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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.
According to the PMBOK (7th edition) by the Project Management Institute (PMI), Cost variance (CV) is a "The amount of budget deficit or surplus at a given point in time, expressed as the difference between the earned value and the actual cost." [19] Cost variance compares the estimated cost of a deliverable with the actual cost. [20]
This algorithm can easily be adapted to compute the variance of a finite population: simply divide by n instead of n − 1 on the last line.. Because SumSq and (Sum×Sum)/n can be very similar numbers, cancellation can lead to the precision of the result to be much less than the inherent precision of the floating-point arithmetic used to perform the computation.
If the set is a sample from the whole population, then the unbiased sample variance can be calculated as 1017.538 that is the sum of the squared deviations about the mean of the sample, divided by 11 instead of 12. A function VAR.S in Microsoft Excel gives the unbiased sample variance while VAR.P is for population variance.
Examples of fixed costs include the depreciation of plant and equipment, and the cost of departments such as maintenance, tooling, production control, purchasing, quality control, storage and handling, plant supervision and engineering. [4] In the early nineteenth century, these costs were of little importance to most businesses.
Price variance (Vmp) is a term used in cost accounting which denotes the difference between the expected cost of an item (standard cost) and the actual cost at the time of purchase. [1] The price of an item is often affected by the quantity of items ordered, and this is taken into consideration.
Even Markowitz, himself, stated that "semi-variance is the more plausible measure of risk" than his mean-variance theory. [5] Later in 1970, several focus groups were performed where executives from eight industries were asked about their definition of risk resulting in semi-variance being a better indicator than ordinary variance. [6]
Analysers consider two types of variances: adverse variance and favourable variance. Adverse variance "exists when the difference between the budgeted and actual figure leads to a lower than expected profit". [14] Favourable variance "exists when the difference between the budgeted and actual figure leads to a higher than expected profit". [14]