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.
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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: ...
Appendix of Videos in Course Textbook
These are the instructional videos that appear throughout the course textbook. Title Link What is Between Group vs Within Group Variation? Transcript https://player.vimeo.com/video/379060892 How to Tell if One Variable "Explains Variation" in ...
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 ...
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 = ...
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.