What is the Residual Sum of Squares?

A Residual sum of square tells you how much of the dependent variables variation your model did not explain in the regression model. It is the sum of error calculated between the data set and the regression function. A regression function is represented by a smaller residual sum of squares figure. The smaller the residual sum of squares, the better; the greater the residual sum of squares, the poorer.

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How is the Residual Sum of Squares (RSS) Used?

It is disputed if the regress function is indeed useful for the explanation of a variance set, except an analysis proves otherwise. Note that the residual sum of squares is a great function for regression function correctness. However, a sufficiently complex regression function is perfectly matched with any data set. The residual sum of squares should not be independently used to determine whether the regression function is true or not. As the financial markets expand, advanced statistical techniques are used to aid informed financial decisions. Such techniques include big data, machine learning, artificial intelligence applications, and many others. The residual sum of squares is an old means still surviving during the expansion. This modern invention has aided many strategic financial decisions Here are some important points to note about the residual sum of squares;

• A Residual sum of squares is used to measure how much of the dependent variables variation your model did not explain in the regression model.
• It is one of the primitive statistical techniques still in existence and widely in use in the financial markets.