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From Sustainability Methods

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 9 - Statistics from the Faculty

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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