Last modified: 16 Nov 2016 18:26
This course is uniquely tailored for biologists and will provide students with the required background and skills relevant to modern ecology and biology. The unique format of example-led lectures and real-world based practicals will provide you with a foundation to become confident and proficient in dealing with real data. Throughout this course, we will introduce you to using the programming language R (an industry standard) to implement modern statistical modelling techniques. You will use the flexible linear and generalised modelling framework to analyse biological data.
|Session||First Sub Session||Credit Points||15 credits (7.5 ECTS credits)|
The module will be divided in themed weeks during which students will gain skills in sampling design (through practicals) and analytical technique (through lecture and computer labs).
Week 1: Students are introduced to simple sampling designs, concepts of inference, causality and probability, and the language R.
Week 2: Students continue their progression in statistical analyses and are introduced to data exploration and visualisation in R as well as real-world sampling designs.
Week 3: Students learn about general linear models and their interpretation (model fitting, model selection, and model validation) and are exposed to more advanced models. Students will undertake a one hour in-class assessemnt.
Week 4: Students extend the linear modelling framework to apply it to a range of data types using generalised linear models. Students will undertake a one hour in-class assessment.
Week 5: Students continue exploring generalised linear models and are also given the option to cover generalised additive models. Students get the opportunity to go over material covered in previous weeks.Week 6: Student-lead teaching. Students are given the opportunity to go over previous material to reinforce learning and are given time to prepare for their final in-class assessment (3 hours). Students are also offered the opportunity to optionally cover additional material such as multivariate statistics.
This is the total time spent in lectures, tutorials and other class teaching.