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T.Engineer
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Can anybody explain to me how to get the mean and the Variance for a specific function.
Thanks alot.
Thanks alot.
T.Engineer said:Can anybody explain to me how to get the mean and the Variance for a specific function.
Thanks alot.
EnumaElish said:For a specific function h of a random variable x with p.d.f. f(x),
Mean = E[h(x)] = ∫h(x)f(x) dx
Variance = E[(h(x) - Mean)^2] = ∫(h(x) - Mean)^2 f(x) dx
both integrated over the domain of f(x).
m = Σi h(xi)/N
s^2 = Σi (h(xi) - m)^2/(N-1)
EnumaElish said:For me to answer this, you should tell me what is random. (You need a random variable for this to work.) Are signal times (t) random? Is the time between two signals random? What is your random variable?
chroot said:T.Engineer,
Please post the complete problem, exactly as it was given to you. You seem to be leaving out a lot of important information.
- Warren
EnumaElish said:You can simulate this for a given n (random t).
You can simulate it for a given t and random n.
You can also simulate it with random t and random n.
You can collect the data and calculate the mean and the variance.
EnumaElish said:You can also simulate it with random t and random n.
You can collect the data and calculate the mean and the variance.
yi is the i'th individual data point (function value). (y1 = first data point, y2 = second, ...)T.Engineer said:Thanks alot!
but I don't know how to start?
should I use the method which represented by
http://w3eos.whoi.edu/12.747/notes/lect06/l06s02.html
and if yes, how to enter my function to this simulation?
for example in the first equation , what did he mean by
yi, y
EnumaElish said:Are they the same t? Do you mean to say Hn(#) = (-1)^n cos(2π fc #)* e^[(#^2)/4] *d^n/d#^n *e^[(#^2)/4] for some generic (general) argument # where # = t - jTf - cj Tc - r d^kj ?
If this is not it, what is it?
EnumaElish said:I will advise that you start simple and also assume n has a uniform frequency distribution.
EnumaElish said:you mean to say Hn(#) = (-1)^n cos(2π fc #)* e^[(#^2)/4] *d^n/d#^n *e^[(#^2)/4] for some generic (general) argument # where # = t - jTf - cj Tc - r d^kj
Once you attach each of t and n to a frequency distribution, you can easily simulate your function to calculate the AC coefficient. You can also determine it analytically, by applying the formulas under this thread and under this other thread.
The formula for calculating the mean of a function is to add up all the values of the function and then divide by the total number of values. This can be represented as:
Mean = (sum of all values) / (total number of values)
To find the variance of a function, you first need to calculate the mean of the function. Then, for each value in the function, subtract the mean and square the result. Finally, add up all the squared differences and divide by the total number of values. The formula for variance is:
Variance = (sum of squared differences) / (number of values)
The mean and variance of a function are important statistical measures that help us understand the central tendency and variability of a set of data. They provide valuable information about the distribution of the data and can be used to make predictions and comparisons.
Many programming languages have built-in functions or libraries that can calculate mean and variance for a given set of data. For example, in Python, you can use the mean()
and var()
functions from the statistics
module. In R, you can use the mean()
and var()
functions from the base
package.
Yes, mean and variance can be calculated for any type of function as long as there is a set of data associated with it. However, it is important to note that the results may not be meaningful for all types of functions. For example, calculating the mean and variance of a quadratic function may not provide useful information as the function is not a representation of a set of data.