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Welcome to Sustainability Methods!
Day 1 - Intro
- Do models and statistics matter? Why does it pay to be literate in statistics and R?
- Getting concepts clear: Generalisation, Sample, and Bias
- History of statistics
- === Day 2 - Data formats based on R ===
- Continuous vs. categorical, and subsets
- Normal distribution
- Poisson, binomial, Pareto
Day 3 - Simple tests 10) Parametric 11) Non-Parametric 12) Hypothesis testing
Day 4 - Correlation and regression 13) What can be correlated? 14) Are all correlations causal? 15) Is the world linear? Transformation.
Day 5 - Correlation and regression 19) P values vs. sample size 20) Residuals 21) Reading correlation plots
Day 6 - Designing studies 22) How do I compare more than two groups groups? 23) Designing experiments - degrees of freedom 24) One way and two way
Day 7 - Designing studies 25) Balanced vs. unbalanced - Welcome to the Jungle 26) Block effects 27) Interaction and reduction
Day 8 - Types of experiments 28) Are all laboratory experiment really made in labs? 29) Are all field experiment really made in fields? 30) What are natural experiments?
Day 9 - Statistics from the Faculty
Day 10 - Statistics down the road 31) Multivariate Statistics 32) AIC
Day 11 - The big recap 33) Distribution & simple test 34) Correlation and regression 35) Anova
Day 12 - models 36) Are models wrong? 37) Are models causal? 38) Are models useful?
Day 13 - Ethics and norms of statistics 31) What is informed consent? 32) How does a board of ethics work? 33) How long do you store data?
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