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What is the benefit of transforming standard normal distributions to conform to the standard distribution?
There are no benefits in doing something that cannot be done. The standard normal distribution is not transformed to the standard distribution because the latter does not exist.
What requirements are necessary for a normal probability distribution to be a standard normal probability?
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.
What is the standard deviation of a standard normal distribution?
The standard deviation in a standard normal distribution is 1.
In the standard normal distribution the standard deviation is always what?
The standard deviation in a standard normal distribution is 1.
Why do you derive a standard normal distribution for data you assume to follow a normal distribution?
The normal distribution is very difficult to work with: the probability density function is not simple [I suspect that only a few mathematics graduates from university would be able to integrate it!]. The standard normal has been tabulated in considerable detail and so, if the normal is transformed to the standard normal, most of the analyses, hypothesis testing, confidence intervals and so on can be found by looking up tables.
What is the benefit of transforming standard normal distributions to conform to the standard distribution?
There are no benefits in doing something that cannot be done. The standard normal distribution is not transformed to the standard distribution because the latter does not exist.
What requirements are necessary for a normal probability distribution to be a standard normal probability?
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.
How the normal distribution could be transformed to a standard normal distribution?
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
How does standard normal distribution differ from normal distribution?
The standard normal distribution has a mean of 0 and a standard deviation of 1.
What is the difference between a normal distribution and the standard normal distribution?
The standard normal distribution is a normal distribution with mean 0 and variance 1.
Which normal distribution is also the standard normal curve?
The normal distribution would be a standard normal distribution if it had a mean of 0 and standard deviation of 1.
What is the standard deviation of a standard normal distribution?
The standard deviation in a standard normal distribution is 1.
In the standard normal distribution the standard deviation is always what?
The standard deviation in a standard normal distribution is 1.
What is the difference of a normal distribution and a stardard normal distribution?
The standard normal distribution is a special case of the normal distribution. The standard normal has mean 0 and variance 1.
Carefully define a standard normal distribution?
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.
In your own words describe the standard normal distribution?
The standard normal distribution is a special case normal distribution, which has a mean of zero and a standard deviation of one.
Why do you derive a standard normal distribution for data you assume to follow a normal distribution?
The normal distribution is very difficult to work with: the probability density function is not simple [I suspect that only a few mathematics graduates from university would be able to integrate it!]. The standard normal has been tabulated in considerable detail and so, if the normal is transformed to the standard normal, most of the analyses, hypothesis testing, confidence intervals and so on can be found by looking up tables.
