# STATS 766 : Multivariate Analysis

## Science

### Course Prescription

A selection of topics from multivariate analysis, including: advanced methods of data display (e.g., Correspondence and Canonical Correspondence Analysis, Biplots, and PREFMAP) and an introduction to classification methods (e.g., various types of Discriminant Function Analysis).

### Course Overview

STATS 766 focuses on the analysis of data with several response variables using modern computational techniques. Most data sets collected in disciplines such as finance, medicine, sociology, and ecology are of a multivariate nature (assets in stock markets, survival times of twins, consumer preferences, occurrence of species) and only a joint analysis using multivariate statistical tools will provide an adequate modelling and understanding.
This course will introduce the classical multivariate statistical techniques (principal component analysis, discriminant analysis, canonical correlation analysis, correspondence analysis, factor analysis, multidimensional scaling) based on the multivariate normal distribution theory. We will furthermore look at modern approaches to dependence modelling using copulas.
The main two course objectives are to obtain a sound understanding of the mathematical techniques underlying multivariate statistical methods and to enable practical statistical data analyses with relevant examples taken from finance, biostatistics, and ecology. Students will be expected to have some experience with R (or SAS or similar software). The necessary matrix algebra concepts will be introduced in lectures and tutorial sessions.

### Course Requirements

Prerequisite: STATS 302 or 767

### Capabilities Developed in this Course

 Capability 1: Disciplinary Knowledge and Practice Capability 2: Critical Thinking Capability 3: Solution Seeking Capability 4: Communication and Engagement Capability 5: Independence and Integrity Capability 6: Social and Environmental Responsibilities

### Learning Outcomes

By the end of this course, students will be able to:
1. Understand and apply computations on data matrices using matrix algebra (Capability 1, 2 and 3)
2. Understand and critically evaluate descriptive multivariate techniques (Capability 1, 2, 3 and 4)
3. Demonstrate an understanding of the theory of multivariate distributions, estimation and hypothesis testing (Capability 1, 2, 3, 4 and 5)
4. Explain and apply multivariate statistical techniques in practical case studies (Capability 1, 2, 3, 4, 5 and 6)

### Assessments

Assessment Type Percentage Classification
Assignments 20% Individual Coursework
Tutorials 10% Individual Coursework
Test 10% Individual Test
Final Exam 60% Individual Examination
1 2 3 4
Assignments
Tutorials
Test
Final Exam
A minimum of 50% in the exam along with a minimum of 50% overall is required to pass

### Key Topics

Matrix algebra, multivariate distributions, theory of the multinormal distribution, principal component analysis, discriminant analysis, canonical correlation analysis, correspondence analysis, factor analysis, multidimensional scaling, cluster analysis, dependence modelling using copulas

### Special Requirements

NA

This course is a standard 15 point course and students are expected to spend 10 hours per week involved in each 15 point course that they are enrolled in.

For this course, you can expect 2 hours of lectures, a 1 hour tutorial, 2 hours of reading and thinking about the content and 5 hours of work on assignments and/or test preparation.

### Delivery Mode

#### Campus Experience

Attendance is expected at scheduled activities including tutorials to receive credit for components of the course.
Lectures will be available as recordings. Tutorials will not be available as recordings but worked solutions will be provided.
Attendance on campus is required for the test and the exam.
The activities for the course are scheduled as a standard weekly timetable.

### Learning Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.

### Student Feedback

During the course Class Representatives in each class can take feedback to the staff responsible for the course and staff-student consultative committees.

At the end of the course students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback.

Your feedback helps to improve the course and its delivery for all students.

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed against online source material using computerised detection mechanisms.

### Class Representatives

The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.

You may copy the course content for the purposes of private study or research, but you may not upload onto any third party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.

### Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website

### Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page .

This should be done as soon as possible and no later than seven days after the affected test or exam date.

### Learning Continuity

In the event of an unexpected disruption, we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and course assessment continues to meet the principles of the University’s assessment policy. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator/director, and if disruption occurs you should refer to the university website for information about how to proceed.

The delivery mode may change depending on COVID restrictions. Any changes will be communicated through Canvas.

### Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter .

### Disclaimer

Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course students may be asked to submit coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. In exceptional circumstances changes to elements of this course may be necessary at short notice. Students enrolled in this course will be informed of any such changes and the reasons for them, as soon as possible, through Canvas.

Published on 11/11/2021 09:30 a.m.