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  2. Mean squared error - Wikipedia

    en.wikipedia.org/wiki/Mean_squared_error

    If a vector of predictions is generated from a sample of data points on all variables, and is the vector of observed values of the variable being predicted, with ^ being the predicted values (e.g. as from a least-squares fit), then the within-sample MSE of the predictor is computed as

  3. Mean squared prediction error - Wikipedia

    en.wikipedia.org/wiki/Mean_squared_prediction_error

    When the model has been estimated over all available data with none held back, the MSPE of the model over the entire population of mostly unobserved data can be estimated as follows.

  4. Root mean square deviation - Wikipedia

    en.wikipedia.org/wiki/Root_mean_square_deviation

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  5. Errors and residuals - Wikipedia

    en.wikipedia.org/wiki/Errors_and_residuals

    It is remarkable that the sum of squares of the residuals and the sample mean can be shown to be independent of each other, using, e.g. Basu's theorem.That fact, and the normal and chi-squared distributions given above form the basis of calculations involving the t-statistic:

  6. Mean square - Wikipedia

    en.wikipedia.org/wiki/Mean_square

    In mathematics and its applications, the mean square is normally defined as the arithmetic mean of the squares of a set of numbers or of a random variable. [ 1 ] It may also be defined as the arithmetic mean of the squares of the deviations between a set of numbers and a reference value (e.g., may be a mean or an assumed mean of the data), [ 2 ...

  7. Bessel's correction - Wikipedia

    en.wikipedia.org/wiki/Bessel's_correction

    The following statements explain the meaning of the remaining columns: The sum of the entries in the first column (a 2) is the sum of the squares of the distance from sample to sample mean; The sum of the entries in the last column (b 2) is the sum of squared distances between the measured sample mean and the correct population mean

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  9. Root mean square - Wikipedia

    en.wikipedia.org/wiki/Root_mean_square

    Physical scientists often use the term root mean square as a synonym for standard deviation when it can be assumed the input signal has zero mean, that is, referring to the square root of the mean squared deviation of a signal from a given baseline or fit. [8] [9] This is useful for electrical engineers in calculating the "AC only" RMS of a signal.