Last modified: 25 Sep 2019 09:58
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.
| Study Type | Postgraduate | Level | 5 |
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| Term | First Term | Credit Points | 15 credits (7.5 ECTS credits) |
| Campus | Aberdeen | Sustained Study | No |
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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.
Information on contact teaching time is available from the course guide.
| Assessment Type | Summative | Weighting | 60 | |
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| Assessment Type | Summative | Weighting | 40 | |
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There are no assessments for this course.
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| Knowledge Level | Thinking Skill | Outcome |
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