Last modified: 05 Oct 2023 08:46
This course is uniquely tailored for ecologists and will provide students with the required background theory and practical skills relevant to modern ecology. 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 ecological data with an emphasis on robust and reproducible statistical 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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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 | 40 | |
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| Assessment Weeks | Feedback Weeks | |||
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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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| Assessment Weeks | Feedback Weeks | |||
| Feedback |
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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