Neyman's Stork data
Data invented by Neyman to look at spurious correlations and adjusting for lurking variables by looking at the relationship between storks and biths.
data(stork)
A data frame with 54 observations on the following 6 variables.
CountyID of county
WomenNumber of Women (*10,000)
No.storksNumber of Storks sighted
No.babiesNumber of Babies Born
Stork.rateStorks per 10,000 women (=No.storks/Women)
Birth.rateBabies per 10,000 women (=No.babies/Women)
This is an entertaining example to show a relationship that is due to a third possibly lurking variable. The source paper shows how completely different relationships can be found by mis-analyzing the data.
Kronmal, Richard A. (1993) Spurious Cerrolation and the Fallacy of the Ratio Standard Revisited. Journal of the Royal Statistical Society. Series A, Vol. 156, No. 3, 379-392.
Neyman, J. (1952) Lectures and Conferences on Mathematical Statistics and Probability, 2nd edn, pp. 143-154. Washington DC: US Department of Agriculture.
data(stork) pairs(stork[,-1], panel=panel.smooth) ## maybe str(stork) ; plot(stork) ...
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