Multivariate Analysis (Bologna) STATS4069

  • Academic Session: 2019-20
  • School: School of Mathematics and Statistics
  • Credits: 12
  • Level: Level 4 (SCQF level 10)
  • Typically Offered: Semester 2
  • Available to Visiting Students: No
  • Available to Erasmus Students: No

Short Description

The course provides the basic tools for dealing with problems and questions arising with multivariate data.

 

Timetable

Requirements of Entry

This course is only available to students on the Double Degree programme in Statistics with the University of Bologna.

Excluded Courses

Multivariate Methods [STATS4046]

Multivariate Methods (Level M) [STATS5021]

Co-requisites

-/-

Assessment

End-of-course examination, carried out in accordance with the assessment procedures and regulations of the University of Bologna.

Main Assessment In: April/May

Are reassessment opportunities available for all summative assessments? Not applicable for Honours courses

Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non-Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below. 

Course Aims

This course aims 

■ to expose students to different methods for supervised and unsupervised learning from multivariate data;

■ to train students in the application and interpretation of statistical models for dimension reduction, clustering and supervised classification; and

■ to train students in using R to fit such models and interpret the output obtained.

Intended Learning Outcomes of Course

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

■ apply and interpret methods of dimension reduction including principal component analysis, multidimensional scaling, factor analysis, canonical variates;

■ perform a cluster analysis;

■ apply and interpret supervised classification techniques; and

■ interpret the output of R procedures for multivariate statistics.

 

Minimum Requirement for Award of Credits

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.