Difference between revisions of "Main Page"

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

Revision as of 05:53, 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
  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. Anova

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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