Mathematical Errors
Mathematical Errors
(1) Inherent Errors :
Errors which are already present in the statement of a problem before its solution , are called inherent errors . Such errors arise either due to the given data being mathematical tables , calculators or the digital computer . Inherent errors can be minimized by taking better data or by using high precision computing aids .
(2) Rounding Errors :
It arises from the process of rounding off the numbers during the computation . Such errors are unavoidable in most of the calculations due to the limitations of the computing aids . Rounding errors can however be reduced by retaining at least one more significant figure at each step than that given in the data and rounding off at the last step .
(3) Truncation Errors :
These are used by using approximate results or on replacing an infinite process by a finite one . If we are using a decimal computer having a fixed word length of 4 digits , rounding off of 13.658 gives 13.66 whereas truncation gives 13.65 .
For example ,
if eˣ = 1+x + x²/2! +x³/3! +.....∞ = X (say) is replaced by 1+x+x²/2! + x³/3! = X' (say)
then the truncation error is X-X'
Truncation error is a type of algorithm error .
For example ,
if eˣ = 1+x + x²/2! +x³/3! +.....∞ = X (say) is replaced by 1+x+x²/2! + x³/3! = X' (say)
then the truncation error is X-X'
Truncation error is a type of algorithm error .
(4) Absolute , Relative,Percentage Errors :
If X is the true value of a quantity and X' is its approximate value , then |X-X'| is called the absolute error Eₐ .
The relative error is defined by
Eᵣ = |X-X' / X |
and the percentage error is
Eₚ = 100Eᵣ = 100 × |X-X' /X|
If X̅ be such a number that
|X-X'| ≤ X̅
then X̅ is an upper limit on the magnitude of absolute error and measures the absolute accuracy .
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