Examples of using Random variable in English and their translations into Turkish
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Colloquial
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Ecclesiastic
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Ecclesiastic
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Programming
So this is my definition of my random variable.
And the random variable, X, is the number of shots I make.
What is the probability that my random variable is equal to 2?
We defined our random variable, x, as the number of shots I make out of 6.
All right,so what are the different values that we care about for our random variable?
And I'm going to define my random variable, X, I will define it.
Because you can just keep on performing the experiment that generates the random variable.
So the expected value of our random variable is equal to the sum.
This random variable, number of heads after 5 flips-- you can't have an infinite number of values here.
The points wherejumps occur are precisely the values which the random variable may take.
Then the logarithm of random variable Y is normally distributed, hence the name.
The probability of getting 2 heads--probability that our random variable is equal to 2,?
Let's define a random variable, X, like we always do. X is equal to number of shots I make.
And then we figured out the different probabilities that the random variable could take on different values.
As we know, a random variable, it's a little different than a regular variable, it's more of a function.
If the distribution of X is continuous,then X is called a continuous random variable.
The random variable, the number of heads I get in 5 flips of the coin-- it was equal to 5 factorial divided by n factorial.
But if you think about it, every time you do one of your experiments and you get a new value for a random variable.
But now, let's prove it to ourselves that this is really true for any a random variable that's described by a binomial distribution.
We have our random variable, x, is equal to-- I don't know-- it's equal to the number of heads after 6 tosses of a fair coin.
In general, the probability of a set for a given continuous random variable can be calculated by integrating the density over the given set.
In probability theory and statistics, a probability mass function(pmf)is a function that gives the probability that a discrete random variable is exactly equal to some value.
In the last video we defined our random variable x as the number of heads we get after flipping a coin five times, and it's a fair coin.
In mathematics,a degenerate distribution or deterministic distribution is the probability distribution of a random variable which only takes a single value.
So if we say that the random variable, x, is equal to the number of-- we could call it successes. The number of successes with probability p after n trials.
So the probability that X is equal to 1--the probability that our random variable is equal to 1 is equal to 6 times 0.3 times 0.7 to the fifth.
And then the probability that my random variable is equal to k, or in this case, that I make exactly 0 shots is equal to the probability of any of the specific ways of making 0 shots times the number of those ways there are times that.
Geostatistical techniques rely on statisticalmodels that are based on random function(or random variable) theory to model the uncertainty associated with spatial estimation and simulation.
Which we saw in the last video was the exact same thing as adding everything together and dividing by the number of numbers, except that that methodworked with an infinite number of an infinite population what the random variable is.
The probability of any random variable Y can be written as probability of Y given that some other random variable X assumes value i times probability of X equals i, sums over all possible outcomes i for the(inaudible) variable X.