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    Overview

    A continuation of the theoretical development begun in STAT270, this paper will cover the theory of ordinary least squares, maximum likelihood estimation and inference, hypothesis testing, and Bayesian inference.

    This course continues to develop theory for making inference from data that was introduced in STAT 270 (or STAT 261). The tools developed by statisticians for analysing data have become a major factor in the advancement of scientific knowledge. Why are these tools so useful? The reason is that they are based on an agreed system of mathematical and statistical reasoning. In order to be confident that the methods used by a statistician are reliable we need an understanding of this theory.

    About this paper

    Paper title Statistical Inference
    Subject Statistics
    EFTS 0.15
    Points 18 points
    Teaching period Semester 2 (On campus)
    Domestic Tuition Fees ( NZD ) $981.75
    International Tuition Fees Tuition Fees for international students are elsewhere on this website.
    Prerequisite
    MATH 140 and (STAT 261 or STAT 270)
    Restriction
    STAT 362
    Schedule C
    Arts and Music, Science
    Eligibility

    Students should have completed both STAT 270 (or STAT 261) and MATH 140 (or MATH 170).

    Contact

    ting.wang@otago.ac.nz

    Teaching staff

    Associate Professor Ting Wang

    Dr Conor Kresin

    Paper Structure

    Main topics:

    • The general linear model
    • The likelihood function
    • Bayesian inference
    • Maximum likelihood estimation
    • Hypothesis testing using the likelihood function
    • Model selection using the likelihood function
    Textbooks

    Textbooks are not required for this paper.

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

    Students who successfully complete the paper will develop an understanding of key concepts in mathematical statistics.

    Timetable

    Semester 2

    Location
    Dunedin
    Teaching method
    This paper is taught On Campus
    Learning management system
    Other

    Lecture

    Stream Days Times Weeks
    Attend
    A1 Tuesday 11:00-11:50 29-35, 37-42
    Wednesday 11:00-11:50 29-35, 37-42
    Friday 11:00-11:50 29-35, 37-42

    Tutorial

    Stream Days Times Weeks
    Attend
    A1 Wednesday 15:00-15:50 29-35, 37-42
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