A way of finding a "line of best fit" by making the total of the square of the "errors" (the differences between observed and predicted values) as small as possible, which is why it is called "least squares".
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A way of finding a "line of best fit" by making the total of the square of the "errors" (the differences between observed and predicted values) as small as possible, which is why it is called "least squares".