A sum of squares adds the squares of numbers for specified real numbers . It is nonnegative and vanishes exactly when every term is zero. For the coordinates of a vector, it is the squared Euclidean norm; for regression residuals, it gives the residual sum of squares minimized by ordinary least squares.
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The "sum of squares" is a statistical concept used to measure the variability or dispersion in a dataset. It is calculated by taking the difference between each data point and the mean of the dataset, squaring those differences, and then summing them up. It can be used in various contexts, including inferential statistics, regression analysis, and the analysis of variance (ANOVA). ### Formula Given a dataset with \( n \) observations \( x_1, x_2, ...