Lines {regression line}| {regression curve} closest to all points can go through correlation graphs.
Regression curve can be straight line {linear regression}|.
Regression lines pass as close as possible {best fit}| to all points and so minimize sum of distances from points to regression line. Lines can pass closest to points if sum of squares of differences from line to points is minimum. Best-fit lines must pass through all property means: x2 = m * x1 + b, where x1 and x2 are means, m is slope, and b is y-intercept. m = (N * (sum from i = 1 to i = N of n1(i) * n2(i)) - (sum from i = 1 to i = N of n1(i)) * (sum from i = 1 to i = N of n2(i))) / (N * (sum from i = 1 to i = N of n1(i)^2) - (sum from i = 1 to i = N of n1(i)^2)). b = x2 - m * x1.
Regression curves can predict {prediction} second-variable amount from first-variable amount. Second-variable value y equals regression-curve slope m times first variable x plus intercept b: y = m*x + b.
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Date Modified: 2022.0225