Argylesock says: It’s been too long since I wrote about numbers here. It may be some more time before I do it again, partly because when not blogging I’m analysing data in R (among other things, obviously.) Meanwhile here’s what Igor at Human Sciences Explored has said about R.
The use of statistics has long been important in the human sciences. An early example is an analysis by William Sealy Gosset (alias “Student”) of biometric data obtained by Scotland Yard around 1900. The heights of 3,000 male criminals fit a bell curve almost perfectly:
Standard statistical methods allow the identification of correlations, which mark possible causal links:
Newer, more sophisticated statistical methods allow the exploration of time series and spatial data. For example, this project looks at the spatial distribution of West Nile virus (WNV) – which disease clusters are significant, and which are merely tragic coincidence:
SPSS has been the mainstay of statistical analysis in the human sciences, but many newer techniques are better supported in the free R toolkit. For example, this paper discusses detecting significant clusters of diseases using R. The New York Times has commented on R’s growing popularity, and James Holland Jones points out
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