Description Usage Format Details Source References Examples

Data invented by Neyman to look at spurious correlations and adjusting for lurking variables by looking at the relationship between storks and biths.

1 |

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)

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.

1 2 3 | ```
data(stork)
pairs(stork[,-1], panel=panel.smooth)
## maybe str(stork) ; plot(stork) ...
``` |

TeachingDemos documentation built on May 29, 2017, 11:33 a.m.

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