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Normal probability plot

`normplot(x)`

`normplot(ax,x)`

`h = normplot(___)`

`normplot(`

creates a normal probability plot
comparing the distribution of the data in `x`

)`x`

to the normal
distribution.

`normplot`

plots each data point in `x`

using plus sign (`'+'`

) markers and draws two reference lines that
represent the theoretical distribution. A solid reference line connects the first
and third quartiles of the data, and a dashed reference line extends the solid line
to the ends of the data. If the sample data has a normal distribution, then the data
points appear along the reference line. A distribution other than normal introduces
curvature in the data plot.

`normplot`

matches the quantiles of sample data to the quantiles of
a normal distribution. The sample data is sorted and plotted on the x-axis. The y-axis
represents the quantiles of the normal distribution, converted into probability values.
Therefore, the y-axis scaling is not linear.

Where the x-axis value is the *i*th sorted value from a sample of
size *N*, the y-axis value is the midpoint between evaluation points of
the empirical cumulative distribution function of the data. The midpoint is equal to $$\frac{\left(i-0.5\right)}{N}$$.

`normplot`

superimposes a reference line to assess the linearity of
the plot. The line goes through the first and third quartiles of the data.

You can use the `probplot`

function to create a probability
plot. The `probplot`

function enables you to indicate censored data
and specify the distribution for a probability plot.