:50 9-13, 15-22
AND one stream from
Z1 Monday 11:00-11:50 9-13, 15-22
Z2 Monday 11:00-11:50 9-13, 15-22

Lecture

Stream Days Times Weeks
Attend
L1 Monday 10:00-10:50 9-13, 15-22
Thursday 11:00-11:50 9-13, 15-22