Sum of Squares (SS)

Sum of Squares (SS)

Sum of Squares (SS) is the total area of the squared residuals; a way to quantify error, which gets around the problem of the sum of residuals adding up to 0; an important feature of SS is that the one number model that uniquely minimizes it is the mean.

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    • SS Model

      SS model is the reduction in error (measured in sums of squares) due to the model; the area of all the squared deviations based on the distance between the complex model predictions and the null model predictions.

    • sum()

      The sum() function computes the sum of a series of values. Example 1: # Sum by indicating a series of values sum(1, 2, 100) Example output: Example 2: # Sum by using a saved vector expenses <- c(33, 74, 12, 248, 520) sum(expenses) Example output: ...

    • SS Total

      SS Total is the amount of error revealed by the empty model (the mean); it is the total area of the squared residuals based on the distance of each score from the mean.

    • SS Error

      SS error is the amount of error left unexplained by the model; the area of all the squared residuals based on the distance of each score from the model prediction.

    • Sum of Absolute Deviations (SAD)

      Sum of Absolute Deviations (SAD) is the total of the absolute values of each deviation from the mean; a way to quantify error, which gets around the problem of the sum of deviations adding up to 0; also called SAE, Sum of Absolute Error.