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STAT401 Applied Statistical Methods and Models

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.

Paper title Applied Statistical Methods and Models
Paper code STAT401
Subject Statistics
EFTS 0.1667
Points 20 points
Teaching period Semester 1 (On campus)
Domestic Tuition Fees (NZD) $1,206.91
International Tuition Fees Tuition Fees for international students are elsewhere on this website.

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(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

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Semester 1

Teaching method
This paper is taught On Campus
Learning management system

Computer Lab

Stream Days Times Weeks
A1 Wednesday 16:00-16:50 11-14, 16-22
Friday 16:00-16:50 9-10


Stream Days Times Weeks
A1 Tuesday 15:00-16:50 9-14, 16, 18-22
Wednesday 15:00-15:50 11-14, 16-22
Friday 15:00-15:50 9-10