{ "__type": "IngestedDoc", "__tag": 4010, "_content": { "Notes": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Even if " }, { "__type": "ParamRef", "__tag": 4071, "name": "plot" }, { "__type": "Text", "__tag": 4046, "value": " is given, the figure is not shown or saved by " }, { "__type": "CrossRef", "__tag": 4002, "value": "probplot", "reference": { "__type": "LocalRef", "__tag": 4022, "kind": "module", "path": "scipy.stats._morestats:probplot" }, "kind": "module" }, { "__type": "Text", "__tag": 4046, "value": "; " }, { "__type": "InlineCode", "__tag": 4051, "value": "plt.show()" }, { "__type": "Text", "__tag": 4046, "value": " or " }, { "__type": "InlineCode", "__tag": 4051, "value": "plt.savefig('figname.png')" }, { "__type": "Text", "__tag": 4046, "value": " should be used after calling " }, { "__type": "CrossRef", "__tag": 4002, "value": "probplot", "reference": { "__type": "LocalRef", "__tag": 4022, "kind": "module", "path": "scipy.stats._morestats:probplot" }, "kind": "module" }, { "__type": "Text", "__tag": 4046, "value": "." } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "CrossRef", "__tag": 4002, "value": "probplot", "reference": { "__type": "LocalRef", "__tag": 4022, "kind": "module", "path": "scipy.stats._morestats:probplot" }, "kind": "module" }, { "__type": "Text", "__tag": 4046, "value": " generates a probability plot, which should not be confused with a Q-Q or a P-P plot. Statsmodels has more extensive functionality of this type, see " }, { "__type": "InlineCode", "__tag": 4051, "value": "statsmodels.api.ProbPlot" }, { "__type": "Text", "__tag": 4046, "value": "." } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "The formula used for the theoretical quantiles (horizontal axis of the probability plot) is Filliben's estimate " } ] }, { "__type": "Code", "__tag": 4050, "value": "quantiles = dist.ppf(val), for\n\n 0.5**(1/n), for i = n\n val = (i - 0.3175) / (n + 0.365), for i = 2, ..., n-1\n 1 - 0.5**(1/n), for i = 1", "execution_status": null }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "where " }, { "__type": "InlineCode", "__tag": 4051, "value": "i" }, { "__type": "Text", "__tag": 4046, "value": " indicates the i-th ordered value and " }, { "__type": "InlineCode", "__tag": 4051, "value": "n" }, { "__type": "Text", "__tag": 4046, "value": " is the total number of values." } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Strong", "__tag": 4048, "children": [ { "__type": "Text", "__tag": 4046, "value": "Array API Standard Support" } ] } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "CrossRef", "__tag": 4002, "value": "probplot", "reference": { "__type": "LocalRef", "__tag": 4022, "kind": "module", "path": "scipy.stats._morestats:probplot" }, "kind": "module" }, { "__type": "Text", "__tag": 4046, "value": " has experimental support for Python Array API Standard compatible backends in addition to NumPy. 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