One of the intended use cases of my DMTD is to measure minute frequency differences to enable the frequency tuning of clocks against a reference. A typical frequency display update for this use case would be at most every second. As the DMTD can only measure phase this phase has to be converted into frequency. The advice I received was to make  many short duration phase measurements and use a linear regression on those phase measurements to calculate the frequency at the display update interval. A typical display of the measured frequency using linear regression over 100 phase measurements and the noise of the phase measurements (green trace) can be found here: http://athome.kaashoek.com/time-nuts/DMTD/freq_capture.png An alternative method would be to measure the phase at the required display update frequency and convert the measured phase and the previously measured phase and the measurement interval into the frequency. But what method delivers the most accurate frequency? Linear regression accuracy improves with the number of measurements involved but the accuracy of the phase measurements decreases with shorter phase measurements. To determine accuracy, both methods where used to measure two 10 MHz clocks with a 10 microHz frequency difference over a period of 100 seconds. Using these 100 measurements the ADEV was calculated and compared for a tau of 1 second. The method using linear regression and 100 phase measurements every second had an ADEV at 1 second of 2.1e-13, the method using a single phase measurement every second had an ADEV at 1 second of 1.2e-13. At first it seems the second method has an advantage but as its using the phase measurement of the previous second it uses twice the measurement data so in practice I feel there is no difference. Is this to be expected?