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| − | == Welcome to Sustainability Methods! == | + | == Welcome to the ''Sustainability Methods Wiki!'' == |
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| | + | The aim of this wiki is to present and explain fundamental methods, terms and tools relevant to (sustainability) science and answer relevant underlying questions. The wiki is composed of several sub-wikis that can be accessed below. |
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| − | === Day 1 - Intro ===
| + | * [[Classes]] (currently only Statistics)<br> |
| − | # [[Why statistics matters|Do models and statistics matter?]] Why does it pay to be literate in statistics and R?
| + | * |
| − | # Getting concepts clear: Generalisation, Sample, and Bias
| + | * [[Wiki Entries]] (Methods, Skills & Tools)<br> |
| − | #:- See also, [[Misunderstood concepts in statistics|misunderstood concepts]]
| + | * |
| − | # [[Why statistics matters#A very short history of statistics|History of statistics]]
| + | * [[Normativity of Methods]]<br> |
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| − | === Day 2 - Data formats based on R ===
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| − | # [[Data formats#Continuous data|Continuous vs. categorical, and subsets]]
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| − | # [[Data distribution|Normal distribution]]
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| − | # Poisson, binomial, Pareto
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| − | === Day 3 - Simple tests ===
| + | For more information, please refer to the [[Sustainability_Methods:About|About]] page and the [[FAQ]]. |
| − | # [[Simple Tests|Parametric and non-parametric]]
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| − | # [[Hypothesis building|Hypothesis testing]]
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| − | # [[Designing studies#P-value|The power of probability]]
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| − | === Day 4 - Correlation and regression ===
| + | If you would like to provide feedback or ask further questions, please contact us via the contact form in the [[FAQ]] section. |
| − | == What are correlation and regressions ==
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| − | Propelled through the general development of science during the Enlightenment, numbers started piling up. With more technological possibilities to measure more and more information, and slow to store this information, people started wondering whether these numbers could lead to something. The increasing numbers had diverse sources, some were from science, such as Astronomy or other branches of natural science. Other prominent sources of numbers were from engineering, and even other from economics, such as double bookkeeping. It was thanks to the tandem efforts of Adrien-Marie Legendre and Carl Friedrich Gauss that mathematics offered with the methods of least squares the first approach to relate one line of data with another. How is one continuous variable related to another? The box of the Panters was opened, and questions started to emerge. Economists were the first who utilised regression analysis at a larger scale, relating all sorts of economical and social indicators with each other, building an ever more complex controlling, management and maybe even understanding of statistical relations. The Gross domestic product -or GDP- became for quite some time kind of a pet variable for many economists, and especially Growth become a core goal of many analysis to inform policy. What people basically did is ask themselves, how one variable is related to another variable. If nutrition of people increases, do they live longer (Yes). If Economies have a higher GDP do they offer more social security (No). Does a higher income lead to more Co2 emissions at a country scale (yes). As these relations started coming in the questions of whether two continuous variables are casually related becoming a nagging thought. With more and more data being available, correlation became a staple of modern statistics. There are some core questions related to the application of correlations and regressions.
| + | Following are links to some pages that you might be interested in: |
| − | 1) Are relations between two variables positive or negative?
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| − | Relations between two variables can be positive or negative. Being taller leads to a significant increase in body weight. Being smaller leads to an overall lower gross calorie demand. The strength of this relation -what statisticians call the estimate- is an important measure when evaluating correlations and regressions. Is a relation positive or negative, and how strong is the estimate of the relation?
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| − | 2) Does the relation show a significantly strong effect, or is it rather weak? In other words, can the regression explain a lot of variance of your data, or is the results rather weak regarding its explanatory power? Take EXAMPLE
| + | * [[Index : Statistics|Link to Statistics Topics on the Wiki]] |
| | + | * [[Former Homepage|Link to the Former Homepage]] |
| | + | * [[Table of Contributors]] |
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| − | 3) Relation can explain a lot of variance for some data, and less variance for other parts of the data. Take the percentage of people working in Agriculture within individual countries. At a low income (<5000 Dollar/year) there is a high variance. Half of the population of the Chad work in agriculture, while in Zimbabwe with a even slightly lower income its 10 %. At an income above 15000 Dollar/year, there is hardly any variance in the people that work in agriculture within a country. The proportion is very low. This has reasons, there is probably one or several variables that explain at least partly the high variance within different income segments. Finding such variance that explain partly unexplained variance is a key effort in doing correlation analysis.
| + | Here are the 5 newest wiki entries that have been made on Sustainability Methods: |
| − | | + | {{Special:NewestPages/-/5}} |
| − | Causal vs non-causal relations
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| − | #:- See also, [[Misunderstood concepts in statistics#Correlation|misunderstood concepts]]
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| − | # Are all correlations causal?
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| − | # Is the world linear?
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| − | # Transformation
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| − | === Day 5 - Correlation and regression ===
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| − | # [[Designing studies#P-value|P values]] vs. [[Designing studies#Sample size|sample size]]
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| − | # Residuals
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| − | # Reading correlation plots
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| − | === Day 6 - Designing studies Pt. 1 ===
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| − | # How do I compare more than two groups?
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| − | # Designing experiments - degrees of freedom
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| − | # One way and two way
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| − | === Day 7 - Designing studies Pt. 2 ===
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| − | # Balanced vs. unbalanced - Welcome to the Jungle
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| − | # [[Designing studies#Block effects|Block effects]]
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| − | # Interaction and reduction
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| − | === Day 8 - Types of experiments ===
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| − | # Are all laboratory experiment really made in labs?
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| − | # Are all field experiment really made in fields?
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| − | # What are natural experiments?
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| − | | |
| − | === Day 9 - Statistics from the Faculty ===
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| − | === Day 10 - Statistics down the road ===
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| − | # Multivariate Statistics
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| − | # AIC
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| − | === Day 11 - The big recap ===
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| − | # Distribution & simple test
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| − | # Correlation and regression
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| − | #:- See also, [[Misunderstood concepts in statistics|misunderstood concepts]]
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| − | # [[Analysis of Variance]]
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| − | === Day 12 - Models ===
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| − | # Are models wrong?
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| − | # Are models causal?
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| − | # Are models useful?
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| − | | |
| − | === Day 13 - Ethics and norms of statistics ===
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| − | # What is informed consent?
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| − | # How does a board of ethics work?
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| − | # How long do you store data?
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| − | View [https://sustainabilitymethods.org/index.php/Special:AllPages All Pages].
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| − | == Admin Tools ==
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| − | * [https://www.mediawiki.org/wiki/Special:MyLanguage/Manual:Configuration_settings Configuration settings list]
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| − | * [https://www.mediawiki.org/wiki/Special:MyLanguage/Manual:FAQ MediaWiki FAQ]
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| − | * [https://lists.wikimedia.org/mailman/listinfo/mediawiki-announce MediaWiki release mailing list]
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| − | * [https://www.mediawiki.org/wiki/Special:MyLanguage/Localisation#Translation_resources Localise MediaWiki for your language]
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| − | * [https://www.mediawiki.org/wiki/Special:MyLanguage/Manual:Combating_spam Learn how to combat spam on your wiki]
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