Use a *normal probability plot* to
assess graphically whether sample data has a normal distribution and
the type of departure from normality, if any.

A normal probability plot plots the empirical cumulative distribution
of the sample data versus the theoretical cumulative distribution
function of a normal distribution. The horizontal axis plots the sorted
sample data. The vertical axis plots the normal order statistic medians,
calculated using the uniform order statistic medians and the inverse
cumulative distribution function (icdf) of the normal distribution.
If the sample data has a normal distribution, then the plot appears
linear. Distributions other than normal introduce curvature in the
plot.

`normplot`

superimposes a fit line onto the
plot using a robust linear fit of the sample order statistics for
the data in second and third quartile of the sample data. `normplot`

then
extrapolates linearly to the minimum and maximum values in the sample
to help visually assess the data in the tails.

`normplot`

uses midpoint probability plotting
positions to plot along the *y*-axis. The *i*th
sorted value from a sample of size *N* is plotted
against the midpoint between tick marks of the empirical cdf on the *y*-axis.
If the sample data is uncensored, the midpoint is equal to $$\frac{\left(i-0.5\right)}{N}$$.
If your sample data includes censored observations, use `probplot`

instead of `normplot`

to
create a normal probability plot.