dist_plot

normtest.looney_gulledge.dist_plot(axes, test=None, alphas=[0.1, 0.05, 0.01])

This function generates axis with critical data from the Looney-Gulledge Normality test [1].

Parameters:

axesmatplotlib.axes.SubplotBase

The axis of the graph;

testtuple (optional), with two elements:

statisticfloat (positive)

The test statistic;

sample_sizeint

The sample size. Must be equal or greater than 4;

alphaslist of floats, optional

The significance level (\(\alpha\)) to draw the critical lines. Default is [0.10, 0.05, 0.01]. It can be a combination of:

• 0.005;

• 0.01;

• 0.025;

• 0.05;

• 0.10;

• 0.25;

• 0.50;

• 0.75;

• 0.90;

• 0.95;

• 0.975;

• 0.99;

• 0.995;

Returns:

axesmatplotlib.axes.SubplotBase

The axis of the graph;

References

[1]

LOONEY, S. W.; GULLEDGE, T. R. Use of the Correlation Coefficient with Normal Probability Plots. The American Statistician, v. 39, n. 1, p. 75-79, fev. 1985.

Examples

>>> from normtest import looney_gulledge
>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots(figsize=(6, 4))
>>> looney_gulledge.dist_plot(axes=ax, test=(0.98538, 7))
>>> # plt.savefig("dist_plot.png")
>>> plt.show()