This blog looks at how to obtain an unbiased and reasonably precise estimate of the population R-squared. It also presents power and sample size guidelines for regression analysis.
Reviewing the basic statistical concepts: expected value and variance.
Low R-squared values are not always bad and high R-squared values are not always good!
What if your regression model has significant variables but explains little of the variability? With low P values and a low R-squared.
A type of scientific modelling that tries to link main features of society and economy with the biosphere and atmosphere into one modelling framework.
Emerging evidence highlights the increasing risks of non-linearities and systemic changes caused by climate change. These include large-scale tipping points in the earth’s climate such as the disintegration of the Greenland and West Antarctic Ice Sheets, permafrost collapse, and the breakdown of the Atlantic Meridional Overturning Circulation among others.
From The Institute for New Economic Thinking at the Oxford Martin School (INET Oxford)
https://www.inet.ox.ac.uk/research/programmes/complexity-economics/
