Gauss had not published his finding because of his preference for fulling developing his ideas before making them public. Today, Gauss receives most of the credit for the invention of least squares, and thus regression.
Still, Gauss remained troubled throughout his life that people had questioned his claim to regression. The statistical historian R. Stigler told us that these kinds of priority disagreements are common in the history of scientific discovery. The term regression was first applied to statistics by the polymath Francis Galton.
Galton is a major figure in the development of statistics and genetics. Galton used the term regression to explain a phenomenon he observed in nature. In the s Galton collected data on the height of the descendants of extremely tall and extremely short trees. Galton published his analysis of his data in the paper Regression Towards Mediocrity in Hereditary Stature.
We now refer to this phenomenon that Galton discovered as regression to the mean. If today is extremely hot, you should probably expect tomorrow to be hot, but not quite as hot as today. If a baseball player just had by far the best season of his career, his next year is likely to be a disappointment.
Extreme events tend to be followed by something closer to the norm. Regression analysis as we know it today is primarily the work of R. Fisher, one the most renowned statisticians of the 20th Century. Fisher combined the work of Gauss and Pearson to develop a fully realized theory of the properties of least squares estimation.
Post Fisher, there have been a variety of important extensions of regression including logistic regression , nonparametric regression, Bayesian regression, and regression that incorporates regularization. Computing technology brought regression into the mainstream. In the s, IBM created mechanical punched card tabulators that could be used to calculate the answers to computationally heavy statistical analyses like regressions.
Before this, all calculations had to be done by hand, so regression was only for very small datasets or for those willing to do a mind numbing number of multiplication problems. Even still, all the way up to the s, the computations to complete a regression could take days and the technology was only available to select researchers. It was not until the emergence of the modern desktop computer that the use of regression analysis was truly democratized. Today, anyone with access to a PC can run a regression for a moderately sized dataset in less than a second.
Gauss and Legendre would be amazed at the ubiquity of least squares regression today. Regression analyses are frequently used by academics, policy analysts, journalists and even sports teams to predict the future and understand the past.
Even with the development of increasingly sophisticated algorithms for prediction and inference, good old least squares regression is still perhaps the crown jewel of statistical analysis. Our next post is about a woman trying to get the neglected story of the Philippines in World War II written into textbooks. This post was written by Dan Kopf ; follow him on Twitter here.
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The Discovery of Statistical Regression. Published Nov 6, Books from Priceonomics. Everything is Bullshit. Hipster Business Models. The Content Marketing Handbook. Linear equations can also be solved by iterative, non-linear methods, but there is seldom reason to do so now.
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Add a comment. Active Oldest Votes. Improve this answer. James Phillips James Phillips 1, 3 3 gold badges 8 8 silver badges 7 7 bronze badges. Whether Galton used this crude method as a quick first approximation I do not know, but certainly the accuracy would have been too low for his work. The details of his technique for modeling data I do not have. My understanding is that he created the term "regression" as in "regression to the mean".
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