Residual Sum of Squares is a statistical technique which is used to measure the amount of variance in a set of data points that cannot be explained by a regression model.
Residual sum of squares is calculated by subtracting the predicted estimated values from the real values available, which is then squared and added.
Where Yi is the ith value of the variable to be predicted and Yi hat is the predicted value of Y in a standard linear regression model where yi=a+bxi+c
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