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squared deviations : ウィキペディア英語版
squared deviations

In probability theory and statistics, the definition of variance is either the expected value (when considering a theoretical distribution), or average value (for actual experimental data), of squared deviations from the mean. Computations for analysis of variance involve the partitioning of a sum of squared deviations. An understanding of the complex computations involved is greatly enhanced by a detailed study of the statistical value:
: \operatorname( X ^ 2 ).
It is well known that for a random variable X with mean \mu and variance \sigma^2:
: \sigma^2 = \operatorname( X ^ 2 ) - \mu^2〔Mood & Graybill: ''An introduction to the Theory of Statistics'' (McGraw Hill)〕
Therefore
: \operatorname( X ^ 2 ) = \sigma^2 + \mu^2.
From the above, the following are easily derived:
: \operatorname\left( \sum\left( X ^ 2\right) \right) = n\sigma^2 + n\mu^2
: \operatorname\left( \left(\sum X \right)^ 2 \right) = n\sigma^2 + n^2\mu^2
If \hat is a vector of n predictions, and Y is the vector of the true values, then the SSE of the predictor is:
SSE=\frac\sum_^n(\hat - Y_i)^2
== Sample variance ==
(詳細はsample variance (before deciding whether to divide by ''n'' or ''n'' − 1) is most easily calculated as
: S = \sum x ^ 2 - \frac
From the two derived expectations above the expected value of this sum is
: \operatorname(S) = n\sigma^2 + n\mu^2 - \frac
which implies
: \operatorname(S) = (n - 1)\sigma^2.
This effectively proves the use of the divisor ''n'' − 1 in the calculation of an unbiased sample estimate of ''σ''2.

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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