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.CondaPkg/env/lib/python3.11/site-packages/scipy/stats/tests/data/fisher_exact_results_from_r.py
vendored
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607
.CondaPkg/env/lib/python3.11/site-packages/scipy/stats/tests/data/fisher_exact_results_from_r.py
vendored
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# DO NOT EDIT THIS FILE!
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# This file was generated by the R script
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# generate_fisher_exact_results_from_r.R
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# The script was run with R version 3.6.2 (2019-12-12) at 2020-11-09 06:16:09
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from collections import namedtuple
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import numpy as np
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Inf = np.inf
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Parameters = namedtuple('Parameters',
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['table', 'confidence_level', 'alternative'])
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RResults = namedtuple('RResults',
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['pvalue', 'conditional_odds_ratio',
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'conditional_odds_ratio_ci'])
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data = [
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(Parameters(table=[[100, 2], [1000, 5]],
|
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confidence_level=0.95,
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alternative='two.sided'),
|
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RResults(pvalue=0.1300759363430016,
|
||||
conditional_odds_ratio=0.25055839934223,
|
||||
conditional_odds_ratio_ci=(0.04035202926536294,
|
||||
2.662846672960251))),
|
||||
(Parameters(table=[[2, 7], [8, 2]],
|
||||
confidence_level=0.95,
|
||||
alternative='two.sided'),
|
||||
RResults(pvalue=0.02301413756522116,
|
||||
conditional_odds_ratio=0.0858623513573622,
|
||||
conditional_odds_ratio_ci=(0.004668988338943325,
|
||||
0.895792956493601))),
|
||||
(Parameters(table=[[5, 1], [10, 10]],
|
||||
confidence_level=0.95,
|
||||
alternative='two.sided'),
|
||||
RResults(pvalue=0.1973244147157191,
|
||||
conditional_odds_ratio=4.725646047336587,
|
||||
conditional_odds_ratio_ci=(0.4153910882532168,
|
||||
259.2593661129417))),
|
||||
(Parameters(table=[[5, 15], [20, 20]],
|
||||
confidence_level=0.95,
|
||||
alternative='two.sided'),
|
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RResults(pvalue=0.09580440012477633,
|
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conditional_odds_ratio=0.3394396617440851,
|
||||
conditional_odds_ratio_ci=(0.08056337526385809,
|
||||
1.22704788545557))),
|
||||
(Parameters(table=[[5, 16], [16, 25]],
|
||||
confidence_level=0.95,
|
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alternative='two.sided'),
|
||||
RResults(pvalue=0.2697004098849359,
|
||||
conditional_odds_ratio=0.4937791394540491,
|
||||
conditional_odds_ratio_ci=(0.1176691231650079,
|
||||
1.787463657995973))),
|
||||
(Parameters(table=[[10, 5], [10, 1]],
|
||||
confidence_level=0.95,
|
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alternative='two.sided'),
|
||||
RResults(pvalue=0.1973244147157192,
|
||||
conditional_odds_ratio=0.2116112781158479,
|
||||
conditional_odds_ratio_ci=(0.003857141267422399,
|
||||
2.407369893767229))),
|
||||
(Parameters(table=[[10, 5], [10, 0]],
|
||||
confidence_level=0.95,
|
||||
alternative='two.sided'),
|
||||
RResults(pvalue=0.06126482213438735,
|
||||
conditional_odds_ratio=0,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
