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)

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.

Examples

data(stork)
pairs(stork[,-1], panel=panel.smooth)

## maybe str(stork) ; plot(stork) ...