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  2. Standard error - Wikipedia

    en.wikipedia.org/wiki/Standard_error

    For correlated random variables the sample variance needs to be computed according to the Markov chain central limit theorem.. Independent and identically distributed random variables with random sample size

  3. Mean squared error - Wikipedia

    en.wikipedia.org/wiki/Mean_squared_error

    Definition and basic properties. The MSE either assesses the quality of a predictor (i.e., a function mapping arbitrary inputs to a sample of values of some random variable), or of an estimator (i.e., a mathematical function mapping a sample of data to an estimate of a parameter of the population from which the data is sampled).

  4. Propagation of uncertainty - Wikipedia

    en.wikipedia.org/wiki/Propagation_of_uncertainty

    where represents the standard deviation of the function , represents the standard deviation of , represents the standard deviation of , and so forth.. It is important to note that this formula is based on the linear characteristics of the gradient of and therefore it is a good estimation for the standard deviation of as long as ,,, … are small enough.

  5. Residual sum of squares - Wikipedia

    en.wikipedia.org/wiki/Residual_sum_of_squares

    In statistics, the residual sum of squares ( RSS ), also known as the sum of squared residuals ( SSR) or the sum of squared estimate of errors ( SSE ), is the sum of the squares of residuals (deviations predicted from actual empirical values of data). It is a measure of the discrepancy between the data and an estimation model, such as a linear ...

  6. Bias of an estimator - Wikipedia

    en.wikipedia.org/wiki/Bias_of_an_estimator

    In statistics, the bias of an estimator (or bias function) is the difference between this estimator 's expected value and the true value of the parameter being estimated. An estimator or decision rule with zero bias is called unbiased. In statistics, "bias" is an objective property of an estimator. Bias is a distinct concept from consistency ...

  7. Clustered standard errors - Wikipedia

    en.wikipedia.org/wiki/Clustered_standard_errors

    Huber-White standard errors assume is diagonal but that the diagonal value varies, while other types of standard errors (e.g. Newey–West, Moulton SEs, Conley spatial SEs) make other restrictions on the form of this matrix to reduce the number of parameters that the practitioner needs to estimate. Clustered standard errors assume that is block ...

  8. Pooled variance - Wikipedia

    en.wikipedia.org/wiki/Pooled_variance

    Pooled variance is an estimate when there is a correlation between pooled data sets or the average of the data sets is not identical. Pooled variation is less precise the more non-zero the correlation or distant the averages between data sets. The variation of data for non-overlapping data sets is: where the mean is defined as: Given a biased ...

  9. Root mean square deviation - Wikipedia

    en.wikipedia.org/wiki/Root_mean_square_deviation

    In bioinformatics, the root mean square deviation of atomic positions is the measure of the average distance between the atoms of superimposed proteins. In structure based drug design, the RMSD is a measure of the difference between a crystal conformation of the ligand conformation and a docking prediction.