EASURING RELATIONSHI PS/REGRESSION
The Method of Least Squares (Continued)
fit criterion of goodness known as the principte of least squares
Ghoose, as the b_est fitting line, the line that minimizes the sum of squares of
the deviations of the observed values of y from those predicted
Expressed mathematically, minimize the sum of squared errors given by:
substituting for f, on" obtains the following expression:
sum of squared errors
The least square estimator of
- (0, * 0,,*,)]'
and p, are calculated as follows:
x- ?, '
0,=* and 0o=i-0,i
6r.".ng $l h"r" been computed, substitute their values into the equation of a
line to obtain the least
squares prediction equation, or;gglgsspn line.
As noted earlier, the prediction equation for
Where: $o anO $, ,"pr""ent estimates of the true and
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