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Text normalization is the process of transforming text into a single canonical form that it might not have had before. Normalizing text before storing or processing it allows for separation of concerns, since input is guaranteed to be consistent before operations are performed on it. Text normalization requires being aware of what type of text ...
Excel offers many user interface tweaks over the earliest electronic spreadsheets; however, the essence remains the same as in the original spreadsheet software, VisiCalc: the program displays cells organized in rows and columns, and each cell may contain data or a formula, with relative or absolute references to other cells.
In another usage in statistics, normalization refers to the creation of shifted and scaled versions of statistics, where the intention is that these normalized values allow the comparison of corresponding normalized values for different datasets in a way that eliminates the effects of certain gross influences, as in an anomaly time series. Some ...
Selecting the target range depends on the nature of the data. The general formula for a min-max of [0, 1] is given as: [3] ′ = () where is an original value, ′ is the normalized value. For example, suppose that we have the students' weight data, and the students' weights span [160 pounds, 200 pounds].
The key feature of spreadsheets is the ability for a formula to refer to the contents of other cells, which may, in turn, be the result of a formula. To make such a formula, one replaces a number with a cell reference. For instance, the formula =5*C10 would produce the result of multiplying the value in cell C10 by the number 5.
A rewriting system is strongly normalizing, terminating, noetherian, or has the (strong) normalization property (SN), if each of its objects is strongly normalizing. [ 2 ] A rewriting system has the normal form property (NF) if for all objects a and normal forms b , b can be reached from a by a series of rewrites and inverse rewrites only if a ...
(Or one can do it in the opposite direction, first calculating and then from those. The two give equivalent results.) The two give equivalent results.) At the edges of the dataset, when one is missing some of the surrounding points, the missing points can be approximated by a number of methods.
The Voigt profile is normalized: (;,) =,since it is a convolution of normalized profiles. The Lorentzian profile has no moments (other than the zeroth), and so the moment-generating function for the Cauchy distribution is not defined.
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