You want some measure of how the observations are spread about the mean. If you used the deviations their sum would be zero which would provide no useful information. You could use absolute deviations instead.
The sum of squared deviations turns out to have some useful statistical properties including a relatively simple way of calculating it. For example, the Gaussian (or Normal) distribution is completely defined by its mean and variance.
For the same reason that numbers in ordinary notation need computing.
Generally not without further reason. Extreme values are often called outliers. Eliminating unusually high values will lower the standard deviation. You may want to calculate standard deviations with and without the extreme values to identify their impact on calculations. See related link for additional discussion.
Squared, not squard, means multiplied by itself. The reason is that the area of a square, with sides of length, s is s*s: the side length multiplied by itself. So, for example, 3.5 squared = 3.5*3.5 = 12.25 3.5 squared can also be written as 3.52.
In most production management systems, a "Planned" quantity and material cost is calculated based on the associated Bill of Materials (BOM) and Operatons being performed (Route) creating labor and overhead related costs. The "Actual" quantities, material costs, and labor/overhead costs are issued to a Work in Process (WIP) account and the quantities/values of the produced items are recieved from the WIP account. A variance usually occurs when there is a difference between the issued material cost plus labor and overhead and the recieved material cost of the produced item. The reasons for these variances can be differences in planned vs actual quantities, differences in system or planned cost of materials, labor, or overhead vs actual cost, or any other potential reason for an unplanned difference.
The square. Reason : 10 feet X 10 feet = 100 feet squared (square) 9 feet X 4 feet = 36 feet squared (rectangle)
No, a standard deviation or variance does not have a negative sign. The reason for this is that the deviations from the mean are squared in the formula. Deviations are squared to get rid of signs. In Absolute mean deviation, sum of the deviations is taken ignoring the signs, but there is no justification for doing so. (deviations are not squared here)
ABSURDITY
to organize similar data
With the advent of Cloud Computing, there is no more reason for you computing for individual users can be hosted in a professional data center instead of on a desk.
For the same reason that numbers in ordinary notation need computing.
You need a job at the end of it?
A favorable/unfavorable price variance does not effect your quantity variance. The reason you would see a favorable price variance and an unfavorable quantity variance is because you consumed more materials than your standard allows AND the price you paid for those material was less than your standard price. If you paid more than your standard price, you would have experienced an unfavorable variance in both quantity and price.
Generally not without further reason. Extreme values are often called outliers. Eliminating unusually high values will lower the standard deviation. You may want to calculate standard deviations with and without the extreme values to identify their impact on calculations. See related link for additional discussion.
The sum of deviations from the mean, for any set of numbers, is always zero. For this reason it is quite useless.
for profit.........
Cubed. The reason is that space has three dimensions - and that is basically what we are measuring.
Your question is a bit difficult to answer, as "succinct" is usually a quality in reference to a description or explanation. It is defined by Webster's dictionary as "marked by compact precise expression without wasted words." See related link. For this reason, I have reworded your question as follows: Does the variance fully describe or summarize the raw data? The answer is no. For any set of data, many statistical measures can be calculated, including the mean and variance. The variance or more commonly the square of the variance (standard deviation) is a very useful in identifying the dispersion of data, but is incomplete in fully describing the data. The mean is also important. Graphs can improve the summarization of data in a more visual manner.