This is the famous Galton data on the heights or parents and their children (i.e., where the term "regression" comes from).
heights
A data frame with 898 cases and 6 variables:
The family that the child belongs to, labeled by the numbers from 1 to 204 and 136A.
The father's height, in inches.
The mother's height, in inches.
The gender of the child, male (M) or female (F).
The height of the child, in inches.
The number of kids in the family of the child.
1 if the child is male. 0 if the child is female.
1 if the child is female. 0 if the child is male.
Francis Galton, 2017, "Galton height data", https://doi.org/10.7910/DVN/T0HSJ1, Harvard Dataverse, V1, UNF:6:2ty+0YgqR2a66FlvjCuPkQ== [fileUNF]
Galton, F. (1886). Regression Towards Mediocrity in Hereditary Stature. The Journal of the Anthropological Institute of Great Britain and Ireland, 15, 246-263. doi:10.2307/2841583.
Wikipedia: Regression toward the mean
The troubling legacy of Francis Galton
#> 'data.frame': 898 obs. of 8 variables: #> $ family: Factor w/ 197 levels "1","10","100",..: 1 1 1 1 108 108 108 108 123 123 ... #> $ father: num 78.5 78.5 78.5 78.5 75.5 75.5 75.5 75.5 75 75 ... #> $ mother: num 67 67 67 67 66.5 66.5 66.5 66.5 64 64 ... #> $ gender: Factor w/ 2 levels "F","M": 2 1 1 1 2 2 1 1 2 1 ... #> $ height: num 73.2 69.2 69 69 73.5 72.5 65.5 65.5 71 68 ... #> $ kids : int 4 4 4 4 4 4 4 4 2 2 ... #> $ male : num 1 0 0 0 1 1 0 0 1 0 ... #> $ female: num 0 1 1 1 0 0 1 1 0 1 ...#> family father mother gender height kids male female #> 1 1 78.5 67.0 M 73.2 4 1 0 #> 2 1 78.5 67.0 F 69.2 4 0 1 #> 3 1 78.5 67.0 F 69.0 4 0 1 #> 4 1 78.5 67.0 F 69.0 4 0 1 #> 5 2 75.5 66.5 M 73.5 4 1 0 #> 6 2 75.5 66.5 M 72.5 4 1 0