LooneyGulledge

class normtest.looney_gulledge.LooneyGulledge(alpha=0.05, safe=True, weighted=False, **kwargs)

This class instantiates an object to perform the Looney-Gulledge Normality test [1].

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 LooneyGulledge
>>> import numpy as np
>>> x = np.array([6, 1, -4, 8, -2, 5, 0])
>>> test = LooneyGulledge()
>>> test.fit(x)
>>> print(test.normality)
LooneyGulledge(statistic=0.9844829186140105, critical=0.898, p_value=0.8715547240126971, conclusion='Fail to reject H₀')

Attributes:

statisticfloat (positive)

The test statistic;

criticalfloat

The critical value of the test;

p_valuefloat or str

The probability of the test;

conclusionstr

The test conclusion (e.g, Normal/Not Normal).

alphafloat, optional

The level of significance (\(\alpha\)). Default is 0.05;

normalitynamed tuple

A tuple with the main test results summarized

normality_hypothesisstr

Description of the Normality test

safebool, optional

Whether to check the inputs before performing the calculations (True, default) or not (False). Useful for beginners to identify problems in data entry (may reduce algorithm execution time);

Methods

fit(x_data)	Applies the Looney-Gulledge Normality test;
dist_plot(axes, alphas=[0.10, 0.05, 0.01]):	Generates axis with critical data from the Looney-Gulledge Normality test;
correlation_plot(axes)	Generates an axis with the Looney-Gulledge test correlation graph;
line_up(seed=None, correct=False)	Generates a Figure with the correlation graphs for the line up method;
citation(export=False)	Returns the Looney-Gulledge’s test reference;

citation(export=False)

This method returns the reference from Looney-Gulledge’s test, with the option to export the reference in .bib format.

Parameters:

exportbool

Whether to export the reference as LooneyGulledge1985.bib file (True) or not (False, default);

Returns:

referencestr

The Looney-Gulledge test reference;

correlation_plot(axes)

This method generates an axis with the correlation plotfor the Looney-Gulledge Normality test.

Parameters:

axesmatplotlib.axes.SubplotBase

The axis of the graph;

Returns:

axesmatplotlib.axes.SubplotBase

The axis of the graph;

See also

correlation_plot

dist_plot(axes, alphas=[0.1, 0.05, 0.01])

This method generates an axis with critical data from the Looney-Gulledge Normality test.

Parameters:

axesmatplotlib.axes.SubplotBase

The axis of the graph;

alphaslist of floats, optional

The significance level (\(\alpha\)) to draw the critical lines. Default is [0.10, 0.05, 0.01];

Returns:

axesmatplotlib.axes.SubplotBase

The axis of the graph;

See also

dist_plot

fit(x_data)

This method applies the Looney-Gulledge test.

Parameters:

x_datanumpy array

One dimension numpy array with at least 4 observations.

Returns:

x_datanumpy array

One dimension numpy array with at least 4 observations.

statisticfloat (positive)

The test statistic;

criticalfloat

The critical value of the test;

p_valuefloat or str

The probability of the test;

conclusionstr

The test conclusion (e.g, Normal/Not Normal).

normalitynamed tuple

A tuple with the main test results summarized

See also

test

get_alpha()

Returns the current alpha value

Returns:

alphafloat

The level of significance (\(\alpha\)).

get_safe()

Returns the current status of parameter safe

Returns:

safebool, optional

Whether to check the inputs before performing the calculations (True, default) or not (False). Useful for beginners to identify problems in data entry (may reduce algorithm execution time);

line_up(seed=None, correct=False)

This method generates a Figure with the correlation graphs for the line up method.

Parameters:

seedint or None, optional

A numerical value that generates a new set or repeats pseudo-random numbers. Use a positive integer value to be able to repeat results. Default is None what generates a random seed;

correctbool, optional

Whether the x_data is to be drawn in red (False) or black (True, default);

Returns:

figmatplotlib.figure.Figure

A figure with the generated graphics;

See also

line_up

set_alpha(alpha)

Changes the alpha value

Parameters:

alphafloat

The level of significance (\(\alpha\)).

Notes

This method only allows input of type float and between 0.0 and 1.0.

set_safe(safe)

Changes the current status of parameter safe

Parameters:

safebool, optional

Whether to check the inputs before performing the calculations (True, default) or not (False). Useful for beginners to identify problems in data entry (may reduce algorithm execution time);