1.451643573543705))),
|
||||
(Parameters(table=[[5, 0], [1, 4]],
|
||||
confidence_level=0.95,
|
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alternative='two.sided'),
|
||||
RResults(pvalue=0.04761904761904762,
|
||||
conditional_odds_ratio=Inf,
|
||||
conditional_odds_ratio_ci=(1.024822256141754,
|
||||
Inf))),
|
||||
(Parameters(table=[[0, 5], [1, 4]],
|
||||
confidence_level=0.95,
|
||||
alternative='two.sided'),
|
||||
RResults(pvalue=1,
|
||||
conditional_odds_ratio=0,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
39.00054996869288))),
|
||||
(Parameters(table=[[5, 1], [0, 4]],
|
||||
confidence_level=0.95,
|
||||
alternative='two.sided'),
|
||||
RResults(pvalue=0.04761904761904761,
|
||||
conditional_odds_ratio=Inf,
|
||||
conditional_odds_ratio_ci=(1.024822256141754,
|
||||
Inf))),
|
||||
(Parameters(table=[[0, 1], [3, 2]],
|
||||
confidence_level=0.95,
|
||||
alternative='two.sided'),
|
||||
RResults(pvalue=1,
|
||||
conditional_odds_ratio=0,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
39.00054996869287))),
|
||||
(Parameters(table=[[200, 7], [8, 300]],
|
||||
confidence_level=0.95,
|
||||
alternative='two.sided'),
|
||||
RResults(pvalue=2.005657880389071e-122,
|
||||
conditional_odds_ratio=977.7866978606228,
|
||||
conditional_odds_ratio_ci=(349.2595113327733,
|
||||
3630.382605689872))),
|
||||
(Parameters(table=[[28, 21], [6, 1957]],
|
||||
confidence_level=0.95,
|
||||
alternative='two.sided'),
|
||||
RResults(pvalue=5.728437460831947e-44,
|
||||
conditional_odds_ratio=425.2403028434684,
|
||||
conditional_odds_ratio_ci=(152.4166024390096,
|
||||
1425.700792178893))),
|
||||
(Parameters(table=[[190, 800], [200, 900]],
|
||||
confidence_level=0.95,
|
||||
alternative='two.sided'),
|
||||
RResults(pvalue=0.574111858126088,
|
||||
conditional_odds_ratio=1.068697577856801,
|
||||
conditional_odds_ratio_ci=(0.8520462587912048,
|
||||
1.340148950273938))),
|
||||
(Parameters(table=[[100, 2], [1000, 5]],
|
||||
confidence_level=0.99,
|
||||
alternative='two.sided'),
|
||||
RResults(pvalue=0.1300759363430016,
|
||||
conditional_odds_ratio=0.25055839934223,
|
||||
conditional_odds_ratio_ci=(0.02502345007115455,
|
||||
6.304424772117853))),
|
||||
(Parameters(table=[[2, 7], [8, 2]],
|
||||
confidence_level=0.99,
|
||||
alternative='two.sided'),
|
||||
RResults(pvalue=0.02301413756522116,
|
||||
conditional_odds_ratio=0.0858623513573622,
|
||||
conditional_odds_ratio_ci=(0.001923034001462487,
|
||||
1.53670836950172))),
|
||||
(Parameters(table=[[5, 1], [10, 10]],
|
||||
confidence_level=0.99,
|
||||
alternative='two.sided'),
|
||||
RResults(pvalue=0.1973244147157191,
|
||||
conditional_odds_ratio=4.725646047336587,
|
||||
conditional_odds_ratio_ci=(0.2397970951413721,
|
||||
1291.342011095509))),
|
||||
(Parameters(table=[[5, 15], [20, 20]],
|
||||
confidence_level=0.99,
|
||||
alternative='two.sided'),
|
||||
RResults(pvalue=0.09580440012477633,
|
||||
conditional_odds_ratio=0.3394396617440851,
|
||||
conditional_odds_ratio_ci=(0.05127576113762925,
|
||||
1.717176678806983))),
|
||||
(Parameters(table=[[5, 16], [16, 25]],
|
||||
confidence_level=0.99,
|
||||
alternative='two.sided'),
|
||||
RResults(pvalue=0.2697004098849359,
|
||||
conditional_odds_ratio=0.4937791394540491,
|
||||
conditional_odds_ratio_ci=(0.07498546954483619,
|
||||
2.506969905199901))),
|
||||
(Parameters(table=[[10, 5], [10, 1]],
|
||||
confidence_level=0.99,
|
||||
alternative='two.sided'),
|
||||
RResults(pvalue=0.1973244147157192,
|
||||
conditional_odds_ratio=0.2116112781158479,
|
||||
conditional_odds_ratio_ci=(0.0007743881879531337,
|
||||
4.170192301163831))),
|
||||
(Parameters(table=[[10, 5], [10, 0]],
|
||||
confidence_level=0.99,
|
||||
alternative='two.sided'),
|
||||
RResults(pvalue=0.06126482213438735,
|
||||
