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From Sustainability Methods
Revision as of 05:47, 4 September 2019 by 46.183.103.17 (talk)

Welcome to Sustainability Methods!

Day 1 - Intro

  1. Do models and statistics matter? Why does it pay to be literate in statistics and R?
  2. Getting concepts clear: Generalisation, Sample, and Bias
  3. History of statistics
  1. === Day 2 - Data formats based on R ===
  2. Continuous vs. categorical, and subsets
  3. Normal distribution
  4. 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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