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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.
A pre-determined overhead rate is normally the term when using a single, plant-wide base to calculate and apply overhead. Overhead is then applied by multiplying the pre-determined overhead rate by the actual driver units. Any difference between applied overhead and the amount of overhead actually incurred is called over- or under-applied overhead.
In statistics, the variance inflation factor (VIF) is the ratio of the variance of a parameter estimate when fitting a full model that includes other parameters to the variance of the parameter estimate if the model is fit with only the parameter on its own. [1]
Variance analysis can be carried out for both costs and revenues. Variance analysis is usually associated with explaining the difference (or variance) between actual costs and the standard costs allowed for the good output. For example, the difference in materials costs can be divided into a materials price variance and a materials usage variance.
For a set of numbers {10, 15, 30, 45, 57, 52 63, 72, 81, 93, 102, 105}, if this set is the whole data population for some measurement, then variance is the population variance 932.743 as the sum of the squared deviations about the mean of this set, divided by 12 as the number of the set members.
In statistics, explained variation measures the proportion to which a mathematical model accounts for the variation of a given data set.Often, variation is quantified as variance; then, the more specific term explained variance can be used.
In statistics, the variance function is a smooth function that depicts the variance of a random quantity as a function of its mean.The variance function is a measure of heteroscedasticity and plays a large role in many settings of statistical modelling.
A i is the number of data type A at sample site i, B i is the number of data type B at sample site i, K is the number of sites sampled and || is the absolute value. This index is probably better known as the index of dissimilarity (D). [44] It is closely related to the Gini index. This index is biased as its expectation under a uniform ...