conditional_odds_ratio=0,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
2.642491011905582))),
|
||||
(Parameters(table=[[5, 0], [1, 4]],
|
||||
confidence_level=0.99,
|
||||
alternative='two.sided'),
|
||||
RResults(pvalue=0.04761904761904762,
|
||||
conditional_odds_ratio=Inf,
|
||||
conditional_odds_ratio_ci=(0.496935393325443,
|
||||
Inf))),
|
||||
(Parameters(table=[[0, 5], [1, 4]],
|
||||
confidence_level=0.99,
|
||||
alternative='two.sided'),
|
||||
RResults(pvalue=1,
|
||||
conditional_odds_ratio=0,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
198.019801980198))),
|
||||
(Parameters(table=[[5, 1], [0, 4]],
|
||||
confidence_level=0.99,
|
||||
alternative='two.sided'),
|
||||
RResults(pvalue=0.04761904761904761,
|
||||
conditional_odds_ratio=Inf,
|
||||
conditional_odds_ratio_ci=(0.496935393325443,
|
||||
Inf))),
|
||||
(Parameters(table=[[0, 1], [3, 2]],
|
||||
confidence_level=0.99,
|
||||
alternative='two.sided'),
|
||||
RResults(pvalue=1,
|
||||
conditional_odds_ratio=0,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
198.019801980198))),
|
||||
(Parameters(table=[[200, 7], [8, 300]],
|
||||
confidence_level=0.99,
|
||||
alternative='two.sided'),
|
||||
RResults(pvalue=2.005657880389071e-122,
|
||||
conditional_odds_ratio=977.7866978606228,
|
||||
conditional_odds_ratio_ci=(270.0334165523604,
|
||||
5461.333333326708))),
|
||||
(Parameters(table=[[28, 21], [6, 1957]],
|
||||
confidence_level=0.99,
|
||||
alternative='two.sided'),
|
||||
RResults(pvalue=5.728437460831947e-44,
|
||||
conditional_odds_ratio=425.2403028434684,
|
||||
conditional_odds_ratio_ci=(116.7944750275836,
|
||||
1931.995993191814))),
|
||||
(Parameters(table=[[190, 800], [200, 900]],
|
||||
confidence_level=0.99,
|
||||
alternative='two.sided'),
|
||||
RResults(pvalue=0.574111858126088,
|
||||
conditional_odds_ratio=1.068697577856801,
|
||||
conditional_odds_ratio_ci=(0.7949398282935892,
|
||||
1.436229679394333))),
|
||||
(Parameters(table=[[100, 2], [1000, 5]],
|
||||
confidence_level=0.95,
|
||||
alternative='less'),
|
||||
RResults(pvalue=0.1300759363430016,
|
||||
conditional_odds_ratio=0.25055839934223,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
1.797867027270803))),
|
||||
(Parameters(table=[[2, 7], [8, 2]],
|
||||
confidence_level=0.95,
|
||||
alternative='less'),
|
||||
RResults(pvalue=0.0185217259520665,
|
||||
conditional_odds_ratio=0.0858623513573622,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
0.6785254803404526))),
|
||||
(Parameters(table=[[5, 1], [10, 10]],
|
||||
confidence_level=0.95,
|
||||
alternative='less'),
|
||||
RResults(pvalue=0.9782608695652173,
|
||||
conditional_odds_ratio=4.725646047336587,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
127.8497388102893))),
|
||||
(Parameters(table=[[5, 15], [20, 20]],
|
||||
confidence_level=0.95,
|
||||
alternative='less'),
|
||||
RResults(pvalue=0.05625775074399956,
|
||||
conditional_odds_ratio=0.3394396617440851,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
1.032332939718425))),
|
||||
(Parameters(table=[[5, 16], [16, 25]],
|
||||
confidence_level=0.95,
|
||||
alternative='less'),
|
||||
RResults(pvalue=0.1808979350599346,
|
||||
conditional_odds_ratio=0.4937791394540491,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
1.502407513296985))),
|
||||
(Parameters(table=[[10, 5], [10, 1]],
|
||||
confidence_level=0.95,
|
||||
alternative='less'),
|
||||
RResults(pvalue=0.1652173913043479,
|
||||
conditional_odds_ratio=0.2116112781158479,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
1.820421051562392))),
|
||||
(Parameters(table=[[10, 5], [10, 0]],
|
||||
confidence_level=0.95,
|
||||
alternative='less'),
|
||||
RResults(pvalue=0.0565217391304348,
|
||||
conditional_odds_ratio=0,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
