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  2. Numeric precision in Microsoft Excel - Wikipedia

    en.wikipedia.org/wiki/Numeric_precision_in...

    Excel graph of the difference between two evaluations of the smallest root of a quadratic: direct evaluation using the quadratic formula (accurate at smaller b) and an approximation for widely spaced roots (accurate for larger b). The difference reaches a minimum at the large dots, and round-off causes squiggles in the curves beyond this minimum.

  3. Symmetric mean absolute percentage error - Wikipedia

    en.wikipedia.org/wiki/Symmetric_mean_absolute...

    Provided the data are strictly positive, a better measure of relative accuracy can be obtained based on the log of the accuracy ratio: log(F t / A t) This measure is easier to analyze statistically and has valuable symmetry and unbiasedness properties

  4. Circular error probable - Wikipedia

    en.wikipedia.org/wiki/Circular_error_probable

    The concept of CEP also plays a role when measuring the accuracy of a position obtained by a navigation system, ... is given by the following formula: (, ...

  5. Microsoft Excel - Wikipedia

    en.wikipedia.org/wiki/Microsoft_Excel

    A backup of an Excel Spreadsheet Add-in (DLL) .xll: Adds custom functionality; written in C++/C, Fortran, etc. and compiled into a special dynamic-link library: Macro .xlm: A macro is created by the user or pre-installed with Excel. Template .xlt: A pre-formatted spreadsheet created by the user or by Microsoft Excel. Module .xlv

  6. Mean absolute percentage error - Wikipedia

    en.wikipedia.org/wiki/Mean_absolute_percentage_error

    This little-known but serious issue can be overcome by using an accuracy measure based on the logarithm of the accuracy ratio (the ratio of the predicted to actual value), given by ⁡ (). This approach leads to superior statistical properties and also leads to predictions which can be interpreted in terms of the geometric mean.

  7. Mean absolute scaled error - Wikipedia

    en.wikipedia.org/wiki/Mean_absolute_scaled_error

    It was proposed in 2005 by statistician Rob J. Hyndman and Professor of Decision Sciences Anne B. Koehler, who described it as a "generally applicable measurement of forecast accuracy without the problems seen in the other measurements."