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: ...
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 ...
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 ...
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.