Explained Sum Of Squares
In statistics, the explained sum of squares (ESS), alternatively known as the Model Sum of Squares or Sum of Squares due to Regression, is a quantity used in describing how well a model, often a regression model, represents the data being modelled. In particular, the explained sum of squares measures how much variation there is in the modelled values and this is compared to the total sum of squares, which measures how much variation there is in the observed data, and to the residual sum of squares, which measures the variation in the modelling errors.
Read more about Explained Sum Of Squares: Definition, Partitioning in Simple Linear Regression, Partitioning in The General OLS Model
Famous quotes containing the words explained, sum and/or squares:
“It was heady stuff, recognizing ourselves as an oppressed class, but the level of discussion was poor. We explained systemic discrimination, and men looked prettily confused and said: “But, I like women.””
—Jane O’Reilly, U.S. feminist and humorist. The Girl I Left Behind, ch. 2 (1980)
“And what is the potential man, after all? Is he not the sum of all that is human? Divine, in other words?”
—Henry Miller (1891–1980)
“And New York is the most beautiful city in the world? It is not far from it. No urban night is like the night there.... Squares after squares of flame, set up and cut into the aether. Here is our poetry, for we have pulled down the stars to our will.”
—Ezra Pound (1885–1972)