Understanding RMS vs. Absolute Value for Calculating Averages in Data Analysis

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Root Mean Square (RMS) is preferred over absolute value for calculating averages in data analysis because it accounts for the power consumption associated with negative data. RMS is defined as the square root of the average of the squared values, making it more relevant for applications like alternating current, where power depends on the square of the voltage. The RMS voltage provides a DC equivalent that reflects the heating effect of the waveform, which is crucial for accurate power calculations. In contrast, the average of absolute values does not capture the increased heating effect from higher peaks in the waveform. Understanding these differences is essential for accurate data analysis in electrical engineering contexts.
gsingh2011
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I never really understood why using the Root Mean Square of negative data is the preferred method of finding the average of the data as opposed to taking the absolute value of the data and taking the average (arithmetic mean) of that. The example that recently made me wonder about this is alternating current. The RMS current is the maximum current divided by root two. But why isn't the average current simply the average value after taking the absolute value of the current?
 
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gsingh2011 said:
I never really understood why using the Root Mean Square of negative data is the preferred method of finding the average of the data as opposed to taking the absolute value of the data and taking the average (arithmetic mean) of that. The example that recently made me wonder about this is alternating current. The RMS current is the maximum current divided by root two. But why isn't the average current simply the average value after taking the absolute value of the current?


THe RMS current is not the average current, it is the root of the average squared current. You do not use RMS current/average current interchangeably, I believe (but hey I'm not a physicist/EE)
 
The RMS current is useful because it's directly related to the power consumption.
 
gsingh2011 said:
I never really understood why using the Root Mean Square of negative data is the preferred method of finding the average of the data as opposed to taking the absolute value of the data and taking the average (arithmetic mean) of that. The example that recently made me wonder about this is alternating current. The RMS current is the maximum current divided by root two. But why isn't the average current simply the average value after taking the absolute value of the current?

The difference is that power depends on the square of the voltage, not just the voltage.
The RMS voltage of a waveform is the DC voltage that would have the same heating effect as the this waveform.

So, the parts of the waveform that are twice as big as others have 4 times as much heating ability.

The average voltage of a sinewave (allowing for absolute values) is 0.637 times the peak value, but the RMS value is 0.707 times the peak value.
The difference between these values is due to the heating effect of the parts of the sinewave near the peak value.
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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