STATS 302 : Applied Multivariate Analysis
Science
2021 Semester One (1213) (15 POINTS)
Course Prescription
Course Overview
Analysis of data with more than one response variable, including high dimensional cases with hundreds or thousands of responses. The course focuses on the use of R software and the interpretation and presentation of results. Illustrated using data sets from biology, environmental science, finance, sociology, and other areas. This course is suitable for both majors and non-majors. In particular, it is an accessible follow-on from stats 20x for non-stats majors wanting to develop their quantitative skills, particularly those interested in biology, ecology, chemistry, finance, or marketing.
Course Requirements
Capabilities Developed in this Course
| Capability 1: | Disciplinary Knowledge and Practice |
| Capability 2: | Critical Thinking |
| Capability 3: | Solution Seeking |
| Capability 4: | Communication and Engagement |
Learning Outcomes
- Select an appropriate multivariate procedure based on a description of the data and questions of interest, and justify the choice made. (Capability 3)
- Perform correctly the multivariate procedures in a software package, and write justifications for choices made in the course of the analysis. (Capability 1)
- Identify the assumptions of their analysis, and means of checking these assumptions. (Capability 2)
- Produce visualisations of multivariate data, including appropriate labels and captions. (Capability 4)
- Summarise the conclusions of a multivariate procedure in writing, using non-technical language. (Capability 4)
Assessments
| Assessment Type | Percentage | Classification |
|---|---|---|
| Quizzes | 10% | Individual Coursework |
| Assignments | 20% | Individual Coursework |
| Test | 20% | Individual Test |
| Final Exam | 50% | Individual Examination |
| 4 types | 100% |
| Assessment Type | Learning Outcome Addressed | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||||||
| Quizzes | ||||||||||
| Assignments | ||||||||||
| Test | ||||||||||
| Final Exam | ||||||||||
50% on the final exam is required.
Key Topics
Exploratory and descriptive techniques including principal components, canonical variates, clustering, and multidimensional scaling; with associated visualizations. Non-Euclidian distances and their uses. Inference for differences between groups, and associations between groups of variables, using both permutation testing and parametric methods. Methods for dealing with multiple testing including control of the false discovery rate.
Workload Expectations
This course is a standard 15 point course and students are expected to spend 150 hours over the course of the semester for each 15 point course that they are enrolled in.
For this course, in a typical week you can expect 3 hours of lectures, 3 hours of reading and thinking about the content and 4 hours of work on assignments and/or test preparation.
Delivery Mode
Campus Experience
Attendance is required at scheduled activities including the mid semester test to receive credit for components of the course.
Lectures will be available as recordings. Other learning activities including zoom office hour will be available as recordings.
The course will include live online events including zoom office hours.
Attendance on campus is required for the test and exam.
The activities for the course are scheduled as a standard weekly timetable.
Learning Resources
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.
Digital 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.
Academic Integrity
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.
Copyright
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 http://disability.auckland.ac.nz
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 https://www.auckland.ac.nz/en/students/academic-information/exams-and-final-results/during-exams/aegrotat-and-compassionate-consideration.html.
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 your assessment is fair, and not compromised. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator, and if disruption occurs you should refer to the University Website for information about how to proceed.
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 https://www.auckland.ac.nz/en/students/forms-policies-and-guidelines/student-policies-and-guidelines/student-charter.html.
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 you may be asked to submit your 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. The final decision on the completion mode for a test or examination, and remote invigilation arrangements where applicable, will be advised to students at least 10 days prior to the scheduled date of the assessment, or in the case of an examination when the examination timetable is published.