It is the Standard Normal distribution.
For a normal probability distribution to be considered a standard normal probability distribution, it must have a mean of 0 and a standard deviation of 1. This standardization allows for the use of z-scores, which represent the number of standard deviations a data point is from the mean. Any normal distribution can be transformed into a standard normal distribution through the process of standardization.
The normal distribution and the t-distribution are both symmetric bell-shaped continuous probability distribution functions. The t-distribution has heavier tails: the probability of observations further from the mean is greater than for the normal distribution. There are other differences in terms of when it is appropriate to use them. Finally, the standard normal distribution is a special case of a normal distribution such that the mean is 0 and the standard deviation is 1.
A Bernoulli distribution is a discrete probability distribution which takes value 1 with success probability p and value 0 with failure probability q = 1 - p.
The exponential distribution is a continuous probability distribution with probability density definded by: f(x) = ke-kx for x ≥ 0 and f(x) = 0 otherwise.
The answer depends on the probability distribution function for the random variable.
For a normal probability distribution to be considered a standard normal probability distribution, it must have a mean of 0 and a standard deviation of 1. This standardization allows for the use of z-scores, which represent the number of standard deviations a data point is from the mean. Any normal distribution can be transformed into a standard normal distribution through the process of standardization.
The probability density of the standardized normal distribution is described in the related link. It is the same as a normal distribution, but substituted into the equation is mean = 0 and sigma = 1 which simplifies the formula.
The mean must be 0 and the variance must be 1.
No. The Normal distribution is symmetric: skewness = 0.
The normal distribution and the t-distribution are both symmetric bell-shaped continuous probability distribution functions. The t-distribution has heavier tails: the probability of observations further from the mean is greater than for the normal distribution. There are other differences in terms of when it is appropriate to use them. Finally, the standard normal distribution is a special case of a normal distribution such that the mean is 0 and the standard deviation is 1.
With a single throw of a normal die, the probability is 0.With a single throw of a normal die, the probability is 0.With a single throw of a normal die, the probability is 0.With a single throw of a normal die, the probability is 0.
probability is 43.3%
0.4846
2.16
A Bernoulli distribution is a discrete probability distribution which takes value 1 with success probability p and value 0 with failure probability q = 1 - p.
The exponential distribution is a continuous probability distribution with probability density definded by: f(x) = ke-kx for x ≥ 0 and f(x) = 0 otherwise.
The answer depends on the probability distribution function for the random variable.