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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.
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.
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 probability theory and statistics, variance is the expected value of the squared deviation from the mean of a random variable. The standard deviation (SD) is obtained as the square root of the variance.
where R 2 is the coefficient of determination and VAR err and VAR tot are the variance of the residuals and the sample variance of the dependent variable. SS err (the sum of squared predictions errors, equivalently the residual sum of squares ), SS tot (the total sum of squares ), and SS reg (the sum of squares of the regression, equivalently ...
In variance analysis (accounting) direct material total variance is the difference between the actual cost of actual number of units produced and its budgeted cost in terms of material. Direct material total variance can be divided into two components: the direct material price variance, the direct material usage variance.
The variance of the random variable resulting from an algebraic operation between random variables can be calculated using the following set of rules: Addition : V a r [ Z ] = V a r [ X + Y ] = V a r [ X ] + 2 C o v [ X , Y ] + V a r [ Y ] {\displaystyle \mathrm {Var} [Z]=\mathrm {Var} [X+Y]=\mathrm {Var} [X]+2\mathrm {Cov} [X,Y]+\mathrm {Var ...
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.