Z statistic: Difference between revisions
imported>Doug Williamson (Categorise page.) |
imported>Doug Williamson (Link with Mean and Standard deviation pages.) |
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A commonly used transformation of a standard normal distribution. | A commonly used transformation of a standard normal distribution. | ||
The resulting distribution has a mean of 0 and a standard deviation of 1. Used extensively in hypothesis testing. | The resulting distribution has a [[mean]] of 0 and a [[standard deviation]] of 1. Used extensively in hypothesis testing. | ||
Also known as the Z score. | Also known as the Z score. | ||
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* [[Standardised normal distribution]] | * [[Standardised normal distribution]] | ||
[[Category: | [[Category:Risk_frameworks]] | ||
Revision as of 09:25, 9 November 2013
A commonly used transformation of a standard normal distribution. The resulting distribution has a mean of 0 and a standard deviation of 1. Used extensively in hypothesis testing.
Also known as the Z score.
So for example if a data point has a Z score (or Z statistic) of -1.64, then it lies 1.64 standard deviations below the mean.
The Z score is calculated as the difference between the data point (X) and the mean E[x], all divided by the standard deviation of the population (SD). For example if:
the mean (E[x]) of a population = 100;
the standard deviation (SD) = 10; and
a given observation (or data point) = 83.6;
then the Z score (Z) is calculated as: Z = (X - E[x])/SD
= (83.6 - 100 = -16.4)/10
= - 1.64 standard deviations.
In this case the Z score is negative, indicating that the data point (83.6) lies below the mean (of 100).
