Content deleted Content added
Radagast83 (talk | contribs) mNo edit summary |
No edit summary |
||
Line 1:
{{Mergeto|Least squares|date=July 2006}}
In [[statistics]], the '''residual sum of squares (RSS)''' is the [[sum]] of squares of [[errors and residuals in statistics|residuals]]
:<math>RSS = \sum_{i=1}^n (y_i - f(x_i))^2. </math>
In a standard [[regression model]] <math>y_i = a+bx_i+\varepsilon_i\,</math>, where ''a'' and ''b'' are [[coefficient]]s, ''y'' and ''x'' are the [[regressand]] and the [[regressor]], respectively, and ε is the error term. The sum of squares of residuals is the sum of squares of [[estimator|estimates]] of ε<sub>''i''</sub>.▼
▲In a standard [[regression model]] <math>y_i = a+bx_i+\varepsilon_i\,</math>, where ''a'' and ''b'' are [[coefficient]]s, ''y'' and ''x'' are the [[regressand]] and the [[regressor]], respectively, and ε is the error term. The sum of squares of residuals is the sum of squares of [[estimator|estimates]] of ε<sub>''i''</sub>
:<math>RSS = \sum_{i=1}^n (y_i - (a+bx_i))^2. </math>
In general: [[total sum of squares]] = [[explained sum of squares]] + '''residual sum of squares'''.
| |||