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 | ||
| + | ## [[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 | + | # [[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 | + | # [[Hypothesis building|Hypothesis testing]] |
=== Day 4 - Correlation and regression === | === Day 4 - Correlation and regression === | ||
Revision as of 06:11, 4 September 2019
Contents
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1 Welcome to Sustainability Methods!
- 1.1 Day 1 - Intro
- 1.2 Day 2 - Data formats based on R
- 1.3 Day 3 - Simple tests
- 1.4 Day 4 - Correlation and regression
- 1.5 Day 5 - Correlation and regression
- 1.6 Day 6 - Designing studies
- 1.7 Day 7 - Designing studies
- 1.8 Day 8 - Types of experiments
- 1.9 Day 9 - Statistics from the Faculty
- 1.10 Day 10 - Statistics down the road
- 1.11 Day 11 - The big recap
- 1.12 Day 12 - Models
- 1.13 Day 13 - Ethics and norms of statistics
- 2 Admin Tools
Welcome to Sustainability Methods!
Day 1 - Intro
- Do models and statistics matter?
- Why does it pay to be literate in statistics and R?
- Getting concepts clear: Generalisation, Sample, and Bias
- History of statistics
Day 2 - Data formats based on R
- Continuous vs. categorical, and subsets
- Normal distribution
- Poisson, binomial, Pareto
Day 3 - Simple tests
- Parametric
- Non-Parametric
- Hypothesis testing
Day 4 - Correlation and regression
- What can be correlated?
- Are all correlations causal?
- Is the world linear?
- Transformation
Day 5 - Correlation and regression
- P values vs. sample size
- Residuals
- Reading correlation plots
Day 6 - Designing studies
- How do I compare more than two groups groups?
- Designing experiments - degrees of freedom
- One way and two way
Day 7 - Designing studies
- Balanced vs. unbalanced - Welcome to the Jungle
- Block effects
- Interaction and reduction
Day 8 - Types of experiments
- Are all laboratory experiment really made in labs?
- Are all field experiment really made in fields?
- What are natural experiments?
Day 9 - Statistics from the Faculty
Day 10 - Statistics down the road
- Multivariate Statistics
- AIC
Day 11 - The big recap
- Distribution & simple test
- Correlation and regression
- Analysis of Variance
Day 12 - Models
- Are models wrong?
- Are models causal?
- Are models useful?
Day 13 - Ethics and norms of statistics
- What is informed consent?
- How does a board of ethics work?
- How long do you store data?
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