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The plots at the bottom show the signal intensity in the indicated row of the image (red: original signal, blue: with noise). Signal-to-noise ratio ( SNR or S/N) is a measure used in science and engineering that compares the level of a desired signal to the level of background noise. SNR is defined as the ratio of signal power to noise power ...
Wide FM, 20.0 kHz width, ±5 kHz deviation, still widely used for amateur radio, NOAA weather radio, marine, and aviation users and land mobile users below 50 MHz [2] 11K2 F3E Narrow FM, 11.25 kHz bandwidth, ±2.5 kHz deviation – In the United States, all Part 90 Land Mobile Radio Service (LMRS) users operating above 50 MHz were required to ...
is the reference distance, usually 1 km (or 1 mile) for a large cell and 1 m to 10 m for a microcell. [1] is the path loss exponent. is a normal (Gaussian) random variable with zero mean, reflecting the attenuation (in decibels) caused by flat fading [citation needed]. In the case of no fading, this variable is 0.
For the music theory concept of "modes", see Mode (music). In statistics, the mode is the value that appears most often in a set of data values. [ 1 ] If X is a discrete random variable, the mode is the value x at which the probability mass function takes its maximum value (i.e., x=argmaxxi P (X = xi) ). In other words, it is the value that is ...
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it.
Average absolute deviation. The average absolute deviation ( AAD) of a data set is the average of the absolute deviations from a central point. It is a summary statistic of statistical dispersion or variability. In the general form, the central point can be a mean, median, mode, or the result of any other measure of central tendency or any ...
Another method of grouping the data is to use some qualitative characteristics instead of numerical intervals. For example, suppose in the above example, there are three types of students: 1) Below normal, if the response time is 5 to 14 seconds, 2) normal if it is between 15 and 24 seconds, and 3) above normal if it is 25 seconds or more, then the grouped data looks like:
Sum ← Sum + x. SumSq ← SumSq + x × x. Var = (SumSq − (Sum × Sum) / n) / (n − 1) This algorithm can easily be adapted to compute the variance of a finite population: simply divide by n instead of n − 1 on the last line. Because SumSq and (Sum×Sum)/n can be very similar numbers, cancellation can lead to the precision of the result to ...