You use the z-transformation.
For any variable X, with mean m and standard error s,
Z = (X - m)/s is distributed as N(0, 1).
You use the z-transformation.
For any variable X, with mean m and standard error s,
Z = (X - m)/s is distributed as N(0, 1).
You use the z-transformation.
For any variable X, with mean m and standard error s,
Z = (X - m)/s is distributed as N(0, 1).
You use the z-transformation.
For any variable X, with mean m and standard error s,
Z = (X - m)/s is distributed as N(0, 1).
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You use the z-transformation.
For any variable X, with mean m and standard error s,
Z = (X - m)/s is distributed as N(0, 1).
It's the same as a z-Transformation. for all xi: (xi-mean(x)) / std(x)
The normal distribution would be a standard normal distribution if it had a mean of 0 and standard deviation of 1.
The standard normal curve is symmetrical.
The standard normal distribution is a normal distribution with mean 0 and variance 1.
A mathematical definition of a standard normal distribution is given in the related link. A standard normal distribution is a normal distribution with a mean of 0 and a variance of 1.