R Dataset / Package psych / galton

On this R-data statistics page, you will find information about the galton data set which pertains to Galton's Mid parent child height data. The galton data set is found in the psych R package. You can load the galton data set in R by issuing the following command at the console data("galton"). This will load the data into a variable called galton. If R says the galton data set is not found, you can try installing the package by issuing this command install.packages("psych") and then attempt to reload the data with the library() command. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the galton R data set. The size of this file is about 9,261 bytes.

Galton's Mid parent child height data

Description

Two of the earliest examples of the correlation coefficient were Francis Galton's data sets on the relationship between mid parent and child height and the similarity of parent generation peas with child peas. This is the data set for the Galton height.

Usage

data(galton)

Format

A data frame with 928 observations on the following 2 variables.

parent: Mid Parent heights (in inches)
child: Child Height

Details

Female heights were adjusted by 1.08 to compensate for sex differences. (This was done in the original data set)

Source

This is just the galton data set from UsingR, slightly rearranged.

References

Stigler, S. M. (1999). Statistics on the Table: The History of Statistical Concepts and Methods. Harvard University Press. Galton, F. (1886). Regression towards mediocrity in hereditary stature. Journal of the Anthropological Institute of Great Britain and Ireland, 15:246-263. Galton, F. (1869). Hereditary Genius: An Inquiry into its Laws and Consequences. London: Macmillan.

Wachsmuth, A.W., Wilkinson L., Dallal G.E. (2003). Galton's bend: A previously undiscovered nonlinearity in Galton's family stature regression data. The American Statistician, 57, 190-192.

Examples

data(galton)
describe(galton)
 #show the scatter plot and the lowess fit 
pairs.panels(galton,main="Galton's Parent child heights")
#but this makes the regression lines look the same
pairs.panels(galton,lm=TRUE,main="Galton's Parent child heights") 
 #better is to scale them 
pairs.panels(galton,lm=TRUE,xlim=c(62,74),ylim=c(62,74),main="Galton's Parent child heights")

Dataset imported from https://www.r-project.org.

Attachments: csv, json

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