Last modified: 4 Days, 9 Hours, 25 Minutes ago
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 |
|---|---|---|---|
| Session | Second Sub Session | 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 two 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: An Introduction to R. Three days of structured lessons will allow you to learn to use R in your own way and at your own pace. You will learn how to conduct basic operations in R and R Studio, how to import and manipulate dataframes, how to visualise data and then to run basic statistics in R. At the end of the week you will have a MyAberdeen based assessment on statistical inference and R.
Week 2: Linear Modelling using R. In this week you learn about the theory and practice of fitting simple linear models in R, how to validate and interpret linear models, 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. The first four days of the week you work through structured lessons and on the fifth day you complete the second assessment.
The second assessment for the course is a MyAberdeen based assessment on linear models with a single continuous variable.
Information on contact teaching time is available from the course guide.
| Assessment Type | Summative | Weighting | 70 | |
|---|---|---|---|---|
| Assessment Weeks | 37 | Feedback Weeks | 38 | |
| 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. |
| Assessment Type | Summative | Weighting | 30 | |
|---|---|---|---|---|
| Assessment Weeks | 35 | Feedback Weeks | 36 | |
| 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 | Have an appreciation and working knowledge of how to conduct your data analysis in a robust and reproducible way. |
| Conceptual | Understand | Understand how we can ask questions in science and specifically how we can apply statistical inference to estimate population parameters. |
| Procedural | Apply | Be able to visualise and explore biological and ecological data using appropriate graphs and summary tables 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 | Apply | Be able to visualise and explore biological and ecological data using appropriate graphs and summary tables 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 | Evaluate | Be able to critically evaluate linear models through model validation and also interpret model output in a biological context. |
| Procedural | Understand | Have a good understanding and working knowledge of using R. |
| Conceptual | Understand | Have an appreciation and working knowledge of how to conduct your data analysis in a robust and reproducible way. |
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