Examples of using Expected value in English and their translations into Turkish
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Standard, in this case, means the expected value is 0 and the variance is 1.
But the solar neutrino flux was a fraction of its theoretically expected value.
The mean, the expected value of this distribution, is p. And p might be here or something.
This is the probability mass function of the Poisson distribution with expected value λ.
The expected value of this lottery is two dollars; this is a lottery in which you should invest your money.
The Cauchy distribution,an example of a distribution which does not have an expected value or a variance.
But just going back, the expected value is a a probability weighted sum of each of these.
And in this video, I'm going to introduce you to the concept of the expected value of a random variable.
The expected value and variance of a Poisson-distributed random variable are both equal to λ.
Let X be a random variable with finite expected value μ and finite non-zero variance σ2.
The expected value of this random variable is n times p, or sometimes people will write p times n.
But we will see in the future that the expected value doesn't have to be the most probable value. .
The expected value of this lottery is two dollars; this is a lottery in which you should invest your money.
In this video we will find a general formula for the mean,or actually, for the expected value of a binomial distribution.
So the expected value of X, the expected value of our random variable that's being described as binomial distribution-- it's equal to the sum.
So, the full bit is a low standard deviation indicates the value must be close to orthe same as… the mean or expected value.
And then, we actually calculated the expected value for the particular binomial distributions that we studied, especially the one with the flipping of the coin.
Suppose X1,…, Xn areindependent and identically distributed, and are normally distributed with unknown expected value μ and known variance 1.
In the last video we learned alittle bit about what the expected value of random variable is, and we saw that it was really just the population mean-- the same thing.
So if you have that information you can then actually figure out the population mean for thispopulation that's described by this probability distribution, or the expected value.
When the expected value of the Poisson distribution is 1, then Dobinski's formula says that the"n"th moment equals the number of partitions of a set of size"n.
If you had a 50% chance of making five times your money,and only a 50% chance of losing your money, the expected value here is, it would be 0.5 times 25 plus 0.5 times zero.
The expected value of any of our actions-- that is, the goodness that we can count on getting-- is the product of two simple things: the odds that this action will allow us to gain something, and the value of that gain to us.
So I know I said--and you really shouldn't necessarily strictly view expected value as the number of shots you should expect to make because sometimes probability distributions can be kind of weird.
The polynomials"He""n" are sometimes denoted by"H""n", especially in probability theory, because:formula_4is the probability density function for the normal distribution with expected value 0 and standard deviation 1.
When U{\displaystyle U} and V{\displaystyle V} are two independent normally distributed random variables with expected value 0 and variance 1, then the ratio U/ V{\displaystyle U/V} has the standard Cauchy distribution.
If the entries in the column vector: formula_1are random variables, each with finite variance, then the covariance matrix Σ is the matrix whose("i","j") entry is the covariance: formula_2where:formula_3is the expected value of the"i"th entry in the vector X.
But what's useful now is we can apply the same principles,but we're finding the arithmetic mean of an infinite population, or the expected value of a random variable, which is the same thing as the arithmetic mean of the population of this random variable.
But the purpose of this video is to really show you that the expected value calculation is the same thing as the population mean calculation, but we do it this way because you can't add up an infinite number of data points and divide by an infinite number.
And I really want to hit that point home because sometimes in probability books they will just give you a formula--oh, the expected value of a probability distribution is each of the outcomes times their frequency.