If, and only if the positive value is > the negative value.
It depends on what the value of x is. If x is a positive number, then it will be negative because a negative number multiplied by a positive number is negative. If x is a negative number, then it will be positive because the product of two negative numbers is always a positive number.
Sometimes you will take the absolute value of the percent error because your estimated number could be less than the theoretical, meaning the calculation is negative. But an absolute value is always positive. A percent error can be left as a negative though, and this would be perfectly acceptable (or even preferred) depending on what you're doing.Answer:In the sciences, a negative percent error indicates a low result. If you have a 0% error, then your observed (lab) result was exactly the same as the theoretical result. A 5% error could mean that your observed result was a little high. A negative percent error is possible; if your observed results were lower than the expected, then you would have a negative percent error. A -5% error could mean that your results were a little low. Having a negative percent error isn't worse than positive percent error -- it could mean the same thing. If you were to have a choice in having a 20% error and a -5% error, the negative percent error is more accurate.
Positive plus positive is positive. Negative plus negative is negative. Positive plus negative is positive if the absolute value of the positive number is greater than the absolute value of the negative one. Positive plus negative is negative if the absolute value of the negative number is greater than the absolute value of the positive one.
The absolute value is only ever positive. * * * * * Or 0.
The polarity of a random error refers to whether the error is positive or negative relative to the true value. In statistical analysis, random errors can be equally likely to be positive or negative, and their effect should cancel out when many measurements are averaged. Monitoring polarity can help identify biases or systematic errors in data collection or measurement processes.
No because taking the absolute value of a number always yields a positive value.
No, the product of the multiplication of a positive and a negative value is negative.
If, and only if the positive value is > the negative value.
Divide the calculated or estimated error by the magnitude of the measurement. Take the absolute value of the result, that is, if it is negative, convert to positive. This would make the percent error = | error / measurement |.
If the absolute value of the negative is bigger than that of the positive, then the answer is negative. If the absolute value of the negative is the same, then zero. If the absolute value of the negative is smaller, then positive. Absolute value is the value ignoring the sign.
Negative
There is no negative of zero, nor is there a positive. Zero is no value, hence it has no positive or negative value.
If negative value>positive value then result is -ve If positve value>negative value then result is +ve
It is a negative if the negative number has the higher absolute value and positive if the positive number has the higher absolute value.
Depending on whether you subtract actual value from expected value or other way around, a positive or negative percent error, will tell you on which side of the expected value that your actual value is. For example, suppose your expected value is 24, and your actual value is 24.3 then if you do the following calculation to figure percent error:[percent error] = (actual value - expected value)/(actual value) - 1 --> then convert to percent.So you have (24.3 - 24)/24 -1 = .0125 --> 1.25%, which tells me the actual is higher than the expected. If instead, you subtracted the actual from the expected, then you would get a negative 1.25%, but your actual is still greater than the expected. My preference is to subtract the expected from the actual. That way a positive error tells you the actual is greater than expected, and a negative percent error tells you that the actual is less than the expected.
the absolute value for a negative or positive value is always positive