Difference between revisions of "Main Page"

From Sustainability Methods
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# Why does it pay to be literate in statistics and R?
 
# Why does it pay to be literate in statistics and R?
 
# Getting concepts clear: Generalisation, Sample, and Bias
 
# Getting concepts clear: Generalisation, Sample, and Bias
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## [[Misunderstood concepts in statistics|See also, misunderstood concepts]]
 
# History of statistics
 
# History of statistics
  
 
=== Day 2 - Data formats based on R ===
 
=== Day 2 - Data formats based on R ===
# Continuous vs. categorical, and subsets
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# [[Data formats|Continuous vs. categorical, and subsets]]
 
# [[Data distribution|Normal distribution]]
 
# [[Data distribution|Normal distribution]]
 
# Poisson, binomial, Pareto
 
# Poisson, binomial, Pareto
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# Parametric
 
# Parametric
 
# Non-Parametric
 
# Non-Parametric
# Hypothesis testing
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# [[Hypothesis building|Hypothesis testing]]
  
 
=== Day 4 - Correlation and regression ===
 
=== Day 4 - Correlation and regression ===

Revision as of 06:11, 4 September 2019

Welcome to Sustainability Methods!

Day 1 - Intro

  1. Do models and statistics matter?
  2. Why does it pay to be literate in statistics and R?
  3. Getting concepts clear: Generalisation, Sample, and Bias
    1. See also, misunderstood concepts
  4. 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
  2. Non-Parametric
  3. Hypothesis testing

Day 4 - Correlation and regression

  1. What can be correlated?
  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

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

Day 7 - Designing studies

  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
  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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