Search results
Results from the WOW.Com Content Network
When treating the weights as constants, and having a sample of n observations from uncorrelated random variables, all with the same variance and expectation (as is the case for i.i.d random variables), then the variance of the weighted mean can be estimated as the multiplication of the unweighted variance by Kish's design effect (see proof):
IAS 2 allows for two methods of costing, the standard technique and the retail technique. The standard technique requires that inventory be valued at the standard cost of each unit; that is, the usual cost per unit at the normal level of output and efficiency.
The maximum likelihood method weights the difference between fit and data using the same weights . The expected value of a random variable is the weighted average of the possible values it might take on, with the weights being the respective probabilities. More generally, the expected value of a function of a random variable is the probability ...
For normally distributed random variables inverse-variance weighted averages can also be derived as the maximum likelihood estimate for the true value. Furthermore, from a Bayesian perspective the posterior distribution for the true value given normally distributed observations and a flat prior is a normal distribution with the inverse-variance weighted average as a mean and variance ().
The average cost is computed by dividing the total cost of goods available for sale by the total units available for sale. This gives a weighted-average unit cost that is applied to the units in the ending inventory. There are two commonly used average cost methods: Simple weighted-average cost method and perpetual weighted-average cost method. [2]
Kernel average smoother example. The idea of the kernel average smoother is the following. For each data point X 0, choose a constant distance size λ (kernel radius, or window width for p = 1 dimension), and compute a weighted average for all data points that are closer than to X 0 (the closer to X 0 points get higher weights).
EVA = (r − c) × capital [the spread method, or excess return method] where r = rate of return, and c = cost of capital, or the weighted average cost of capital (WACC). NOPAT is profits derived from a company's operations after cash taxes but before financing costs and non-cash bookkeeping entries.
The weighted average return on assets, or WARA, is the collective rates of return on the various types of tangible and intangible assets of a company.. The presumption of a WARA is that each class of a company's asset base (such as manufacturing equipment, contracts, software, brand names, etc.) carries its own rate of return, each unique to the asset's underlying operational risk as well as ...