STATS 760 A Survey of Modern Applied Statistics - Course Outlines

Course Prescription

A survey of techniques from modern applied statistics. Topics covered will be linear, non-linear and generalised linear models, modern regression including CART and neural networks, mixed models, survival analysis, time series and spatial statistics.

Course Overview

The aim of this course is twofold: to introduce students to new statistical techniques and give them experience in finding out details of statistical techniques that are unfamiliar. This course is designed to give them practice in teaching themselves new techniques. As such, students will present findings to their peers to get practice at this. The course is based on the books Statistical Models in S by Chambers and Hastie (C&H), Modern Applied Statistics with S-Plus by Venables and Ripley (V&R), and The Elements of Statistical Learning by Hastie, Tibshirani and Friedman (HT&F). Students work through selected chapters of the books, at their own pace. Students are assigned 5 topics, three of these will be compulsory, and they can choose two others. The compulsory ones (together with the relevant books) are: - Linear and generalized linear models (C&H, V&R) - Modern regression and classification techniques (V&R, HT&F) Gams, regression trees, neural networks, smoothing, nearest neighbours, boosting and bagging, random forests, support vector machines. - Unsupervised Learning and Visualization (V&R, HT&F), K means, hierarchical clustering, Self organizing maps. The two topics are chosen from: Plotting and graphical displays including animation, Mixed models, Multivariate analysis, Survival analysis, Time series analysis, Spatial statistics.

Course Requirements

Prerequisite: STATS 310, 330

Learning Outcomes

By the end of this course, students will be able to:

An appreciation of why the technique is important for the problem at hand. (Capability 2 and 3)
Demonstrate an understanding of the appropriate modelling procedure, outlining how this is driven by the aims of the analysis. (Capability 1 and 6)
Demonstrate an understanding of and appraise the types of data the the technique requires. (Capability 3)
Demonstrate an understanding of the data formats required for using various techniques. (Capability 3)
Communicate how the techniques relate to other techniques and what’s their utility. (Capability 2 and 4)

Assessments

Assessment Type	Percentage	Classification
Journals/meetings/seminars	20%	Individual Coursework
Oral exam	20%	Individual Coursework
Presentation	20%	Group & Individual Coursework
Test	20%	Individual Coursework
Assignments	20%	Individual Coursework
5 types	100%

Assessment Type	Learning Outcome Addressed
	1	2	3	4	5
Journals/meetings/seminars
Oral exam
Presentation
Test
Assignments

Learning Resources

Linear and generalized linear models: (C&H, V&R)

Modern regression and classification techniques (V&R, HT&F) Gams, regression trees, neural networks, smoothing, nearest neighbours, boosting and bagging, random forests, support vector machines. Unsupervised Learning and Visualization (V&R, HT&F), K means, hierarchical clustering, Self organizing maps.

Special Requirements

Student must attend all classes (compulsory unless they ave a good excuse) and engage in the class discussion and tutorial meetings. they must show the ability to undertake self-directed research.

Workload Expectations

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 a total of 12 hours of lectures, 60 hours of reading and thinking about the content and 48 hours of work on assignments and/or test preparation.

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.

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.

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.

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 at 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.

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.

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.

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.

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

STATS 760 : A Survey of Modern Applied Statistics

Science

2020 Semester One (1203) (15 POINTS)