Budget variance is calculated using variance analysis to compare planned, or budgeted, amounts to actual amounts. Variance analysis is a quantitative examination of the differences between budgeted and actual amounts, according to AccountingTools.
To calculate simple variance, also called sample variance, start by calculating the mean of the set of numbers. Next, subtract the mean and square the result for each number. Finally, add the squared results, and divide this sum by one less than the number of data points.
Calculate variance by finding the average of the square of the data values' differences from the mean. You need the data values, mean, squared mean difference and the number of data values in the data set.
Schedule variance occurs when the time needed to complete a project is different from the time scheduled for completion. In some cases projects get completed early. However, schedule variance more often refers to situations in which the timeline for completion exceeds the amount scheduled.
Variance measures how the data in a given variable distribution are spread relative to the mean. Operationally, it is the average squared distance from the mean and is calculated by the square root of the standard deviation. Conceptually, it indicates how much variation is within a given sample.
Variance analysis accounts for discrepancies between planned events and what actually takes place. It is generally performed at the end of each fiscal month and reported to management. Variance analysis is particularly useful in markets with consistent monthly trends.
There are two basic ways of calculating variance in Excel using the function VAR or VAR.S. These functions can then calculate variance in several ways: use of numbers in arguments of function =VAR(2,3,4,5,6,7,8,125), use of cells as arguments in the formula =VAR(A2,A3,A4,A5), use of a range of cells
To calculate the sample variance of a population, first determine the mean of the sample, subtract each data point from the mean, square each resulting number, add all the squared results together, and then divide that number by the total number of points in the data set minus one. A one is subtract
The variance is the second central moment of a continuous probability distribution. The variance of a continuous uniform distribution on the interval [a, b] is (1/12)*(b - a)^2.
By its very definition, variance cannot be negative. Variance is the measure of how spread out a distribution is, and distance can never be negative. Additionally, the formula for variance ensures that its result cannot be negative.