Teaching Responsibility

LJMU Schools involved in Delivery:

Computer Science and Mathematics

Learning Methods

Lecture

Practical

Module Offerings

6108STATS-JAN-CTY

Aims

To enable the student to explore the structure of multidimensional data sets. To introduce the student to inferential procedures using multivariate data.

Learning Outcomes

1.
Carry out an exploratory numerical and graphical analysis of a multivariate data set.
2.
Recognize situations in which a multivariate approach is required and carry out the appropriate inferential procedures.
3.
Present the results of a multivariate data analysis in a brief report.

Module Content

Outline Syllabus:Graphical display and numerical summary of multivariate data. Investigation of the dependence among variables. Discrimination and prediction. Error rate estimation. Hypothesis construction and testing. Use of simultaneous confidence intervals. Principal Components Analysis. Use appropriate software for data exploration, visualisation, parameter estimation and significance testing.
Additional Information:This final year module advances beyond univariate statistical methods to the analysis of data sets with multiple dependent variables (multivariate data). The assessment will be individual and tutor assessed. How does the Module relate to the Programme overall? The module introduces to both the theory of analysing multivariate data sets based on the multivariate normal distribution as well as the practical application of multivariate methods to real-world data sets using modern statistical software.

Assessments

Test