To find the Z score from the random variable you need the mean and variance of the rv.To find the Z score from the random variable you need the mean and variance of the rv.To find the Z score from the random variable you need the mean and variance of the rv.To find the Z score from the random variable you need the mean and variance of the rv.
Variance" is a mesaure of the dispersion of the probability distribution of a random variable. Consider two random variables with the same mean (same aver-age value). If one of them has a distribution with greater variance, then, roughly speaking, the probability that the variable will take on a value far from the mean is greater.
The mean is 0.9592
30 percent.
This is supposed to be Y > u
To find the Z score from the random variable you need the mean and variance of the rv.To find the Z score from the random variable you need the mean and variance of the rv.To find the Z score from the random variable you need the mean and variance of the rv.To find the Z score from the random variable you need the mean and variance of the rv.
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It depends on the parameter - the mean of the distribution.
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.
Variance" is a mesaure of the dispersion of the probability distribution of a random variable. Consider two random variables with the same mean (same aver-age value). If one of them has a distribution with greater variance, then, roughly speaking, the probability that the variable will take on a value far from the mean is greater.
I have included two links. A normal random variable is a random variable whose associated probability distribution is the normal probability distribution. By definition, a random variable has to have an associated distribution. The normal distribution (probability density function) is defined by a mathematical formula with a mean and standard deviation as parameters. The normal distribution is ofter called a bell-shaped curve, because of its symmetrical shape. It is not the only symmetrical distribution. The two links should provide more information beyond this simple definition.
The mean is 0.9592
Assuming you mean random variable here. A random variable is term that can take have different values. for example a random variable x that represent the out come of rolling a dice, that is x can equal 1,2,3,4,5,or 6. Think of probability distribution as the mapping of likelihood of the out comes from an experiment. In the dice case, the probability distribution will tell you that there 1/6 the time you will get 1, 2,3....,or 6. this is called uniform distribution since all the out comes have that same probability of occurring.
No. The mean is the expected value of the random variable but you can also have expected values of functions of the random variable. If you define X as the random variable representing the result of a single throw of a fair die, the expected value of X is 3.5, the mean of the probability distribution of X. However, you play a game where you pay someone a certain amount of money for each throw of the die and the other person pays you your "winnings" which depend on the outcome of the throw. The variable, "your winnings", will also have an expected value. As will your opponent's winnings.
In all likelihood, it stands for Probability. For example, Pr(X < 3) is a way of writing "Probability that a random variable, X, takes a value less than 3".
30 percent.
This is supposed to be Y > u