residual

residual

Residual is the difference between our model prediction and an actual observed score.

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    • abs()

      The abs() function will produce the absolute value for a number. Example: The code below will take the residuals from the empty model for Thumb in the Fingers data frame, and give back their absolute value. empty_model <- lm(Thumb ~ NULL, data = ...
    • resid()

      The resid() function will calculate the residuals (error) from a model. That is, when given a model, it will take each case and calculate how far away the observed value is from the prediction of the model. Example 1: # Calculate the residuals from ...
    • 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 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 ...