From theory to practice - page 72
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Uh-huh
You will get 0).
You will get 0).
modulo
As I said, formula or explanation. "uh-huh" is missing.
by module
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From theory to practice
Maxim Dmitriev, 2017.12.13 22:07
Well just the sum of all outliers divided by the number of outliers
that's not the question.
the question is how is the cs better than the s.
For example, at the MNC I know why they take the square, but here I don't understand.
that's not the question.
The question is how cs is better than cs.
For example, in MNC I know why the buret is squared, but here I don't understand.
The point is that there will be equal areas under the mean line and over the mean line, i.e. if you go to the signals, there will be minimal error in terms of energy.
The way we calculate the standard deviation will not change the areas above and below the line.
whether it will be the standard deviation
or the absolute average.
Why pick on it, the formula is there. RMS is indeed much more common, I would say incomparably more common. First of all, because of the simplicity and computational efficiency generated by the least squares method (LSM). Here's a simple example. For now, I'm going to assume that your average is the same as in MNC, arithmetic.
There are many, many lines. The Great Soviet Encyclopaedia electronically. Need to calculate the average fraction of the number of spaces in the line, and any of the indicators of dispersion of this fraction, RMS or your modulo average deviation from this average (briefly I will call it Cheb, then tell you why.) Each pass on all the lines is expensive, the books are on different Internet resources, modem connection via copper pair.So, to calculate RMS one pass will be enough (just copy the number of lines, the sum of space fractions and the sum of squares of the space fractions, from these amounts count immediately RMS), and for Cheb need two (the first copy the number of lines and the sum of fractions, on them consider the average, the second copy the amount of absolute deviations from the average, it counts the deviation Cheb). The difference in labour intensity is 2 times.
And so everywhere you turn, there is a wedge, if something needs to be done by Cheb methods. The problem of approximating a tabularly defined function generates completely different solution costs. The simplest case is to replace the function with a constant. According to MNA, this is the arithmetic mean, which is found by all in one pass over the table of values. The approximation with minimization of absolute deviation is called uniform approximation, or Chebyshev approximation. It is used to find the median average that ensures the minimum of the sum of absolute deviations from any constant. Think about how to calculate the median. MQL has a ready function for it. What it does is that it first arranges all elements in ascending order. This is not the same as finding the arithmetic mean.
And so on. At the same time, you have to be aware that LOC distorts normal ideas about a phenomenon. For example, the average level of w ages. Statistics agencies take advantage of this by reporting average wages. If a company has 25 employees, of which the top 5 earn a million and the other 20 earn 50,000, the arithmetic average wage will be 6/25=240,000 and the median average will be 50,000.