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You must sample at 2 x the rate of the analog signal (2 x the analog signal frequency).
The central limit theorem basically states that as the sample size gets large enough, the sampling distribution becomes more normal regardless of the population distribution.
Yes, and more so for larger samples. (It follows from the Central Limit Theorem.)
It takes too much time and effort to check each transaction.
If the signal is bandwidth to the fm Hz means signal which has no frequency higher than fm can be recovered completely from set of sample taken at the rate
sampling theorem is used to know about sample signal.
sampling theorem is defined as , the sampling frequency should be greater than or equal to 2*maximum frequency, and the frequency should be bounded.. i,e fs=2*fmax where fs= sampling frequency
sam. theorm
The central limit theorem can be used to determine the shape of a sampling distribution in which of the following scenarios?
I cannot see where the Nyquist theorem relates to cables, fiber or not.The theorem I know, the Nyquist-Shannon sampling theorem, talks about the limitations in sampling a continuous (analog) signal at discrete intervals to turn it into digital form.An optical fiber or other cable merely transport bits, there is no analog/digital conversion and no sampling taking place.
what the importance of studying in theorem Bernoulli in civil engineering
sampling is a one type of process use for converting into analog signal to digital signal.
Sampling Theorum is related to signal processing and telecommunications. Sampling is the process of converting a signal into a numeric sequence. The sampling theorum gives you a rule using DT signals to transmit or receive information accurately.
This is the Central Limit Theorem.
the central limit theorem
The Nyquist Theorem says that the sampling frequency should be twice the bandwidth to avoid aliasing. Thus if the bandwidth of the system is bw then the sampling frequency f=2*bw.
Thanks to the Central Limit Theorem, the sampling distribution of the mean is Gaussian (normal) whose mean is the population mean and whose standard deviation is the sample standard error.