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Consider, for example, students who take a midterm and a final exam. Students who got an extremely high score on the midterm will probably get a good score on the final exam as well, but we expect their score to be closer to the average (i.e.: fewer standard deviations above the average) than their midterm score was. The reason: it is likely that some luck was involved in getting the exceptional midterm score, and this luck cannot be counted on for the final. It is also true that among those who get exceptionally high final exam scores, the average midterm score will be fewer standard deviations above average than the final exam score, since some of those got high scores on the final due to luck that they didn't have on the midterm. Similarly, unusually low scores regress toward the mean.
It is a commonplace observation that matings of two championship athletes, or of two geniuses, usually results in a child who is above average but less talented then either of their parents.
The first regression line drawn on biological data was a plot of seed weights presented by Francis Galton at a Royal Institution lecture in 1877. Galton had seven sets of sweet pea seeds labelled K to Q and in each packet the seeds were of the same weight. He chose sweet peas on the advice of his cousin Charles Darwin and the botanist Joseph Hooker as sweet peas tend not to self fertilise and the seed weight varies little with humidity. He distributed these packets to a group of friends throughout Great Britain who planted them. At the end of the growing season the plants were uprooted and returned to Galton. The seeds were distributed because when Galton had tried this experiment himself in the Kew Gardens in 1874, the crop had failed.
He found that the weights of the offspring seeds were normally distributed, like their parents, and that if he plotted the mean diameter of the offspring seeds against the mean diameter of their parents he could draw a straight line through the points - the first regression line. He also found on this plot that the mean size of the offspring seeds tended to the overall mean size. He initially referred to the slope of this line as the "coefficient of reversion". Once he discovered that this effect was not a heritable property but the result of his manipulations of the data, he changed the name to the "coefficient of regression". This result was important because it appeared to conflict with the current thinking on evolution and natural selection. He went to do extensive work in quantitative genetics and in 1888 coined the term "co-relation" and used the now familiar symbol "r" for this value.
In additional work he investigated geniuses in various fields and noted that their children, while typically gifted, were almost invariably closer to the average than their exceptional parents. He later described the same effect more numerically by comparing fathers' heights to their sons' heights. Again, the son's height is typically closer to the mean height than the father's height.
It is important to realize that regression toward the mean is a ubiquitous statistical phenomenon and has nothing to do with biological inheritance. It is also unrelated to the progression of time: the fathers of exceptionally tall people also tend to be closer to the mean than their sons. The overall variability of height among fathers and sons is the same.
Given two variables X and Y with mean 0, common variance 1, and correlation coefficientIn probability theory and statistics, the correlation also called correlation coefficient between two random variables is found by dividing their covariance by the product of their standard deviations. It is defined only if these standard deviations are f r, the expected value of Y given that the value of X was measured to be x is equal to rx, which is closer to the mean 0 than x since |r| < 1. If the variances of the two variable X and Y are different, and one measures the variables in "normalized units" of standard deviations, then the principle of regression toward the mean also holds true.
This example illustrates a general fact: regression toward the mean is more pronounced the less the two variables are correlated, i.e. the smaller |r| is.