What is Residual Sum of Squares (RSS)?
Residual sum of squares is used to calculate the variance of the data in terms of error or residuals. It is used to calculate the error left between regression data and regression function after running the model. The smaller the value of the residual sum of squares, the better the model.
Residual Sum of Square Formula
Residual Sum of Squares (RSS) can be calculated using the following formula:
Where,
- yi is the ith value of variable to be predicted,
- f(xi) is the predicted value, and
- n is the number of terms or variables.
Residual Sum of Squares
Residual Sum of Squares is one of the types of sum of squares in regression which is used to measure the dispersion of the data points. The sum of squares can also be used to calculate the variance in the values of assets in the case of accounting. If the value of the sum of squares is higher, it represents a higher variance from the mean value and vice versa. The sum of squares is generally of 3 types i.e. Total Sum of Squares, Regressive or Regression Sum of Squares, and Residual Sum of Squares. In this article, we will study majorly the types of Residual Sum of Squares. Other than this we will also discuss both other types in bried as well.