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  6. BI5009: Experimental Design and Analyses

BI5009: EXPERIMENTAL DESIGN AND ANALYSES (2014-2015)

Last modified: 28 Jun 2018 10:27


Course Overview

This course provides rigorous training for biologists in statistical modelling concepts and techniques as well as the design of field experiments. The course uses a combination of lectures, computer-based and field-based practical to introduce students to the flexible linear modelling framework to analyse biological data. In addition to linear and generalized linear modelling, the course also introduces generalized additive modelling and multivariate statistics. You will gain a robust understanding of concept, theory and practice in biostatistics thanks to a unique example-led teaching approach. You will be trained to implement biostatistical analyses in the programming environment R.

Course Details

Study Type Postgraduate Level 5
Session First Sub Session Credit Points 15 credits (7.5 ECTS credits)
Campus None. Sustained Study No
Co-ordinators
  • Professor David Lusseau
  • Dr Alex Douglas

What courses & programmes must have been taken before this course?

None.

What other courses must be taken with this course?

None.

What courses cannot be taken with this course?

None.

Are there a limited number of places available?

No

Course Description

The module will be divided in themed weeks during which students will gain skills in sampling design (through practicals) and analytical technique (through lecture and computer labs). Week 1: introduction to biostatistics. Students are introduced to simple sampling design, core statistical concepts, and statistical software. Week 2: introduction to statistical modelling. Students continue their progression in statistical analyses and are introduced to complex sampling design. Week 3: generalised linear models. Students learn about generalised linear models and the interpretation of models (model fitting, model selection, and model validation) and are exposed to more advanced models. Students carry out sampling in groups for their report. Week 4: categorical data. Students learn about statistical techniques for categorical data. They also learn about power analyses to understand the influence of sample size on tests results. Week 5: multivariate statistics. Students cover multivariate statistical techniques and are given the opportunity to go over material covered in previous weeks. Week 6: student-lead teaching. Students are given the opportunity to go over previous material to reinforce learning and are given time to prepare their report.

Contact Teaching Time

Information on contact teaching time is available from the course guide.

Teaching Breakdown

More Information about Week Numbers


Details, including assessments, may be subject to change until 31 August 2023 for 1st half-session courses and 22 December 2023 for 2nd half-session courses.

Summative Assessments

The module will be assessed based on 2 graded practicals (20% each) and an independent report (60%)

Formative Assessment

There are no assessments for this course.

Feedback

None.

Course Learning Outcomes

None.
 

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