Data invented by Neyman to look at spurious correlations and adjusting for lurking variables by looking at the relationship between storks and biths.
A data frame with 54 observations on the following 6 variables.
ID of county
Number of Women (*10,000)
Number of Storks sighted
Number of Babies Born
Storks per 10,000 women (=No.storks/Women)
Babies 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.
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