This browser is not much use when it comes to mathematics but I'll try.
Suppose X is a random variable with a Normal distribution and let f(x) be the probability density function of x.
Then the mean is mu = E(X) = Integral of x*f(x) dx over the domain of X [which is negative infinity to positive infinity].
The variance is E{[X - E(X)]2} = Integral of (x - mu)2*f(x) dx over the domain of X.
Chat with our AI personalities
The normal distribution can have any real number as mean and any positive number as variance. The mean of the standard normal distribution is 0 and its variance is 1.
The standard normal distribution is a special case of the normal distribution. The standard normal has mean 0 and variance 1.
Z is a variable with mean 0 and variance 1.Z is a variable with mean 0 and variance 1.Z is a variable with mean 0 and variance 1.Z is a variable with mean 0 and variance 1.
It is a discrete distribution in which the men and variance have the same value.
The Normal distribution is a probability distribution of the exponential family. It is a symmetric distribution which is defined by just two parameters: its mean and variance (or standard deviation. It is one of the most commonly occurring distributions for continuous variables. Also, under suitable conditions, other distributions can be approximated by the Normal. Unfortunately, these approximations are often used even if the required conditions are not met!