Residual sum of squares (RSS)

Residual sum of squares (RSS),

Definition of Residual sum of squares (RSS):

  1. Measures the overall difference between actual data and the values predicted by an estimation model. The differences are squared and then added. It represents unexplained variation - a smaller RSS means that the model fits the data well. Also called the Sum of Squared Errors of prediction (SSE).

  2. The residual sum of squares measures the amount of error remaining between the regression function and the data set. A smaller residual sum of squares figure represents a regression function. Residual sum of squares–also known as the sum of squared residuals–essentially determines how well a regression model explains or represents the data in the model.

  3. 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. Regression is a measurement that helps determine the strength of the relationship between a dependent variable and a series of other changing variables or independent variables.

How to use Residual sum of squares (RSS) in a sentence?

  1. 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.
  2. The residual sum of squares is one of many statistical properties enjoying a renaissance in financial markets.
  3. Ideally, the sum of squared residuals should be a smaller or lower value in any regression model.

Meaning of Residual sum of squares (RSS) & Residual sum of squares (RSS) Definition