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YBut if you're doing calculus, an alternate proof would be that f'(x) > 0 for all values of x.
I understand that the gradient function must always be positive... but how do you prove that?
Have a cubic with no stationary points which is obviously increasing for all values of x, can't think how to prove it ><
I'm sure the proof is very obvious
I can do basic differentiation etc.
Thanks
Draw a picture please.
Use ms paint, save as jpg
Go to www.imageshack.us , upload it, and post the link
assuming you have xp, paint can save as jpg, otherwise you'll need a program like image forge.
No no no, all my fault, I made a typo!!
Start again, there are two methods we are talking:
1. The method for finding the standard deviation is the 'root mean squared' (or rms).
2. What I think is more logical is the 'mean distance from the mean' or 'mean root square'.
NB only the order of root & mean have changed between the two methods.
For example, if we have the values 100, 400, 600, 900
The mean is 500,
1. The SD is 336.65
2. The 'mean distance from the mean' is 250
Why is the first figure, the RMS, more useful, or why is it the preferred, used method - and not the (more logical imo) MRS 'mean distance from the mean'.
Exactly, mathsyperson, what advantage does the standard deviation (root mean square), have over the more logical 'mean distance from the mean'?
A statement like 'the standard deviation is slightly different to the mean distance from the mean in that it does xxx' is what I'm after
I guess I'll sit down and figure out *how* it is different, post that, then pose the question *why* we do it the 'root mean square' (rms) way rather than the (more logcial imo) 'mean distance from the mean' way.
Pardon, change my question to "why is it the 'root mean square' and not the 'mean root square' " - the second one appears more logical to me as it's simply the 'mean distance from the mean - ignoring negative values' the first one obviously comes up with a similar result... but why do we use the first one instead of the second one? Do you understand where I'm coming from?
Cheers
Why is it calculated as the 'root mean square', and not the 'mean square root',
The second one seems more logical to me, I'm guessing the first one is used as it does something along the lines of over compensates?
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