What is a 'Residual Sum Of Squares - RSS'

A residual sum of squares (RSS) is a statistical technique used to measure the amount of variance in a data set that is not explained by a regression model. The residual sum of squares is a measure of the amount of error remaining between the regression function and the data set. A smaller residual sum of squares figure represents a regression function which explains a greater amount of the data.

BREAKING DOWN 'Residual Sum Of Squares - RSS'

It is not possible to draw conclusions about the correctness of the regression function solely using the residual sum of squares. Since a sufficiently complex regression function can be made to closely fit virtually any data set, further study is necessary to determine whether the regression function is, in fact, useful in explaining the variance of the dataset. Typically, however, a smaller residual sum of squares is ideal.

Financial markets have increasingly become more quantitatively driven, as such, in search of an edge, many investors are using advanced statistical techniques to aid in their decisions. Big data, machine learning, and artificial intelligence applications further necessitate the use of statistical properties to guide contemporary investment strategies.

The residual sum of squares in one of many statistical properties enjoying a renaissance.

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