Assertion: signal's noise | /r/HasProperty | white
Facets:
Source sentences:
text: If the noise is "white" (that is, evenly distributed over all frequencies) and its standard deviation is D , then the standard deviation of the noise remaining in the signal after one pass of a rectangular smooth will be approximately D /sqrt( m ), where m is the smooth width.
text: (Similarly, if both peaks are measured with the least-squares curve fitting method to be covered later, the fit of the first peak is more stable with the noise and the measured parameters of that peak will be about 3 times more accurate than the second peak, because there are 10 times more data points in that peak, and the measurement precision improves roughly with the square root of the number of data points if the noise is white).
text: (If the noise in the original signal is white, and the signal is condensed by averaging every n points, the noise is reduced in the condensed signal by the square root of n , but with no change in frequency distribution of the noise).
text: Smoothing is useful if the signal is contaminated by non-normal noise such as sharp spikes or if the peak height, position, or width are measured by simple methods, but there is no need to smooth the data if the noise is white and the peak parameters are measured by least-squares methods, because the least-squares results obtained on the unsmoothed data will be more accurate (see CurveFittingC.html#Smoothing).