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In statistics, efficiency is a measure of quality of an estimator, of an experimental design, [1] or of a hypothesis testing procedure. [2] Essentially, a more efficient estimator needs fewer input data or observations than a less efficient one to achieve the Cramér–Rao bound.
In variance analysis, direct material usage (efficiency, quantity) variance is the difference between the standard quantity of materials that should have been used for the number of units actually produced, and the actual quantity of materials used, valued at the standard cost per unit of material.
Labour Rate Variance is the difference between the standard cost and the actual cost paid for the actual number of hours. Labour efficiency variance is the difference between the standard labour hour that should have been worked for the actual number of units produced and the actual number of hours worked when the labour hours are valued at the ...
The variance of randomly generated points within a unit square can be reduced through a stratification process. In mathematics , more specifically in the theory of Monte Carlo methods , variance reduction is a procedure used to increase the precision of the estimates obtained for a given simulation or computational effort. [ 1 ]
Improvement in variance efficiency might sometimes be sacrificed for convenience or cost. For example, in the cluster sampling case the units may have equal or unequal selection probabilities, irrespective of their intra-class correlation (and their negative effect of increasing the variance of the estimators).
Because of the reciprocity of estimator-variance and Fisher information, minimizing the variance corresponds to maximizing the information. When the linear (or linearized) statistical model has several parameters, the mean of the parameter estimator is a vector and its variance is a matrix. The inverse of the variance matrix is called the ...
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.
The Nash–Sutcliffe coefficient masks important behaviors that if re-cast can aid in the interpretation of the different sources of model behavior in terms of bias, random, and other components. [11]