STAT463-20S1 (C) Semester One 2020

Multivariate Statistical Methods

15 points

Details:
Start Date: Monday, 17 February 2020
End Date: Sunday, 21 June 2020
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Friday, 28 February 2020
  • Without academic penalty (including no fee refund): Friday, 29 May 2020

Description

Multivariate Statistical Methods

Multivariate statistical methods extract information from datasets which consist of variables measured on a number of experimental units. These methods are widely applied in a variety of fields, including bioinformatics, epidemiology, finance and marketing. The course will cover the theory and application of various multivariate statistical methods, namely: multiple regression, principal component analysis, factor analysis, discriminant analysis, and clustering methods. It will also introduce the statistical analysis software R, which is a powerful tool when dealing with large multivariate datasets. SAS will also be briefly explained. Special attention will be given to practical applications and the interpretation of the results.

Learning Outcomes

  • The courses will:
  • introduce multiple and multivariate regression
  • introduce principal component analysis (PCA) and factor analysis (FA)
  • introduce discriminant analysis (DA) and clustering methods
  • introduce the use of the statistical analysis software R (and SAS) for basic multivariate analyses
  • give you experience in writing scientific and technical reports

    You will be able to:
  • choose appropriate method for analysis of your dataset
  • use appropriate R function (or SAS procedures) to perform multivariate analyses
  • be able to interpret the analysis results in such a way that a non-user of statistics can understand
  • write a scientific and technical report.

Pre-requisites

Subject to approval of the Head of School.

Timetable 2020

Students must attend one activity from each section.

Lecture A
Activity Day Time Location Weeks
01 Wednesday 11:00 - 12:00 - (25/3, 22/4-27/5)
E16 Lecture Theatre (19/2-18/3)
17 Feb - 29 Mar
20 Apr - 31 May
Lecture B
Activity Day Time Location Weeks
01 Friday 12:00 - 13:00 - (24/4-29/5)
E16 Lecture Theatre (21/2-20/3)
17 Feb - 22 Mar
20 Apr - 31 May
Computer Lab A
Activity Day Time Location Weeks
01 Wednesday 10:00 - 11:00 - (25/3, 22/4-27/5)
Jack Erskine 442 (19/2-18/3)
17 Feb - 29 Mar
20 Apr - 31 May
02 Wednesday 15:00 - 16:00 - (22/4-27/5)
Jack Erskine 442 (19/2-25/3)
17 Feb - 29 Mar
20 Apr - 31 May

Course Coordinator

Carl Scarrott

Lecturers

Carl Scarrott and Heyang (Thomas) Li

Assessment

Assignments give you practice in analysing data and presenting results in a written report. You will be expected to use R (or SAS) for analysis. The assignments provide an opportunity for you to learn not only statistical modeling techniques, but to develop your scientific writing skills.

The course includes a project report and a presentation on a method not covered in the course.

Textbooks / Resources

Recommended Reading

Everitt, Brian. , Dunn, G; Applied multivariate data analysis; 2nd ed; Arnold ;Oxford University Press, 2001.

Hastie, Trevor. , Tibshirani, Robert., Friedman, J. H; The elements of statistical learning : data mining, inference, and prediction; 2nd ed; Springer, 2009 (2001 or 2009 editions suitable).

Johnson, Richard Arnold. , Wichern, Dean W; Applied multivariate statistical analysis; 5th ed; Prentice Hall, 2002.

Indicative Fees

Domestic fee $989.00

* Fees include New Zealand GST and do not include any programme level discount or additional course related expenses.

For further information see Mathematics and Statistics.

All STAT463 Occurrences

  • STAT463-20S1 (C) Semester One 2020