Editing Point estimation (section)

=== Method of least square ===
In the method of least square, we consider the estimation of parameters using some specified form of the expectation and second moment of the observations. For

fitting a curve of the form y = f( x, β<sub>0</sub>, β<sub>1</sub>, ,,,, β<sub>p</sub>) to the data (x<sub>i</sub>, y<sub>i</sub>), i = 1, 2,…n, we may use the method of least squares. This method consists of minimizing the

sum of squares.

When f(x, β<sub>0</sub>, β<sub>1</sub>, ,,,, β<sub>p</sub>) is a linear function of the parameters and the x-values are known, least square estimators will be [[best linear unbiased estimator]] (BLUE). Again, if we assume that the least square estimates are independently and identically normally distributed, then a linear estimator will be [[minimum-variance unbiased estimator]] (MVUE) for the entire class of unbiased estimators. See also [[minimum mean squared error]] (MMSE).<ref name=":1" />