Least Squares Method The function t ( y 1 . . . y n ) {\displaystyle t(y_{1}...y_{n})} must belong to the set of all possible values for E(y) must minimize ∑ i = 1 n ( y i − t ( y 1 . . . y n ) ) 2 {\displaystyle \sum _{i=1}^{n}(y_{i}-t(y_{1}...y_{n}))^{2}} t = t ( y 1 . . . y n ) {\displaystyle t=t(y_{1}...y_{n})} is the LS Estimate. ∑ i = 1 n ( y i − t ) 2 = ∑ i − 1 n ( y i − y ¯ ) 2 + n ( y ¯ − t ) 2 {\displaystyle \sum _{i=1}^{n}(y_{i}-t)^{2}=\sum _{i-1}^{n}(y_{i}-{\bar {y}})^{2}+n({\bar {y}}-t)^{2}} t {\displaystyle t} must equal y ¯ {\displaystyle {\bar {y}}} to minimize!