The periodogram method divides the signal into a number of shorter
(and often overlapped) blocks of data, computes the squared magnitude
of the windowed
(and usually zero-padded)
DFT,
*total* number of samples,
this introduces a tradeoff: Larger individual data blocks provides
better frequency resolution due to the use of a longer window,
but it means there are less blocks to average, so the estimate
has higher variance and appears more noisy.
The best tradeoff depends on the application.
Overlapping blocks by a factor of two to four increases the number
of averages and reduces the variance, but since the same data is being
reused, still more overlapping does not further reduce the variance.
As with any window-based spectrum estimation procedure, the window
function introduces broadening and sidelobes into the power spectrum
estimate.
That is, the periodogram produces an estimate of the *windowed* spectrum

### Example 1

Figure 1 shows the non-negative frequencies of the DFT (zero-padded to 1024 total samples) of 64 samples of a real-valued stochastic signal.