Program Overview

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Course Overview

The Mathematical Methods for Applied Statistics course, denoted as STAT 391, is designed to equip students with the mathematical techniques necessary for drawing correct conclusions from well-chosen statistical models. This includes the construction and maximization of likelihoods, analysis of experimental data, linear models, and an exploration of probability along with several probability distributions.

Course Details

• Dates: 23 February 2026 to 21 June 2026
• Starts: Trimester 1
• Fees:
  • NZ$953.25 for domestic students
  • NZ$5,058.00 for international students
• Lecture Start Times:
  • Monday 12.00pm
  • Tuesday 12.00pm
  • Wednesday 12.00pm
  • Friday 12.00pm
• Campus: Kelburn
• Estimated Workload: Approximately 150 hours or 8.8 hours per week for 17 weeks
• Points: 15

Entry Restrictions

Prerequisites

• STAT 292

Corequisites

• None

Restrictions

• MATH 243
• The pair (ENGR 122/MATH 142, MATH 251)

Taught By

The School of Mathematics and Statistics — Faculty of Science and Engineering

Disclaimer

This course outline may be subject to change.

Key Dates

Important dates, including mid-trimester teaching breaks, can be found on the University's key dates calendar. Assessment dates will be communicated once the course has begun.

About This Course

Topics covered will include:

• Introduction to R
• Random variables and probability distributions
• Differentiation
• Maximum likelihood estimation
• Optimisation
• Integration
• Cumulative and marginal distribution functions
• Expectation
• Matrices
• General linear models The statistical software R will be used throughout the course.

Course Learning Objectives

Students who pass this course should be able to:

1. Recognise and derive properties of probability distributions of several discrete and continuous random variables.
2. Understand and apply basic concepts of differentiation, optimisation, integration, and matrix algebra to statistics problems.
3. Understand and derive likelihood functions.
4. Use the R software package to evaluate properties of probability distributions, optimise likelihood functions, and evaluate linear models using matrix algebra.

How This Course Is Taught

The course is designed for in-person study, with three 50-minute lectures and one 50-minute tutorial per week. Attendance at lectures on campus is strongly recommended, as tests will only be available in person.

Assessment

• Exam Mark: 40%
• Assignments 1, 2, 3, 4: 7.5% each, totaling 30%
• Test 1: 30%

Mandatory Requirements

To pass this course, students must:

1. Achieve an overall pass mark of at least 50%.
2. Achieve an average of at least 40% over the test and exam.

Lecture Times and Rooms

Lecture times are from 23 February 2026 to 5 April 2026 and from 20 April 2026 to 31 May 2026.

What You'll Need to Get

• The statistical software R, which is free to download.
• A scientific calculator for tutorials, assignments, and the test/exam. Only silent non-programmable calculators or silent programmable calculators with their memories cleared are permitted.

Staff

• Dr. John Haywood: Course Coordinator
• David Cox: Lecturer
• Dr. Louise McMillan: Lecturer