Course Title | Multivariate Analysis | ||
Course Code | MH4501 | ||
Offered | Study Year 4, Semester 2 | ||
Course Coordinator | PUN Chi Seng (Asst Prof) | cspun@ntu.edu.sg | 6513 7468 |
Pre-requisites | MH2500 and MH3500 and MH3510 | ||
AU | 4 | ||
Contact hours | Lectures: 39, Tutorials: 12, Laboratories: 6 | ||
Approved for delivery from | AY 2020/21 semester 2 | ||
Last revised | 24 Nov 2020, 10:17 |
This course focuses on the standard methods of multivariate statistical analysis. Many essential data analysis techniques, such as principal component analysis and discriminant analysis, will be covered. This course equips students with the necessary skills for being data analysts.
Upon successfully completing this course, you should be able to:
Multivariate Normal Distribution
Multivariate Inference
Multivariate Analysis of Variance
Principal Component Analysis
Factor Analysis
Canonical Correlation Analysis
Discriminant Analysis
Component | Course ILOs tested | SPMS-MAS Graduate Attributes tested | Weighting | Team / Individual | Assessment Rubrics |
---|---|---|---|---|---|
Continuous Assessment | |||||
Lectures | |||||
Assignment | 1, 2, 3 | 1. a, b, c, d 2. a, b, c 4. a 5. a | 15 | individual | See Appendix for rubric |
Mid-semester Quiz | |||||
Midterm Examination | 1, 2 | 1. a, b, c 2. a, b | 25 | individual | See Appendix for rubric |
Examination (2 hours) | |||||
Final Examination | 1, 2 | 1. a, b, c 2. a, b | 60 | individual | See Appendix for rubric |
Total | 100% |
These are the relevant SPMS-MAS Graduate Attributes.
1. Competence
a. Independently process and interpret mathematical theories and methodologies, and apply them to solve problems
b. Formulate mathematical statements precisely using rigorous mathematical language
c. Discover patterns by abstraction from examples
d. Use computer technology to solve problems, and to communicate mathematical ideas
2. Creativity
a. Critically assess the applicability of mathematical tools in the workplace
b. Build on the connection between subfields of mathematics to tackle new problems
c. Develop new applications of existing techniques
4. Civic-mindedness
a. Develop and communicate mathematical ideas and concepts relevant in everyday life for the benefits of society
5. Character
a. Act in socially responsible and ethical ways in line with the societal expectations of a mathematics professional, particularly in relation to analysis of data, computer security, numerical computations and algorithms
Through the assignments and the in-class discussion with students, the instructor will regularly give feedback to students on how they are learning in this course.
Lectures (39 hours) | Lectures provide systematic instruction of the course content. |
Tutorials (12 hours) | Tutorials and labs consist of practice questions and lab implementation related to the course content. As a result, they provide weekly feedback/knowledge check for the students. |
Laboratories (6 hours) | This will help to develop problem solving and computing skills, and reinforce the understanding of the concepts and notions. |
TEXT: Applied Multivariate Statistical Analysis, R. A. Johnson and D. W. Wichern, 6th, Pearson Prentice Hall, QA278.J68A, 2007. ISBN: 9780132326803.
REFERENCE: An Introduction to Multivariate Statistical Analysis, T.W. Anderson, Wiley-Interscience, QA278.A551, 2003. ISBN: 978-0-471-36091-9.
(1) General
Students are expected to attend all lectures and tutorials/labs punctually and complete and submit all assignments by due dates. Students are expected to take responsibility to follow up with course notes, assignments and course-related announcements.(2) Assignments
All assignments equally contribute to the CA1 (15% of total score). Late submissions will be subject to mark deduction:
Scenario 1: if the assignment is submitted late after the due date but before the solution is released, then 30% of the maximum mark will be deducted.
Scenario 2: if the assignment is submitted late after the solution is released, then it will be marked zero.
Good academic work depends on honesty and ethical behaviour. The quality of your work as a student relies on adhering to the principles of academic integrity and to the NTU Honour Code, a set of values shared by the whole university community. Truth, Trust and Justice are at the core of NTUâ€™s shared values.
As a student, it is important that you recognize your responsibilities in understanding and applying the principles of academic integrity in all the work you do at NTU. Not knowing what is involved in maintaining academic integrity does not excuse academic dishonesty. You need to actively equip yourself with strategies to avoid all forms of academic dishonesty, including plagiarism, academic fraud, collusion and cheating. If you are uncertain of the definitions of any of these terms, you should go to the Academic Integrity website for more information. Consult your instructor(s) if you need any clarification about the requirements of academic integrity in the course.
Instructor | Office Location | Phone | |
---|---|---|---|
PUN Chi Seng (Asst Prof) | SPMS-MAS-05-22 | 6513 7468 | cspun@ntu.edu.sg |
Week | Topic | Course ILO | Readings/ Activities |
---|---|---|---|
1 | Introduction of Multivariate Analysis and review of Matrix Algebra | 1 | TEXT Chapter 1 |
2 | Population and Sample Statistics | 1 | TEXT Chapters 2.5-2.6, 3 |
3 | Multivariate Normal Distribution | 1 | TEXT Chapter 4 |
4 | Multivariate Inference | 1, 2, 3 | TEXT Chapters 5.1-5.5, 6.1-6.3 |
5 | Multivariate Inference | 1, 2, 3 | TEXT Chapters 5.1-5.5, 6.1-6.3 |
6 | Multivariate Analysis of Variance | 1, 2, 3 | TEXT Chapters 6.4-6.6 |
7 | Midterm Examination | 1, 2, 3 | TEXT Chapters 8.1-8.5 |
8 | Principal Component Analysis | 1, 2, 3 | TEXT Chapters 8.1-8.5 |
9 | Principal Component Analysis | 1, 2, 3 | TEXT Chapters 8.1-8.5 |
10 | Factor Analysis | 1, 2, 3 | TEXT Chapter 9 |
11 | Canonical Correlation Analysis | 1, 2, 3 | TEXT Chapter 10 |
12 | Discriminant Analysis | 1, 2, 3 | TEXT Chapters 11.1-11.6 |
13 | Advanced Topics | 1, 2, 3 | TEXT Chapter 12 |
Point-based marking
By mark range
Marks | Criteria |
> 90% | Able to achieve Intended Learning Outcomes completely |
70% to 89% | Able to achieve Intended Learning Outcomes with some minor mistakes |
50% to 69% | Able to achieve Intended Learning Outcomes with some glaring mistakes |
40% to 49% | Able to achieve only some of Intended Learning Outcomes |
< 40% | Unable to achieve Intended Learning Outcomes at all |
Point-based marking
By mark range
Marks | Criteria |
> 90% | Able to achieve Intended Learning Outcomes completely |
70% to 89% | Able to achieve Intended Learning Outcomes with some minor mistakes |
50% to 69% | Able to achieve Intended Learning Outcomes with some glaring mistakes |
40% to 49% | Able to achieve only some of Intended Learning Outcomes |
< 40% | Unable to achieve Intended Learning Outcomes at all |
Point-based marking
By mark range
Marks | Criteria |
> 90% | Able to achieve Intended Learning Outcomes completely |
70% to 89% | Able to achieve Intended Learning Outcomes with some minor mistakes |
50% to 69% | Able to achieve Intended Learning Outcomes with some glaring mistakes |
40% to 49% | Able to achieve only some of Intended Learning Outcomes |
< 40% | Unable to achieve Intended Learning Outcomes at all |