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Excel maintains 15 figures in its numbers, but they are not always accurate; mathematically, the bottom line should be the same as the top line, in 'fp-math' the step '1 + 1/9000' leads to a rounding up as the first bit of the 14 bit tail '10111000110010' of the mantissa falling off the table when adding 1 is a '1', this up-rounding is not undone when subtracting the 1 again, since there is no ...
Rounding a number twice in succession to different levels of precision, with the latter precision being coarser, is not guaranteed to give the same result as rounding once to the final precision except in the case of directed rounding. [nb 2] For instance rounding 9.46 to one decimal gives 9.5, and then 10 when rounding to integer using ...
Round-by-chop: The base-expansion of is truncated after the ()-th digit. This rounding rule is biased because it always moves the result toward zero. Round-to-nearest: () is set to the nearest floating-point number to . When there is a tie, the floating-point number whose last stored digit is even (also, the last digit, in binary form, is equal ...
A cell on a different sheet of the same spreadsheet is usually addressed as: =SHEET2!A1 (that is; the first cell in sheet 2 of the same spreadsheet). Some spreadsheet implementations in Excel allow cell references to another spreadsheet (not the currently open and active file) on the same computer or a local network.
Data validation is intended to provide certain well-defined guarantees for fitness and consistency of data in an application or automated system. Data validation rules can be defined and designed using various methodologies, and be deployed in various contexts. [1]
For example, to round 1.25 to 2 significant figures: Round half away from zero rounds up to 1.3. This is the default rounding method implied in many disciplines [citation needed] if the required rounding method is not specified. Round half to even, which rounds to the nearest even number. With this method, 1.25 is rounded down to 1.2.
For example, while a fixed-point representation that allocates 8 decimal digits and 2 decimal places can represent the numbers 123456.78, 8765.43, 123.00, and so on, a floating-point representation with 8 decimal digits could also represent 1.2345678, 1234567.8, 0.000012345678, 12345678000000000, and so on.
For numbers with a base-2 exponent part of 0, i.e. numbers with an absolute value higher than or equal to 1 but lower than 2, an ULP is exactly 2 −23 or about 10 −7 in single precision, and exactly 2 −53 or about 10 −16 in double precision. The mandated behavior of IEEE-compliant hardware is that the result be within one-half of a ULP.