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: ...

    • variance

      Variance is also called MS, Mean Square; approximated by the sum of squares (SS) divided by the degrees of freedom (i.e., n-1); the MSE from the empty model can be thought of as roughly the average squared deviation.

    • Mean Square (MS)

      Mean Square (MS) is also called variance; approximated by the sum of squares (SS) divided by the degress of freedom (i.e., n-1); the MSE from the empty model can be thought of as roughly the average squared deviation.

    • proportion reduction in error (PRE)

      Proportion reduction in error (PRE) is the proportion of error that has been reduced by a more complex model compared with a simpler model, which in our course is always the empty model. When comparing to the empty model, PRE is calculated as SS ...