ANSC50020 Mixed Model Methodology

Academic Year 2018/2019

The objective of this module is to provide students with an advanced working knowledge of statistical theory and application for life sciences research. Students will gain knowledge in REML, data diagnositcs, analysis of incomplete block designs, repeated measures, linear and nonlinear regression and logistical regression. Practical sessions using SAS software will be used to teach students how to put statistical theory into practice
Students will be required to bring a dataset with them from their Masters/PhD research project to use in the practical classes and use in the project at the end of the semester. Alternatively, if a dataset is not available, they may bring another dataset provided by their project supervisor.
All students must bring a laptop with SAS loaded on to the laptop, and be ready to use for the week of class.

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Curricular information is subject to change

Learning Outcomes:

On completion of this module students should be able to critically assess the appropriateness of the experimental design, have an in depth knowledge of the statistical methodologies to apply to the data, and finally to be able to write SAS code that will allow them to analyse the data correctly.

Student Effort Hours: 
Student Effort Type Hours
Lectures

21
Computer Aided Lab

14
Autonomous Student Learning

65
Total

100
 
Requirements, Exclusions and Recommendations

Not applicable to this module.



 
Description % of Final Grade Timing

Not recorded

Compensation

This module is not passable by compensation

Resit Opportunities

In-semester assessment

Remediation

If you fail this module you may repeat, resit or substitute where permissible.