'''sum of squares (SS)'''

Sub:
sum of squares error - [[제곱합오차,square_sum_error]] ... [[오차,error]]

residual sum of squares (RSS) = sum of squared residuals (SSR) = sum of squared estimate of errors (SSE)
 ''tmp curr [[잔차,residual]]''

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MKLINK
[[제곱,square]]
[[합,sum]]
[[제곱근,square_root]]
[[노름,norm]] esp L2 norm
[[분산,variance]]은 제곱합 나누기 dof.
Srch:sum_of_sq
[[평균제곱오차,mean_square_error,MSE]]
[[선형회귀,linear_regression]]는 [[근사해,approximate_solution]]와 (...)의 [[차이,difference]]([[오차,error]])의 '''제곱합,square_sum'''을 최소화하는 [[해,solution]]를 찾는 것에 대한??? 그 외의 다른 방법도 있음? CHK Ggl:선형회귀

rel
[[power_sum]] - WpEn:Sums_of_powers
[[reciprocal_sum]] - WpEn:Sum_of_reciprocals

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Twins:
[[WpKo:제곱합]] = https://ko.wikipedia.org/wiki/제곱합
[[WpEn:Sum_of_squares]]

... Naver:제곱합 Google:제곱합 Google:sum.of.squares

Up:
[[제곱,square]] [[합,sum]]