  Galton's family heights data has been a preeminent historical dataset in
regression analysis, on which the original model and basic results have
survived the close scrutiny of statisticians for 125 years. However by
revisiting Galton's family data, we challenge whether Galton's classic model
and his regression towards mean interpretation are proper. Using Galton's data
as a benchmark for different regression methods, such as least squares,
orthogonal regression, geometric mean regression, and least sine squares
regression - a newly developed nonparametric robust regression approach, we
elucidate that his regression model has fundamental drawbacks not only in
variable and model selection by "transmuting" women into men thus the simple
linear model, but also a strong bias in least squares regression leading to
otherwise alternative conclusions on the true relationships between the heights
of the child and his or her parents.
