# statsmodels.graphics.gofplots.qqplot¶

statsmodels.graphics.gofplots.qqplot(data, dist=<scipy.stats._continuous_distns.norm_gen object>, distargs=(), a=0, loc=0, scale=1, fit=False, line=None, ax=None, **plotkwargs)[source]

Q-Q plot of the quantiles of x versus the quantiles/ppf of a distribution.

Can take arguments specifying the parameters for dist or fit them automatically. (See fit under Parameters.)

Parameters: data (array-like) – 1d data array dist (A scipy.stats or statsmodels distribution) – Compare x against dist. The default is scipy.stats.distributions.norm (a standard normal). distargs (tuple) – A tuple of arguments passed to dist to specify it fully so dist.ppf may be called. loc (float) – Location parameter for dist a (float) – Offset for the plotting position of an expected order statistic, for example. The plotting positions are given by (i - a)/(nobs - 2*a + 1) for i in range(0,nobs+1) scale (float) – Scale parameter for dist fit (boolean) – If fit is false, loc, scale, and distargs are passed to the distribution. If fit is True then the parameters for dist are fit automatically using dist.fit. The quantiles are formed from the standardized data, after subtracting the fitted loc and dividing by the fitted scale. line (str {'45', 's', 'r', q'} or None) – Options for the reference line to which the data is compared: ‘45’ - 45-degree line ’s‘ - standardized line, the expected order statistics are scaled by the standard deviation of the given sample and have the mean added to them ’r’ - A regression line is fit ’q’ - A line is fit through the quartiles. None - by default no reference line is added to the plot. ax (Matplotlib AxesSubplot instance, optional) – If given, this subplot is used to plot in instead of a new figure being created. **plotkwargs (additional matplotlib arguments to be passed to the) – plot command. fig – If ax is None, the created figure. Otherwise the figure to which ax is connected. Matplotlib figure instance

scipy.stats.probplot

Examples

>>> import statsmodels.api as sm
>>> from matplotlib import pyplot as plt
>>> mod_fit = sm.OLS(data.endog, data.exog).fit()
>>> res = mod_fit.resid # residuals
>>> fig = sm.qqplot(res)
>>> plt.show()


qqplot of the residuals against quantiles of t-distribution with 4 degrees of freedom:

>>> import scipy.stats as stats
>>> fig = sm.qqplot(res, stats.t, distargs=(4,))
>>> plt.show()


qqplot against same as above, but with mean 3 and std 10:

>>> fig = sm.qqplot(res, stats.t, distargs=(4,), loc=3, scale=10)
>>> plt.show()


Automatically determine parameters for t distribution including the loc and scale:

>>> fig = sm.qqplot(res, stats.t, fit=True, line='45')
>>> plt.show()


The following plot displays some options, follow the link to see the code.

Notes

Depends on matplotlib. If fit is True then the parameters are fit using the distribution’s fit() method.