Last modified: 20 Jun 2025 15:10
This course is uniquely tailored for environmental scientists and ecologists and will provide students with the required background theory and practical skills relevant to modern science. Our example-led lectures and real-world based practical sessions 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 environmental and ecological data with an emphasis on robust and reproducible statistical methods.
| Study Type | Postgraduate | Level | 5 |
|---|---|---|---|
| Term | First Term | Credit Points | 15 credits (7.5 ECTS credits) |
| Campus | Aberdeen | Sustained Study | No |
| Co-ordinators |
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This course will be divided into themed weeks during which you will gain a foundational understanding of statistical theory through example-led lectures and practical skills by completing computer-based exercises.
Week 1: You are introduced to concepts of statistical inference, uncertainty and using R and RStudio for reproducible data analysis.
Week 2: You will learn about the process of analysing ecological data and are introduced to data exploration and visualisation in R using real-world data.
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.
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.
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, you will learn how to model discrete count data with a Poisson GLM.
Week 6: In this week you will further extend the GLM framework to fit models to binary (0/1) data with a binomial GLM.
Information on contact teaching time is available from the course guide.
| Assessment Type | Summative | Weighting | 20 | |
|---|---|---|---|---|
| Assessment Weeks | 11 | Feedback Weeks | 11 | |
| Feedback |
90-minute MyAberdeen based test. Written feedback will be provided for each question. |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Understand | Understand how we can ask questions in science and specifically how we can apply statistical inference to estimate population parameters. |
| Conceptual | Understand | Have an appreciation and working knowledge of how to conduct your data analysis in a robust and reproducible way. |
| Procedural | Apply | Be able to visualise and explore biological and ecological data using appropriate graphs and summary tables using R. |
| Assessment Type | Summative | Weighting | 60 | |
|---|---|---|---|---|
| Assessment Weeks | 14 | Feedback Weeks | 16 | |
| Feedback |
Written Report - Analyse a provided data set and complete a structured written report.
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Apply | Understand the theory of linear modelling and how to apply this theory to fit models to biological and ecological data using R. |
| Conceptual | Evaluate | Be able to critically evaluate linear models through model validation and also interpret model output in a biological context. |
| Conceptual | Understand | Have an appreciation and working knowledge of how to conduct your data analysis in a robust and reproducible way. |
| Procedural | Apply | Be able to visualise and explore biological and ecological data using appropriate graphs and summary tables using R. |
| Procedural | Understand | Have a good understanding and working knowledge of using R. |
| Assessment Type | Summative | Weighting | 20 | |
|---|---|---|---|---|
| Assessment Weeks | 12 | Feedback Weeks | 13 | |
| Feedback |
120-minute Myaberdeen based test. Written and verbal feedback will be provided individually. |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Apply | Understand the theory of linear modelling and how to apply this theory to fit models to biological and ecological data using R. |
| Conceptual | Evaluate | Be able to critically evaluate linear models through model validation and also interpret model output in a biological context. |
| Conceptual | Understand | Have an appreciation and working knowledge of how to conduct your data analysis in a robust and reproducible way. |
| Procedural | Understand | Have a good understanding and working knowledge of using R. |
There are no assessments for this course.
| Assessment Type | Summative | Weighting | ||
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback |
Any components that were previously passed will be carried forward. |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Understand | Understand how we can ask questions in science and specifically how we can apply statistical inference to estimate population parameters. |
| Procedural | Understand | Have a good understanding and working knowledge of using R. |
| Conceptual | Apply | Understand the theory of linear modelling and how to apply this theory to fit models to biological and ecological data using R. |
| Conceptual | Understand | Have an appreciation and working knowledge of how to conduct your data analysis in a robust and reproducible way. |
| Conceptual | Evaluate | Be able to critically evaluate linear models through model validation and also interpret model output in a biological context. |
| Procedural | Apply | Be able to visualise and explore biological and ecological data using appropriate graphs and summary tables using R. |
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