The normal distribution would be a standard normal distribution if it had a mean of 0 and standard deviation of 1.
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the standard normal curve 2
You may transform a normal distribution curve, with, f(x), distributed normally, with mean mu, and standard deviation s, into a standard normal distribution f(z), with mu=0 and s=1, using this transform: z = (x- mu)/s
A bell shaped probability distribution curve is NOT necessarily a normal distribution.
A standard normal distribution has a mean of zero and a standard deviation of 1. A normal distribution can have any real number as a mean and the standard deviation must be greater than zero.
No, the normal curve is not the meaning of the Normal distribution: it is one way of representing it.
The normal distribution would be a standard normal distribution if it had a mean of 0 and standard deviation of 1.
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the standard normal curve 2
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The area within the normal curve between -1 standard deviation (SD) and +1 SD is approximately 68%. This means that about 68% of the data falls within one standard deviation of the mean in a normal distribution.
The mean must be 0 and the standard deviation must be 1. Use the formula: z = (x - mu)/sigma
You may transform a normal distribution curve, with, f(x), distributed normally, with mean mu, and standard deviation s, into a standard normal distribution f(z), with mu=0 and s=1, using this transform: z = (x- mu)/s
A bell shaped probability distribution curve is NOT necessarily a normal distribution.
If the Z-Score corresponds to the standard deviation, then the distribution is "normal", or Gaussian.