Difference between revisions of "Main Page"

Revision as of 05:40, 4 September 2019

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 3) History of statistics

Day 2 - Data formats based on R 4) Continuous vs. categorical, and subsets 5) Normal distribution 6) Poisson, binomial, Pareto

Day 3 - Simple tests 10) Parametric 11) Non-Parametric 12) Hypothesis testing

Day 4 - Correlation and regression 13) What can be correlated? 14) Are all correlations causal? 15) Is the world linear? Transformation.

Day 5 - Correlation and regression 19) P values vs. sample size 20) Residuals 21) Reading correlation plots

Day 6 - Designing studies 22) How do I compare more than two groups groups? 23) Designing experiments - degrees of freedom 24) One way and two way

Day 7 - Designing studies 25) Balanced vs. unbalanced - Welcome to the Jungle 26) Block effects 27) Interaction and reduction

Day 8 - Types of experiments 28) Are all laboratory experiment really made in labs? 29) Are all field experiment really made in fields? 30) What are natural experiments?

Day 9 - Statistics from the Faculty

Day 10 - Statistics down the road 31) Multivariate Statistics 32) AIC

Day 11 - The big recap 33) Distribution & simple test 34) Correlation and regression 35) Anova

Day 12 - models 36) Are models wrong? 37) Are models causal? 38) Are models useful?

Day 13 - Ethics and norms of statistics 31) What is informed consent? 32) How does a board of ethics work? 33) How long do you store data?

View All Pages.

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-<strong>MediaWiki has been installed.</strong>
+<strong>Welcome to Sustainability Methods!</strong>
-Consult the [https://www.mediawiki.org/wiki/Special:MyLanguage/Help:Contents User's Guide] for information on using the wiki software.
+<strong>Day 1 - Intro</strong>
+) 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
+<strong>Day 2 - Data formats based on R</strong>
+) Continuous vs. categorical, and subsets
+) Normal distribution
+) Poisson, binomial, Pareto
+<strong>Day 3 - Simple tests</strong>
+) Parametric
+) Non-Parametric
+) Hypothesis testing
+<strong>Day 4 - Correlation and regression</strong>
+) What can be correlated?
+) Are all correlations causal?
+) Is the world linear? Transformation.
+<strong>Day 5 - Correlation and regression</strong>
+) P values vs. sample size
+) Residuals
+) Reading correlation plots
+<strong>Day 6 - Designing studies</strong>
+) How do I compare more than two groups groups?
+) Designing experiments - degrees of freedom
+) One way and two way
+<strong>Day 7 - Designing studies</strong>
+) Balanced vs. unbalanced - Welcome to the Jungle
+) Block effects
+) Interaction and reduction
+<strong>Day 8 - Types of experiments</strong>
+) Are all laboratory experiment really made in labs?
+) Are all field experiment really made in fields?
+) What are natural experiments?
+<strong>Day 9 - Statistics from the Faculty</strong>
+<strong>Day 10 - Statistics down the road</strong>
+) Multivariate Statistics
+) AIC
+<strong>Day 11 - The big recap</strong>
+) Distribution & simple test
+) Correlation and regression
+) Anova
+<strong>Day 12 - models</strong>
+) Are models wrong?
+) Are models causal?
+) Are models useful?
+<strong>Day 13 - Ethics and norms of statistics</strong>
+) What is informed consent?
+) How does a board of ethics work?
+) How long do you store data?
+View [https://sustainabilitymethods.org/index.php/Special:AllPages All Pages].
-View [https://sustainabilitymethods.org/index.php/Special:AllPages all pages].
 == Getting started ==