You will learn statistical methods and reasoning relevant to environmental science. You'll also gain experience in the proper use of statistics for the analysis of weather and climate data. Practical classes use Python. The topics are:
- Introduction to statistics: basic concepts, history
- Exploratory data analysis: summary statistics
- Forecast verification: skill scores
- Linear regression: correlation
- Multiple regression: confounders, causality
- Time series analysis: autocorrelation
- Concepts of probability: Bayes theorem
- Probability distributions: lots of different distributions!
- Parameter estimation: confidence intervals
- Hypothesis testing: significance tests, p-value.
This course is developed from the Statistics for Weather and Climate Science module.
There are practical assignments each week (10 in total) and four of these will be assessed.
Provisional Timetabled sessions, Thursdays, 2-4pm (UK time)28 September 2023, 14:00–16:00
5 October 2023, 14:00–16:00
12 October 2023, 14:00–-16:00
19 October 2023, 14:00–16:00
26 October 2023, 14:00–16:00
No class on 2 November
9 November 2023, 14:00–16:00
16 November, 14:00–16:00
23 November, 14:00–16:00
30 November, 14:00–16:00
7 December 14:00–16:00
A joining link will be given to enrolled learners.
Working knowledge of A-level Mathematics
Equivalent to Further Mathematics A-level or a pass equivalent to a 2.1 in a first year science undergraduate mathematics module.
Programming experience (ideally Python). Standard equivalent to a first year science undergraduate programming module.
Apply by 10 September 2023.