STATS 7008 - Statistics Topic D
North Terrace Campus - Semester 2 - 2015
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General Course Information
Course Details
Course Code STATS 7008 Course Statistics Topic D Coordinating Unit Statistics Term Semester 2 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Available for Study Abroad and Exchange Y Course Staff
Course Coordinator: Associate Professor Inge Koch
Course Timetable
The full timetable of all activities for this course can be accessed from .
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Learning Outcomes
Course Learning Outcomes
In 2014, the topic of this course will be Analysis of Multivariate and High-Dimensional data.
Syllabus. This course will introduce students to the analysis of multivariate and high-dimensional data. Multivariate analysis of data is performed in order to (1) understand the structure in data and summarise the data in simpler ways; (2) understand the relationship of one part of the data to another part; and (3) make decisions or draw inferences based on data. We consider a number of classical and more recent methods for analysing data and start with the three basic methods Principal Component Analysis, Canonical Correlation Analysis, and Discriminant Analysis (Classification), which provide answers to the three criteria above while reducing the dimension and complexity of the data. In the second part we learn about Cluster Analysis with a range of methods which divide the data into meaningful groups, and Factor Analysis which is related to Pricipal Component Analysis but focusses more on decompositions of the covariance matrix and often assumes that the data are normal. The final part includes introductions to the recent the methods Independent Component Analysis, and Projection Pursuit which both pursue similar goals as Factor Analysis but without assuming that the data are normal. Each topic will combine theory with its application to real data, using the software Matlab for the data analysis. Pre-requisites. Students are assumed to have attained at least a credit in Mathematical Statistics III (STATS 3006) and Statistical Modelling III (STATS 3001), or have equivalent knowledge. In addition, students are assumed to be familiar with matrix algebra (positive definite matrices, eigenvectors, eigenvalues, singular value decomposition).
Course Learning Objectives
Students who successfully complete the course should be able to:
1. Demonstrate understanding of advanced principles of the analysis of multivariate data.
2. Demonstrate skills in modern statistical dimension reduction methods using Matlab.
3. Demonstrate understanding of the interplay between clustering and the decision making in classification.
4. Understand the theoretical underpinnings of the multivariate methods of data analysis.
University Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
University Graduate Attribute Course Learning Outcome(s) Knowledge and understanding of the content and techniques of a chosen discipline at advanced levels that are internationally recognised. 1,2,3,4,5,6 The ability to locate, analyse, evaluate and synthesise information from a wide variety of sources in a planned and timely manner. 2,3,4,5 An ability to apply effective, creative and innovative solutions, both independently and cooperatively, to current and future problems. 3,5 Skills of a high order in interpersonal understanding, teamwork and communication. 2,5,6 A proficiency in the appropriate use of contemporary technologies. 1,2,3,5 A commitment to continuous learning and the capacity to maintain intellectual curiosity throughout life. 1,2,3,4,5,6 A commitment to the highest standards of professional endeavour and the ability to take a leadership role in the community. 3,5,6 An awareness of ethical, social and cultural issues within a global context and their importance in the exercise of professional skills and responsibilities. 5,6 -
Learning Resources
Required Resources
I. Koch: Analysis of Multivariate and High-Dimensional Data, Cambridge University Press, 2014.Recommended Resources
R. A. Johnson and D. W. Wichern (2007): Applied Multivariate Statistical Analysis.
K. V. Mardia, J. T. Kent and J. M. Bibby (1992): Multivariate Analysis.
For additional material on statistical learning, see T. Hastie, R. Tibshirani and J. Friedman (2009): The Elements of Statistical Learning.Online Learning
Assignments and other materials will be made available electronically to students. -
Learning & Teaching Activities
Learning & Teaching Modes
This course will be delivered through 30 presented lectures over the semester. There will be six written assignments and a test in Week 9 to provide students with the opportunity to gauge their progress.Workload
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Activity Quantity Workload hours Lectures 30 90 Assignments 6 40 Presentation 1 10 Test 1 16 Total 156 Learning Activities Summary
Lecture Outline
Principal Component Analysis (lectures 1-6)
Canonical Correlation Analysis (lectures 7-11)
Discriminant Analysis (lectures 12-16)
Cluster Analysis (lectures 17-21)
Factor Analysis (lectures 22-25)
Independent Component Analysis (lectures 26-30) -
Assessment
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment Summary
Activity Weighting Objectives assessed Assignments 20% all Presentation 10% all Test 0% or 20% all Examination 50% or 70% all
If a student's exam mark is higher than the test mark, then the test will not factor into the course mark and the weighting of the exam will be 70%. If the exam mark is lower than the test mark, then the weighting of the test will be 20% and the exam 50%.Assessment Related Requirements
A final aggregate score of at least 50% is required to pass the course.Assessment Detail
Assessment item Distributed Due Weighting Assignment 1 Week 1 Week 3 4% Assignment 2 Week 3 Week 5 4% Assignment 3 Week 5 Week 7 4% Assignment 4 Week 7 Week 9 4% Assignment 5 Week 9 Week 11 4% Assignment 6 Week 11 Week 13 4% Individual presentation 10% Test Week 9 20% Exam Exam period 70%
The best 5 assignments only count adding up to a total of 20%.
The individual project will be done throughout the course.
The mark resulting from the test and exam will be 70% and will be the maximum of (exam only, 50% exam + 20% test).
Submission
1. All written assignments are to be submitted electronically to the lecturer or to the designated hand-in boxes in the School of Mathematical Sciences, with a signed cover sheet attached.
2. Late assignments will not be accepted unless with by prior agreement with the lecturer. Please discuss delays owing to medical or compassionate reasons with the lecturer.
3. Marked assignments will usually be returned to students within two weeks of submission.Course Grading
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 1-49 Fail P 50-64 Pass C 65-74 Credit D 75-84 Distinction HD 85-100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through .
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Student Feedback
The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
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Student Support
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- Academic Support with writing and study skills
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- Student Life Counselling Support - Personal counselling for issues affecting study
- Students with a Disability - Alternative academic arrangements
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Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangements Policy
- Academic Integrity Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs Policy
- Copyright Compliance Policy
- Coursework Academic Programs Policy
- Intellectual Property Policy
- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment Policy
- Reasonable Adjustments to Learning, Teaching & Assessment for Students with a Disability Policy
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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