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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]
For normally distributed random variables inverse-variance weighted averages can also be derived as the maximum likelihood estimate for the true value. Furthermore, from a Bayesian perspective the posterior distribution for the true value given normally distributed observations and a flat prior is a normal distribution with the inverse-variance weighted average as a mean and variance ().
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.
In this case, T 2 is more efficient than T 1 if the variance of T 2 is smaller than the variance of T 1, i.e. > for all values of θ. This relationship can be determined by simplifying the more general case above for mean squared error; since the expected value of an unbiased estimator is equal to the parameter value, E [ T ] = θ ...
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.
Place this template at the bottom of appropriate articles in statistics: {{Statistics}} For most articles transcluding this template, the name of that section of the template most relevant to the article (usually where a link to the article itself is found) should be added as a parameter. This configures the template to be shown with all but ...