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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-weighted average of the values the function takes on for each possible value of the random variable.
Unweighted, or "elementary", price indices only compare prices of a single type of good between two periods. They do not make any use of quantities or expenditure weights. They are called "elementary" because they are often used at the lower levels of aggregation for more comprehensive price indices. [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 ().
In the financial field, and more specifically in the analyses of financial data, a weighted moving average (WMA) has the specific meaning of weights that decrease in arithmetical progression. [4] In an n -day WMA the latest day has weight n , the second latest n − 1 {\displaystyle n-1} , etc., down to one.
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 ...
Inverse probability weighting is a statistical technique for estimating quantities related to a population other than the one from which the data was collected. Study designs with a disparate sampling population and population of target inference (target population) are common in application. [1]
The sample mean is the average of the values of a variable in a sample, which is the sum of those values divided by the number of values. Using mathematical notation, if a sample of N observations on variable X is taken from the population, the sample mean is: ¯ = =.
Stock market indices may be categorized by their index weight methodology, or the rules on how stocks are allocated in the index, independent of its stock coverage. For example, the S&P 500 and the S&P 500 Equal Weight each cover the same group of stocks, but the S&P 500 is weighted by market capitalization, while the S&P 500 Equal Weight places equal weight on each constituent.