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The long-run cost curve is a cost function that models this minimum cost over time, meaning inputs are not fixed. Using the long-run cost curve, firms can scale their means of production to reduce the costs of producing the good. [1] There are three principal cost functions (or 'curves') used in microeconomic analysis:
The total cost curve, if non-linear, can represent increasing and diminishing marginal returns.. The short-run total cost (SRTC) and long-run total cost (LRTC) curves are increasing in the quantity of output produced because producing more output requires more labor usage in both the short and long runs, and because in the long run producing more output involves using more of the physical ...
The theory was made rigorous a few decades later by Eudoxus of Cnidus, who used it to calculate areas and volumes. It was later reinvented in China by Liu Hui in the 3rd century AD in order to find the area of a circle. [2] The first use of the term was in 1647 by Gregory of Saint Vincent in Opus geometricum quadraturae circuli et sectionum.
It says what fraction of the variance of the data is explained by the fitted trend line. It does not relate to the statistical significance of the trend line (see graph); the statistical significance of the trend is determined by its t-statistic. Often, filtering a series increases r 2 while making little difference to the fitted trend.
The blue area above the x-axis may be specified as positive area, while the yellow area below the x-axis is the negative area. The integral of a real function can be imagined as the signed area between the x {\displaystyle x} -axis and the curve y = f ( x ) {\displaystyle y=f(x)} over an interval [ a , b ].
The set of all these efficient points that could be traded to is the contract curve. In the graph below, the initial endowments of the two people are at point X, on Kelvin's indifference curve K 1 and Jane's indifference curve J 1. From there they could agree to a mutually beneficial trade to anywhere in the lens formed by these indifference ...
A classification model (classifier or diagnosis [7]) is a mapping of instances between certain classes/groups.Because the classifier or diagnosis result can be an arbitrary real value (continuous output), the classifier boundary between classes must be determined by a threshold value (for instance, to determine whether a person has hypertension based on a blood pressure measure).
The nested triangles graph requires this much area no matter how it is embedded, [2] and several methods are known that can draw planar graphs with at most quadratic area. [3] [4] Binary trees, and trees of bounded degree more generally, have drawings with linear or near-linear area, depending on the drawing style.