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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 ...
As a general rule, rounding is idempotent; [2] i.e., once a number has been rounded, rounding it again to the same precision will not change its value. Rounding functions are also monotonic; i.e., rounding two numbers to the same absolute precision will not exchange their order (but may give the same value).
This effect mostly can be managed by meaningful rounding, which Excel does not apply: It is up to the user. Needless to say, other spreadsheets have similar problems, LibreOffice Calc uses a more aggressive rounding, while gnumeric tries to keep precision and make as well the precision as the 'lack of' visible for the user.
Precision is often the source of rounding errors in computation. The number of bits used to store a number will often cause some loss of accuracy. An example would be to store "sin(0.1)" in IEEE single precision floating point standard.
By this definition, ε equals the value of the unit in the last place relative to 1, i.e. () (where b is the base of the floating point system and p is the precision) and the unit roundoff is u = ε / 2, assuming round-to-nearest mode, and u = ε, assuming round-by-chop.
Accuracy is also used as a statistical measure of how well a binary classification test correctly identifies or excludes a condition. That is, the accuracy is the proportion of correct predictions (both true positives and true negatives) among the total number of cases examined. [10]
Sure, you may be able to deadlift a smidge more if you round your back, but the added 10-pound plate on each side isn’t worth it. “The risk to reward ratio just isn’t there,” says Morgan.
If the correction is also computed to ±1/2000 bit of accuracy (which does not require extra floating-point precision as long as the correction is less than 1/2000 the magnitude of the stored value f(x), and the computed correction is more than ±1/1000 of a bit away from exactly half a bit (the difficult rounding case), then it is known ...