MathLiveResidual sum of squares
Gives the total squared difference between observed values and the values predicted by a model. It is used to measure how well a regression or fitted model matches the data, with smaller values indicating a closer fit.
\mathrm{RSS} = \sum_{i=1}^{n} \left(y_i - f(x_i)\right)^2What the symbols mean
| number of observations | |
| observed value of the i-th data point | |
| model prediction for the input x_i of the i-th data point | |
| input (predictor) value of the i-th data point |
About this formula
- FieldStatistics
- TopicRegression analysis
- LevelUniversity
- Also calledRSS, sum of squared residuals, sum of squared errors, SSR, residual sum of squares formula, least squares criterion
- SourceWikidata Q15096882
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