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Fligner-Killeen's test is distribution free when populations are identical " }, { "__type": "FootnoteReference", "__tag": 4066, "label": "2" }, { "__type": "Text", "__tag": 4046, "value": "." } ] } ], "title": [], "level": 0, "target": null }, "Other Parameters": { "__type": "Section", "__tag": 4015, "children": [], "title": [], "level": 0, "target": null } }, "_ordered_sections": [ "Summary", "Extended Summary", "Parameters", "Attributes", "Methods", "Returns", "Yields", "Receives", "Other Parameters", "Raises", "Warns", "Warnings", "Notes" ], "item_file": "/scipy/stats/_morestats.py", "item_line": 3336, "item_type": "function", "aliases": [ "scipy.stats.fligner" ], "example_section_data": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Code", "__tag": 4050, "value": "import numpy as np\nfrom scipy import stats\n", "execution_status": "success" }, { "__type": "Text", "__tag": 4046, "value": "\nTest whether the lists `a`, `b` and `c` come from populations\nwith equal variances.\n\n" }, { "__type": "Code", "__tag": 4050, "value": "a = [8.88, 9.12, 9.04, 8.98, 9.00, 9.08, 9.01, 8.85, 9.06, 8.99]\nb = [8.88, 8.95, 9.29, 9.44, 9.15, 9.58, 8.36, 9.18, 8.67, 9.05]\nc = [8.95, 9.12, 8.95, 8.85, 9.03, 8.84, 9.07, 8.98, 8.86, 8.98]\nstat, p = stats.fligner(a, b, c)\n", "execution_status": "success" }, { "__type": "Code", "__tag": 4050, "value": "p\n", "execution_status": "failure" }, { "__type": "Text", "__tag": 4046, "value": "\nThe small p-value suggests that the populations do not have equal\nvariances.\n\nThis is not surprising, given that the sample variance of `b` is much\nlarger than that of `a` and `c`:\n\n" }, { "__type": "Code", "__tag": 4050, "value": "[np.var(x, ddof=1) for x in [a, b, c]]\n", "execution_status": "failure" }, { "__type": "Text", "__tag": 4046, "value": "\nFor a more detailed example, see :ref:`hypothesis_fligner`." } ], "title": [], "level": 0, "target": null }, "see_also": [ { "__type": "SeeAlsoItem", "__tag": 4028, "name": { "__type": "CrossRef", "__tag": 4002, "value": "bartlett", "reference": { "__type": "RefInfo", "__tag": 4000, "module": "current-module", "version": "current-version", "kind": "to-resolve", "path": "bartlett" }, "kind": "module" }, "descriptions": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "A parametric test for equality of k variances in normal samples" } ] } ], "type": "func" }, { "__type": "SeeAlsoItem", "__tag": 4028, "name": { "__type": "CrossRef", "__tag": 4002, "value": "hypothesis_fligner", "reference": { "__type": "RefInfo", "__tag": 4000, "module": "current-module", "version": "current-version", "kind": "to-resolve", "path": "hypothesis_fligner" }, "kind": "module" }, "descriptions": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Extended example" } ] } ], "type": "ref" }, { "__type": "SeeAlsoItem", "__tag": 4028, "name": { "__type": "CrossRef", "__tag": 4002, "value": "levene", "reference": { "__type": "LocalRef", "__tag": 4022, "kind": "module", "path": "scipy.stats._morestats:levene" }, "kind": "module" }, "descriptions": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "A robust parametric test for equality of k variances" } ] } ], "type": "func" } ], "signature": { "__type": "SignatureNode", "__tag": 4029, "kind": "function", "parameters": [ { "__type": "SigParam", "__tag": 4030, "name": "samples", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "VAR_POSITIONAL", "default": { "__type": "Empty", "__tag": 4031 } }, { "__type": "SigParam", "__tag": 4030, "name": "center", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "KEYWORD_ONLY", "default": "median" }, { "__type": "SigParam", "__tag": 4030, "name": "proportiontocut", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "KEYWORD_ONLY", "default": "0.05" }, { "__type": "SigParam", "__tag": 4030, "name": "axis", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "KEYWORD_ONLY", "default": "0" }, { "__type": "SigParam", "__tag": 4030, "name": "nan_policy", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "KEYWORD_ONLY", "default": "propagate" }, { "__type": "SigParam", "__tag": 4030, "name": "keepdims", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "KEYWORD_ONLY", "default": "False" } ], "return_annotation": { "__type": "Empty", "__tag": 4031 }, "target_name": "fligner" }, "references": [ ".. [1] Qu, A., Lindsay, B. G., and Li, B. (2000). Improving generalized", " estimating equations using quadratic inference functions.", " Biometrika, 87(4), 823-836.", " :doi:`10.1093/biomet/87.4.823`", ".. [2] Fligner, M.A. and Killeen, T.J. (1976). Distribution-free two-sample", " tests for scale. Journal of the American Statistical Association.", " 71(353), 210-213.", ".. [3] Conover, W. J., Johnson, M. E. and Johnson M. M. (1981). A", " comparative study of tests for homogeneity of variances, with", " applications to the outer continental shelf bidding data.", " Technometrics, 23(4), 351-361." ], "qa": "scipy.stats._morestats:fligner", "arbitrary": [], "local_refs": [ "...", "axis", "center", "keepdims", "nan_policy", "proportiontocut", "pvalue", "sample1", "sample2", "statistic" ] }