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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]
[nb 2] For instance rounding 9.46 to one decimal gives 9.5, and then 10 when rounding to integer using rounding half to even, but would give 9 when rounded to integer directly. Borman and Chatfield [15] discuss the implications of double rounding when comparing data rounded to one decimal place to specification limits expressed using integers.
The significance of trailing zeros in a number not containing a decimal point can be ambiguous. For example, it may not always be clear if the number 1300 is precise to the nearest unit (just happens coincidentally to be an exact multiple of a hundred) or if it is only shown to the nearest hundreds due to rounding or uncertainty.
As most decimal values do not have a clean finite representation in binary they will suffer from 'round off' and 'cancellation' in tasks like the above. E.g. decimal 0.1 has the IEEE double representation 0 (1).1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 1010 × 2^(-4) ; when added to 140737488355328.0 (which is 2 +47 ) it will ...
The calculator was code-named Wizard, [4] which is the first known use of a code name for a calculator. ... 0 – 9 decimal places round-off. Emulators
This alternative definition is significantly more widespread: machine epsilon is the difference between 1 and the next larger floating point number.This definition is used in language constants in Ada, C, C++, Fortran, MATLAB, Mathematica, Octave, Pascal, Python and Rust etc., and defined in textbooks like «Numerical Recipes» by Press et al.
|N (where N is a number) displays N digits after the decimal mark (round output to N decimal places). |-N (where -N is a negative number) replaces N digits before the decimal mark with zero (round output to nearest 10 N). |sigfig=N (where N is a positive number) to specify the number of significant digits (round output to N significant figures).
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.