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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

   - See also, misunderstood concepts

3. History of statistics

Day 2 - Data formats based on R

1. Continuous vs. categorical, and subsets
2. Normal distribution
3. Poisson, binomial, Pareto

Day 3 - Simple tests

1. Parametric and non-parametric
2. Hypothesis testing
3. The power of probability

Day 4 - Correlation and regression

[and regressions]

Causal vs non-causal relations

1. - See also, misunderstood concepts

2. Are all correlations causal?
3. Is the world linear?
4. Transformation

Day 5 - Correlation and regression

1. P values vs. sample size
2. Residuals
3. Reading correlation plots

Day 6 - Designing studies Pt. 1

1. How do I compare more than two groups?
2. Designing experiments - degrees of freedom
3. One way and two way

Day 7 - Designing studies Pt. 2

1. Balanced vs. unbalanced - Welcome to the Jungle
2. Block effects
3. Interaction and reduction

Day 8 - Types of experiments

1. Are all laboratory experiment really made in labs?
2. Are all field experiment really made in fields?
3. What are natural experiments?

Day 10 - Statistics down the road

1. Multivariate Statistics
2. AIC

Day 11 - The big recap

1. Distribution & simple test
2. Correlation and regression

   - See also, misunderstood concepts

3. Analysis of Variance

Day 12 - Models

1. Are models wrong?
2. Are models causal?
3. Are models useful?

Day 13 - Ethics and norms of statistics

1. What is informed consent?
2. How does a board of ethics work?
3. How long do you store data?

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