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    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.

    About this paper

    Paper title Biological Data Analysis and Computing
    Subject Zoology
    EFTS 0.15
    Points 18 points
    Teaching period Semester 1 (On campus)
    Domestic Tuition Fees ( NZD ) $1,173.30
    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

    Professor Christoph Matthaei
    Dr Stephanie Godfrey
    Dr Ludovic Dutoit

    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!


    Semester 1

    Teaching method
    This paper is taught On Campus
    Learning management system

    Computer Lab

    Stream Days Times Weeks
    A1 Monday 11:00-11:50 9-13, 15-22
    Thursday 12:00-12:50 9-13, 15-16, 18-22


    Stream Days Times Weeks
    A1 Monday 10:00-10:50 9-13, 15-22
    Thursday 11:00-11:50 9-13, 15-16, 18-22
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