Q: Is the expected value of a random variable more cumbersome to work with then an objective probability distribution?

Write your answer...

Submit

Still have questions?

Continue Learning about Math & Arithmetic

Expected value is the outcome of confidence of how probability distribution is characterized. If the expected value is greater than the confidence interval then the results are significant.

Yes it can be negative.

It is a probability distribution in which the probability of the random variable being in any interval on one side of the mean (expected value) is the same as for the equivalent interval on the other side of the mean.

Yes, it is true.

The answer depends on the level at which the student is expected to be. A 15-year old should know the probability of getting heads on the toss of a coin but even a mathematics graduate - who did not specialise in probability - would be expected to be able to prove the mathematical relationship between the Normal distribution and the F-distribution. If asked, most student would not even know what the second part of the sentence meant.

Related questions

The expected value is the average of a probability distribution. It is the value that can be expected to occur on the average, in the long run.

Expected value is the outcome of confidence of how probability distribution is characterized. If the expected value is greater than the confidence interval then the results are significant.

The mean of a discrete probability distribution is also called the Expected Value.

Consider a binomial distribution with 10 trials What is the expected value of this distribution if the probability of success on a single trial is 0.5?

This is the answer.

Yes it can be negative.

KLD stands for Kullback-Leibler Divergence. It is a measure of how one probability distribution diverges from a second, expected probability distribution. KLD is a unitless quantity.

It is a probability distribution in which the probability of the random variable being in any interval on one side of the mean (expected value) is the same as for the equivalent interval on the other side of the mean.

The third moment. That is, the expected value of the cubes of the deviations from the mean.

Yes, it is true.

A test using relative errors comparing a frequency table to the expected counts determined using a given probability distribution; the null hypothesis is that the given probability distribution fits the data's distribution.

The answer depends on the level at which the student is expected to be. A 15-year old should know the probability of getting heads on the toss of a coin but even a mathematics graduate - who did not specialise in probability - would be expected to be able to prove the mathematical relationship between the Normal distribution and the F-distribution. If asked, most student would not even know what the second part of the sentence meant.