In 1910, Karl Pearson weighed in on the debate, fostered by the temperance movement, on the evils done by alcohol not only to drinkers, but to their families. The report "A first study of the influence of parental alcholism on the physique and ability of their offspring" was an ambitious attempt to the new methods of statistics to bear on an important question of social policy, to see if the hypothesis that children were damaged by parental alcoholism would stand up to statistical scrutiny.
Working with his assistant, Ethel M. Elderton, Pearson collected voluminous data in Edinburgh and Manchester on many aspects of health, stature, intelligence, etc. of children classified according to the drinking habits of their parents. His conclusions where almost invariably negative: the tendency of parents to drink appeared unrelated to any thing he had measured.
The firestorm that this report set off is well described by Stigler (1999),
Chapter 1. The data set
DrinksWages is just one of Pearsons
many tables, that he published in a letter to The Times,
August 10, 1910.
A data frame with 70 observations on the following 6 variables, giving the number of non-drinkers (
and drinkers (
drinks) in various occupational categories (
wage class: a factor with levels
a factor with levels
the number of non-drinkers, a numeric vector
the number of drinkers, a numeric vector
weekly wage (in shillings), a numeric vector
total number, a numeric vector
The data give Karl Pearson's tabulation of the father's trades from an Edinburgh sample, classified by whether they dring or are sober, and giving average weekly wage.
The wages are averages of the individuals' nominal wages. Class A is those with wages under 2.5s.; B: those with wages 2.5s. to 30s.; C: wages over 30s.
Pearson, K. (1910). The Times, August 10, 1910.
Stigler, S. M. (1999). Statistics on the Table: The History of Statistical Concepts and Methods. Harvard University Press, Table 1.1
M. E. Elderton & K. Pearson (1910). A first study of the influence of parental alcholism on the physique and ability of their offspring, Eugenics Laboratory Memoirs, 10.
1 2 3 4 5 6 7 8 9 10 11 12 13 14
data(DrinksWages) plot(DrinksWages) # plot proportion sober vs. wage | class with(DrinksWages, plot(wage, sober/n, col=c("blue","red","green")[class])) # fit logistic regression model of sober on wage mod.sober <- glm(cbind(sober, n) ~ wage, family=binomial, data=DrinksWages) summary(mod.sober) op <- par(mfrow=c(2,2)) plot(mod.sober) par(op) # TODO: plot fitted model
Call: glm(formula = cbind(sober, n) ~ wage, family = binomial, data = DrinksWages) Deviance Residuals: Min 1Q Median 3Q Max -2.2720 -0.5235 0.3472 0.5476 1.2329 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -0.839963 0.323962 -2.593 0.00952 ** wage 0.001862 0.012811 0.145 0.88443 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 44.717 on 69 degrees of freedom Residual deviance: 44.696 on 68 degrees of freedom AIC: 194.06 Number of Fisher Scoring iterations: 4
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.