enow.com Web Search

Search results

  1. Results from the WOW.Com Content Network
  2. Variance - Wikipedia

    en.wikipedia.org/wiki/Variance

    The general formula for the variance of the outcome, X, ... This expression can be used to calculate the variance in situations where the CDF, ...

  3. Algorithms for calculating variance - Wikipedia

    en.wikipedia.org/wiki/Algorithms_for_calculating...

    Algorithms for calculating variance play a major role in computational statistics.A key difficulty in the design of good algorithms for this problem is that formulas for the variance may involve sums of squares, which can lead to numerical instability as well as to arithmetic overflow when dealing with large values.

  4. Propagation of uncertainty - Wikipedia

    en.wikipedia.org/wiki/Propagation_of_uncertainty

    Any non-linear differentiable function, (,), of two variables, and , can be expanded as + +. If we take the variance on both sides and use the formula [11] for the variance of a linear combination of variables ⁡ (+) = ⁡ + ⁡ + ⁡ (,), then we obtain | | + | | +, where is the standard deviation of the function , is the standard deviation of , is the standard deviation of and = is the ...

  5. Pooled variance - Wikipedia

    en.wikipedia.org/wiki/Pooled_variance

    In statistics, pooled variance (also known as combined variance, composite variance, or overall variance, and written ) is a method for estimating variance of several different populations when the mean of each population may be different, but one may assume that the variance of each population is the same. The numerical estimate resulting from ...

  6. Covariance - Wikipedia

    en.wikipedia.org/wiki/Covariance

    For two jointly distributed real-valued random variables and with finite second moments, the covariance is defined as the expected value (or mean) of the product of their deviations from their individual expected values: [3] [4]: 119

  7. Conditional variance - Wikipedia

    en.wikipedia.org/wiki/Conditional_variance

    In words: the variance of Y is the sum of the expected conditional variance of Y given X and the variance of the conditional expectation of Y given X. The first term captures the variation left after "using X to predict Y", while the second term captures the variation due to the mean of the prediction of Y due to the randomness of X.

  8. Standard deviation - Wikipedia

    en.wikipedia.org/wiki/Standard_deviation

    This is a "one pass" algorithm for calculating variance of n samples without the need to store prior data during the calculation. Applying this method to a time series will result in successive values of standard deviation corresponding to n data points as n grows larger with each new sample, rather than a constant-width sliding window calculation.

  9. Root mean square deviation - Wikipedia

    en.wikipedia.org/wiki/Root_mean_square_deviation

    For an unbiased estimator, the RMSD is the square root of the variance, known as the standard deviation. Samples ... , the formula becomes = = (,,). ...