Red X iconGreen tick iconYellow tick icon


    Theory and methods of statistics, with applications.

    Develops theoretical knowledge and computation skills required for an in-depth understanding of modern applied statistical methods.

    An ideal paper for someone who wants to 'get under the hood' and really understand the tools they have used previously, and how this extends to more complex methods and models.

    About this paper

    Paper title Applied Statistical Methods and Models
    Subject Statistics
    EFTS 0.1667
    Points 20 points
    Teaching period Semester 1 (On campus)
    Domestic Tuition Fees ( NZD ) $1,240.75
    International Tuition Fees Tuition Fees for international students are elsewhere on this website.
    (STAT 210 and STAT 260) or (HASC 413 and HASC 415)

    Teaching staff

    Dr Peter Dillingham 

    Dr Xun Xiao 


    Recommended reading:

    • Rice, J. A. (2006). Mathematical statistics and data analysis. Nelson Education. (3rd edition)
    • Faraway, J. J. (2016). Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models. Chapman and Hall/CRC
    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 probability, distributions, and how this informs data analysis
    • Knowledge of different paradigms for statistical inference
    • Understanding of the relationship between theory and some of the most commonly used statistical modelling techniques, especially regression
    • Ability to develop methods for problems without standard solutions using maximum likelihood
    • Ability to apply methodology and statistical computing to analyse data using advanced regression techniques, and interpret results in a logical manner
    • Autonomy and judgement in presenting results to others, including non-scientists


    Semester 1

    Teaching method
    This paper is taught On Campus
    Learning management system

    Computer Lab

    Stream Days Times Weeks
    A1 Friday 15:00-15:50 9-12, 15-22
    Friday 16:00-16:50 9-12, 15-22


    Stream Days Times Weeks
    A1 Tuesday 15:00-16:50 9-13, 15-22
    Back to top