What is the intuition behind root mean square?

In summary: Thanks but why the distance needs to divide by n(in this case n=3)?ie\frac{\sqrt{(x^2+ y^2+ z^2)}}{n} Seems like it is the average distance of each dimension in the n-dimension to the original point. Am I right?If yes, may I know what is the significant value to define it as such? n is the number of dimensions in the set. So it is the size of the set divided by the number of dimensions.
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  • #2
Essentially the root mean square is a distance. If you were to calculate the distance the point (x, y, z) is from (0,0,0) you would calculate [itex]\sqrt{x^2+ y^2+ z^2}[/itex]. The "root mean square" is really an "average distance", thinking of each value in the set as a "dimension".

Of course, that's not the only way to define an "average". Also used is to, not "inverse" every sign since that would mean changing positive to negative, take the arithmetic average of the absolute values:
[tex]\frac{|a_1|+ |a_2|+ \cdot\cdot\cdot+ |a_n|}{n}[/tex]
 
  • #3
Thanks but why the distance needs to divide by n(in this case n=3)?
ie
[tex]\frac{\sqrt{(x^2+ y^2+ z^2)}{n}[/tex]

Seems like it is the average distance of each dimension in the n-dimension to the original point. Am I right?
If yes, may I know what is the significant value to define it as such?

>>not "inverse" every sign since that would mean changing positive to negative
Sorry typo, it should be taking absolute value as you mentioned:)
 
Last edited:
  • #4
jack1234 said:
Thanks but why the distance needs to divide by n(in this case n=3)?
ie
[tex]\frac{\sqrt{(x^2+ y^2+ z^2)}}{n}[/tex]

Seems like it is the average distance of each dimension in the n-dimension to the original point. Am I right?
If yes, may I know what is the significant value to define it as such?
When you measure an "average" of a number of things you are, basically, measuring 'how large' they are. A distance measure is a natural analogy to use.

>>not "inverse" every sign since that would mean changing positive to negative
Sorry typo, it should be taking absolute value as you mentioned:)
 

What is the intuition behind root mean square?

The root mean square (RMS) is a statistical measure that is used to find the average value of a set of numbers. It takes into account both the magnitude and direction of the numbers, making it a useful tool for analyzing data that includes both positive and negative values.

How is root mean square calculated?

The formula for calculating root mean square is the square root of the sum of the squared values, divided by the number of values in the set. In mathematical notation, it is written as RMS = √(ΣX²/n), where X is each value in the set and n is the number of values.

Why is root mean square used in data analysis?

Root mean square is commonly used in data analysis because it provides a more accurate representation of the data compared to other measures of central tendency, such as the arithmetic mean. It takes into account the variability of the data, making it a better measure for data sets with a wide range of values.

What are the advantages of using root mean square?

One advantage of using root mean square is that it gives equal weight to all values in the data set. This means that outliers or extreme values do not have a disproportionate influence on the result. Additionally, RMS is a useful tool for comparing data sets with different units or scales, as it standardizes the values.

When should root mean square not be used?

Root mean square should not be used when the data set contains a large number of extreme values, as it can be heavily influenced by these outliers. It is also not appropriate for data sets with a small sample size, as it may not accurately represent the population. In these cases, other measures of central tendency such as the median may be more appropriate.

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