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Wikipedia / Explained variation

In statistics, explained variation measures the proportion to which a mathematical model accounts for the variation (dispersion) of a given data set. Often, variation is quantified as variance; then, the more specific term explained variance can be used.

The complementary part of the total variation is called unexplained or residual variation; likewise, when discussing variance as such, this is referred to as unexplained or residual variance.

Definition in terms of information gain

Information gain by better modelling

Following Kent (1983), we use the Fraser information (Fraser 1965)

.

Parameters are determined by maximum likelihood estimation,

The information gain of model 1 over model 0 is written as

where a factor of 2 is included for convenience. Γ is always nonnegative; it measures the extent to which the best model of family 1 is better than the best model of family 0 in explaining g(r).

Information gain by a conditional model

where X shall be considered as an explanatory variable, and Y as a dependent variable. Models of family 1 "explain" Y in terms of X,

  1. ,

whereas in family 0, X and Y are assumed to be independent. We define the randomness of Y, and the randomness of Y, given X. Then,

can be interpreted as proportion of the data dispersion which is "explained" by X.

Special cases and generalized usage

Linear regression

The fraction of variance unexplained is an established concept in the context of linear regression. The usual definition of the coefficient of determination is based on the fundamental concept of explained variance.

Correlation coefficient as measure of explained variance

Let X be a random vector, and Y a random variable that is modeled by a normal distribution equals the squared correlation coefficient.

Note the strong model assumptions: the centre of the Y distribution must be a linear function of X, and for any given x, the Y as proportion of explained variance.

In principal component analysis

Explained variance is routinely used in principal component analysis. The relation to the Fraser–Kent information gain remains to be clarified.

Criticism

, it shares all the disadvantages of the latter: it reflects not only the quality of the regression, but also the distribution of the independent (conditioning) variables.

supports these claims". is enhanced just by jointly considering data from two different populations: "'Explained variance' explains nothing."

See also

References