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    Application of advanced statistical methods through case studies

    In real-world data analysis, a skilled statistician will utilise and adapt existing methodology to suit research goals at hand. This paper illustrates and provides background on a raft of specialised techniques in applied statistics, motivated by real case studies in statistics.

    About this paper

    Paper title Case Studies in Statistics
    Subject Statistics
    EFTS 0.1667
    Points 20 points
    Teaching period Semester 2 (On campus)
    Domestic Tuition Fees ( NZD ) $1,240.75
    International Tuition Fees Tuition Fees for international students are elsewhere on this website.
    STAT 401 or (STAT 270 and STAT 310)

    Enrolments for this paper require departmental permission.
    Pre-requisites are STAT 401, or STAT 270 and STAT 310, or equivalent.


    Teaching staff

    Dr Tilman Davies

    Professor Martin Hazelton

    Paper Structure

    Content and case studies form 5 to 6 modules drawn from:

    • Univariate smoothing
    • Time series
    • Multivariate smoothing
    • Spatial regression
    • Generalised additive models
    • Non-parametric testing
    Teaching Arrangements

    Lectures (2 per week); practicals/tutorials (1 per fortnight); student seminars (1 per fortnight).


    Recommended reading:

    A full course book/reader will be provided.

    Graduate Attributes Emphasised
    Interdisciplinary perspective, Lifelong learning, Scholarship, Communication, Critical thinking, Ethics, Information literacy, Research, Self-motivation.
    View more information about Otago's graduate attributes.
    Learning Outcomes

    Students successfully completing this course will be able to demonstrate the following:

    • Understanding of the relationship between theory and application of specialized statistical techniques
    • Identification of research question and ability to adapt statistical methods to problems without standard solutions
    • Ability to apply methodology and statistical computing to analyse data using specialized techniques, and interpret results in a logical manner
    • Display autonomy and judgement in presenting results to others, including non-scientists


    Semester 2

    Teaching method
    This paper is taught On Campus
    Learning management system
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