In this course, 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.
There are practical assignments each week (10 in total) and four of these will be assessed.
See website for mathematical and programming prerequisites.
