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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.
The level of variable cost is influenced by many factors, such as fixed cost, duration of project, uncertainty and discount rate. An analytical formula of variable cost as a function of these factors has been derived. It can be used to assess how different factors impact variable cost and total return in an investment. [5]
Difference between the amount of labor time that should have been used and the labor that was actually used, multiplied by the standard rate. For example, assume that the standard cost of direct labor per unit of product A is 2.5 hours x $14 = $35. Assume further that during the month of March the company recorded 4500 hours of direct labor time.
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.
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.
How variable rate caps work. In many cases, lenders set caps on variable-rate products. This was designed to protect consumer borrowers from the kind of runaway interest the country saw during the ...
In statistics, dispersion (also called variability, scatter, or spread) is the extent to which a distribution is stretched or squeezed. [1] Common examples of measures of statistical dispersion are the variance, standard deviation, and interquartile range. For instance, when the variance of data in a set is large, the data is widely scattered.