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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):
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 ...
Exponential smoothing or exponential moving average (EMA) is a rule of thumb technique for smoothing time series data using the exponential window function. Whereas in the simple moving average the past observations are weighted equally, exponential functions are used to assign exponentially decreasing weights over time. It is an easily learned ...
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]
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 ().
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.
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 cost of capital (WACC) is an approach to determining a discount rate that incorporates both equity and debt financing; the method determines the subject company's actual cost of capital by calculating the weighted average of the company's cost of debt and cost of equity.