Although no data set is exactly normally distributed, most statistical analyses require that the data be approximately normally distributed for their findings to be valid. One way of testing for normality is through a quantile-quantile (q-q) plot, a technique for determining if data sets originate from populations with a common distribution.
In this tutorial, you will determine if a data set is normally distributed by comparing its quantiles against those of a theoretical normal distribution. You will also learn how to make a data set nearly normally distributed.
10-18:2016 Update: As of 10-18-2016 we are providing a link to an Excel .xlsx file that reproduces Table 1 of the tutorial.
09-20-2016 Update: References update: active links were added to two references in the Reference section.
05-25-2016 updated link:
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