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In the example from "Double rounding" section, rounding 9.46 to one decimal gives 9.4, which rounding to integer in turn gives 9. With binary arithmetic, this rounding is also called "round to odd" (not to be confused with "round half to odd"). For example, when rounding to 1/4 (0.01 in binary), x = 2.0 ⇒ result is 2 (10.00 in binary)
In computing, a roundoff error, [1] also called rounding error, [2] is the difference between the result produced by a given algorithm using exact arithmetic and the result produced by the same algorithm using finite-precision, rounded arithmetic. [3]
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.
Rounds (parameter 1) by (parameter 2) decimal places, and formats. Scientific notation is used for numbers greater than 1×10^9, or less than 1×10^−4. Template parameters [Edit template data] Parameter Description Type Status number 1 The number to be rounded Number required decimal places 2 The number of decimal places, if negative the number is rounded so the last (parameter 2) digits are ...
In decimal notation, a number ending in the digit "5" is also considered more round than one ending in another non-zero digit (but less round than any which ends with "0"). [2] [3] For example, the number 25 tends to be seen as more round than 24. Thus someone might say, upon turning 45, that their age is more round than when they turn 44 or 46.
For example, rounded to one decimal place, 2.304 would round to 2.3; it is not unchanged under the traditional rounding scheme, but rather rounded down, thus making five cases for rounding down and five for rounding up. If we consider rounding all of 0.00, 0.01, ..., 1.00 (101 numbers) to the nearest one, we get 50 0s and 51 1s.
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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 ...
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