Estimation is the calculated approximation of a result which is usable even if input data may be incomplete, uncertain, or noisy.
In statistics, see estimation theory, estimator.
In mathematics, approximation or estimation typically means finding upper or lower bounds of a quantity that cannot readily be computed precisely. While initial results may be unusably uncertain, recursive input from output, can purify results to be approximately accurate, certain, complete and noise-free.
In project management (ie. for engineering), accurate estimates are the basis of sound project planning, and are a critical component of . Many process which have been developed to aid engineers in making accurate estimates, such as
compartmentalization (ie. breakdown of tasks), structured planning, educated assumptions, identifying dependencies, examining historical data, estimating each task, and documenting the results
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An approximation is an inexact representation of something that is still close enough to be useful. Although approximation is most often applied to numbers, it is also frequently applied to such things as mathematical functions, shapes, and physical laws.
Approximations may be used because incomplete information prevents use of exact representations. Many problems in physics are either too complex to solve analytically, or impossible to solve. Thus, even when the exact representation is known, an approximation may yield a sufficiently accurate solution while reducing the complexity of the problem significantly.
For instance, physicists often approximate the shape of the Earth as a sphere even though more accurate representations are possible, because many physical behaviours — e.g. gravity — are much easier to calculate for a sphere than for less regular shapes.
The problem consisting of two or more planets orbiting around a sun has no exact solution. Often, ignoring the gravitational effects of the planets gravitational pull on each other and assuming that the sun does not move achieve a good approximation. The use of perturbations to correct for the errors can yield more accurate solutions. Simulations of the motions of the planets and the star also yields more accurate solutions.
The type of approximation used depends on the available information, the degree of accuracy required, the sensitivity of the problem to this data, and the savings (usually in time and effort) that can be achieved by approximation.