What is the difference between least squares Mean and Mean?

Answer 1

Mean is the sum of several values of the same type (x1, x2,..., xN ) divided by the number of values.

Mean = (x1 + x2 + ... xN ) /N

The Least square method is used when doing a regression of a cloud of point { (x1,y1), (x2,y2) etc. } by a function (linear, parabolic hyperbolic etc.). With this special algorithm we get the closest function f (x) to approximated the cloud of point.

f(x, Beta) ~ y

Beta = (XTX)-1XT Y = coefficients of the regression

The points must be in 2 dimensions, because the methods needs to derivate the function f.

I think that the least square mean is not the proper term because you have a function f ... What is the mean of f (x) = a *x + b ??? See.