Data invented by Neyman to look at spurious correlations and adjusting
for lurking variables by looking at the relationship between storks and biths.
Usage
data(stork)
Format
A data frame with 54 observations on the following 6 variables.
County
ID of county
Women
Number of Women (*10,000)
No.storks
Number of Storks sighted
No.babies
Number of Babies Born
Stork.rate
Storks per 10,000 women (=No.storks/Women)
Birth.rate
Babies per 10,000 women (=No.babies/Women)
Details
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.
Source
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.
References
Neyman, J. (1952) Lectures and Conferences on Mathematical Statistics
and Probability, 2nd edn, pp. 143-154. Washington DC: US Department of Agriculture.