Search results
Results from the WOW.Com Content Network
The expected return (or expected gain) on a financial investment is the expected value of its return (of the profit on the investment). It is a measure of the center of the distribution of the random variable that is the return. [1]
Total revenue, the product price times the quantity of the product demanded, can be represented at an initial point by a rectangle with corners at the following four points on the demand graph: price (P 1), quantity demanded (Q 1), point A on the demand curve, and the origin (the intersection of the price axis and the quantity axis).
Despite the newly abstract situation, this definition is extremely similar in nature to the very simplest definition of expected values, given above, as certain weighted averages. This is because, in measure theory, the value of the Lebesgue integral of X is defined via weighted averages of approximations of X which take on finitely many values ...
In statistics, the method of estimating equations is a way of specifying how the parameters of a statistical model should be estimated. This can be thought of as a generalisation of many classical methods—the method of moments , least squares , and maximum likelihood —as well as some recent methods like M-estimators .
The marginal revenue function has twice the slope of the inverse demand function. [9] The marginal revenue function is below the inverse demand function at every positive quantity. [10] The inverse demand function can be used to derive the total and marginal revenue functions. Total revenue equals price, P, times quantity, Q, or TR = P×Q.
Price and total revenue have a negative relationship when demand is elastic (price elasticity > 1), which means that increases in price will lead to decreases in total revenue. Price changes will not affect total revenue when the demand is unit elastic (price elasticity = 1). Maximum total revenue is achieved where the elasticity of demand is 1.
Estimation (or estimating) is the process of finding an estimate or approximation, which is a value that is usable for some purpose even if input data may be incomplete, uncertain, or unstable. The value is nonetheless usable because it is derived from the best information available. [ 1 ]
Bias is a property of the estimator, not of the estimate. Often, people refer to a "biased estimate" or an "unbiased estimate", but they really are talking about an "estimate from a biased estimator", or an "estimate from an unbiased estimator". Also, people often confuse the "error" of a single estimate with the "bias" of an estimator.