Last modified: 26 Oct 2022 11:10
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. Our 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 statistical methods.
| Study Type | Postgraduate | Level | 5 |
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| Session | First Sub Session | 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 you will gain a foundational understanding of statistical theory through example-led lectures and practical skills through computer based exercises.
Week 1: You are introduced to concepts of sampling, statistical inference, uncertainty and using R and RStudio for reproducible research and data analysis.
Week 2: You will learn about the process of analysing biological data and are introduced to data exploration and visualisation in R using real-world data examples. Data and instructions for your final assessment will be released to you this week.
Week 3: During this week you will learn about the theory and practice of fitting simple linear models in R. You will also learn how to validate and interpret linear models. Towards the end of the week you will complete your first in-class course assessment (20% of final course mark).
Week 4: You will learn how to extend the linear modelling framework and apply it to more complex models and data. You will also learn how to compare different plausible models and select the most informative model. You will undertake your second in-class assessment (20% of final course mark)
Week 5: During this week, you will learn how to extend the linear modelling framework to fit generalised linear models (GLMs) to analyse different types of data. Specifically, this week you will learn how to model discrete count data with a Poisson GLM.
Week 6: In this week you will further extend the generalised linear modelling framework to fit models to binary (0/1) data with a binomial GLM. You will also submit your final assessment which will be a structured written report based on your analysis and interpretation of a pre-existing dataset released to you in week 2 (60% of final course mark).
Information on contact teaching time is available from the course guide.
| Assessment Type | Summative | Weighting | 40 | |
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Duration of practicals: 75 minutes and 90 minutes |
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| Assessment Type | Summative | Weighting | 60 | |
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Data analysis report |
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| Knowledge Level | Thinking Skill | Outcome |
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There are no assessments for this course.
| Assessment Type | Summative | Weighting | ||
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| Assessment Weeks | Feedback Weeks | |||
| Feedback | ||||
| Knowledge Level | Thinking Skill | Outcome |
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| Knowledge Level | Thinking Skill | Outcome |
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| Factual | Remember | ILO’s for this course are available in the course guide. |
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