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BI5009: EXPERIMENTAL DESIGN AND ANALYSIS (2022-2023)

Last modified: 26 Oct 2022 11:10


Course Overview

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.

Course Details

Study Type Postgraduate Level 5
Session First Sub Session Credit Points 15 credits (7.5 ECTS credits)
Campus Aberdeen Sustained Study No
Co-ordinators
  • Dr A Douglas

What courses & programmes must have been taken before this course?

  • One of Master Of Science In Marine Conservation or MSc Applied Marine and Fisheries Ecology or Master Of Science In Ecology & Conservation or MSci Biological Sciences
  • One of Any Postgraduate Programme (Studied) or BI4015 Grant Proposal (Passed) or BI4515 Grant Proposal - Semester 2 (Passed)

What other courses must be taken with this course?

None.

What courses cannot be taken with this course?

Are there a limited number of places available?

No

Course Description

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).

 

 


Details for second half-session courses, including assessments, may be subject to change until 23 December 2022.

Contact Teaching Time

Information on contact teaching time is available from the course guide.

Teaching Breakdown

  • 4 Computer Practicals during University week8
  • 5 Computer Practicals during University weeks 9 - 13
  • 1 Lecture during University week8

More Information about Week Numbers


Details for second half-session courses, including assessments, may be subject to change until 23 December 2022.

Summative Assessments

2x in-class graded practicals (20% each)

Assessment Type Summative Weighting 40
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Duration of practicals: 75 minutes and 90 minutes

Learning Outcomes
Knowledge LevelThinking SkillOutcome
Sorry, we don't have this information available just now. Please check the course guide on MyAberdeen or with the Course Coordinator

Report: Individual

Assessment Type Summative Weighting 60
Assessment Weeks Feedback Weeks

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Data analysis report

Learning Outcomes
Knowledge LevelThinking SkillOutcome
Sorry, we don't have this information available just now. Please check the course guide on MyAberdeen or with the Course Coordinator

Formative Assessment

There are no assessments for this course.

Resit Assessments

Resubmission of failed elements

Assessment Type Summative Weighting
Assessment Weeks Feedback Weeks

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Learning Outcomes
Knowledge LevelThinking SkillOutcome
Sorry, we don't have this information available just now. Please check the course guide on MyAberdeen or with the Course Coordinator

Course Learning Outcomes

Knowledge LevelThinking SkillOutcome
FactualRememberILO’s for this course are available in the course guide.

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