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BI5009: EXPERIMENTAL DESIGN AND ANALYSES (2018-2019)

Last modified: 22 May 2019 17:07


Course Overview

This course is uniquely tailored for biologists and will provide students with the required background theory and practical skills relevant to modern ecology and biology. The unique format of example-led lectures and real-world based practicals will provide you with a foundation to become confident and proficient in analysing real data. Throughout this course, we will introduce you to using the programming language R to implement modern statistical modelling techniques. You will use the flexible linear and generalised linear modelling frameworks to analyse biological data with emphasis on robust and reproducible research methods.

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?

  • Either Any Postgraduate Programme (Studied) or BI4015 Grant Proposal (Passed)
  • One of Master Of Science In Marine Conservation or MSc Applied Marine and Fisheries Ecology or Master Of Science In Ecology & Conservation or Master Of Science In Environmental And Forest Management or MSci Biological Sciences

What other courses must be taken with this course?

None.

What courses cannot be taken with this course?

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: Students are introduced to simple sampling designs, concepts of inference, causality and probability, and the language R.

Week 2: Students continue their progression in statistical analyses and are introduced to data exploration and visualisation in R as well as real-world sampling designs.

Week 3: Students learn about general linear models and their interpretation (model fitting, model selection, and model validation) and are exposed to more advanced models.  Students will undertake a one hour in-class assessemnt.

Week 4: Students extend the linear modelling framework to apply it to a range of data types using generalised linear models. Students will undertake a one hour in-class assessment.

Week 5: Students continue exploring generalised linear models. Students get 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 for their final in-class assessment (3 hours). Students are also offered the opportunity to optionally cover additional material such as multivariate statistics.

Associated Costs

None


Contact Teaching Time

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

Teaching Breakdown

More Information about Week Numbers


Summative Assessments

 

The module will be assessed based on 3 in-class graded practicals (20%, 20%, 60%).

Resit: Resubmission of failed individual elements of continuous assessment

Formative Assessment

There are no assessments for this course.

Feedback

None.

Course Learning Outcomes

None.

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