1.06224603077045))),
|
||||
(Parameters(table=[[5, 0], [1, 4]],
|
||||
confidence_level=0.95,
|
||||
alternative='less'),
|
||||
RResults(pvalue=1,
|
||||
conditional_odds_ratio=Inf,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
Inf))),
|
||||
(Parameters(table=[[0, 5], [1, 4]],
|
||||
confidence_level=0.95,
|
||||
alternative='less'),
|
||||
RResults(pvalue=0.5,
|
||||
conditional_odds_ratio=0,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
19.00192394479939))),
|
||||
(Parameters(table=[[5, 1], [0, 4]],
|
||||
confidence_level=0.95,
|
||||
alternative='less'),
|
||||
RResults(pvalue=1,
|
||||
conditional_odds_ratio=Inf,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
Inf))),
|
||||
(Parameters(table=[[0, 1], [3, 2]],
|
||||
confidence_level=0.95,
|
||||
alternative='less'),
|
||||
RResults(pvalue=0.4999999999999999,
|
||||
conditional_odds_ratio=0,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
19.00192394479939))),
|
||||
(Parameters(table=[[200, 7], [8, 300]],
|
||||
confidence_level=0.95,
|
||||
alternative='less'),
|
||||
RResults(pvalue=1,
|
||||
conditional_odds_ratio=977.7866978606228,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
3045.460216525746))),
|
||||
(Parameters(table=[[28, 21], [6, 1957]],
|
||||
confidence_level=0.95,
|
||||
alternative='less'),
|
||||
RResults(pvalue=1,
|
||||
conditional_odds_ratio=425.2403028434684,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
1186.440170942579))),
|
||||
(Parameters(table=[[190, 800], [200, 900]],
|
||||
confidence_level=0.95,
|
||||
alternative='less'),
|
||||
RResults(pvalue=0.7416227010368963,
|
||||
conditional_odds_ratio=1.068697577856801,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
1.293551891610822))),
|
||||
(Parameters(table=[[100, 2], [1000, 5]],
|
||||
confidence_level=0.99,
|
||||
alternative='less'),
|
||||
RResults(pvalue=0.1300759363430016,
|
||||
conditional_odds_ratio=0.25055839934223,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
4.375946050832565))),
|
||||
(Parameters(table=[[2, 7], [8, 2]],
|
||||
confidence_level=0.99,
|
||||
alternative='less'),
|
||||
RResults(pvalue=0.0185217259520665,
|
||||
conditional_odds_ratio=0.0858623513573622,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
1.235282118191202))),
|
||||
(Parameters(table=[[5, 1], [10, 10]],
|
||||
confidence_level=0.99,
|
||||
alternative='less'),
|
||||
RResults(pvalue=0.9782608695652173,
|
||||
conditional_odds_ratio=4.725646047336587,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
657.2063583945989))),
|
||||
(Parameters(table=[[5, 15], [20, 20]],
|
||||
confidence_level=0.99,
|
||||
alternative='less'),
|
||||
RResults(pvalue=0.05625775074399956,
|
||||
conditional_odds_ratio=0.3394396617440851,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
1.498867660683128))),
|
||||
(Parameters(table=[[5, 16], [16, 25]],
|
||||
confidence_level=0.99,
|
||||
alternative='less'),
|
||||
RResults(pvalue=0.1808979350599346,
|
||||
conditional_odds_ratio=0.4937791394540491,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
2.186159386716762))),
|
||||
(Parameters(table=[[10, 5], [10, 1]],
|
||||
confidence_level=0.99,
|
||||
alternative='less'),
|
||||
RResults(pvalue=0.1652173913043479,
|
||||
conditional_odds_ratio=0.2116112781158479,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
3.335351451901569))),
|
||||
(Parameters(table=[[10, 5], [10, 0]],
|
||||
confidence_level=0.99,
|
||||
alternative='less'),
|
||||
RResults(pvalue=0.0565217391304348,
|
||||
conditional_odds_ratio=0,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
2.075407697450433))),
|
||||
(Parameters(table=[[5, 0], [1, 4]],
|
||||
confidence_level=0.99,
|
||||
alternative='less'),
|
||||
RResults(pvalue=1,
|
||||
conditional_odds_ratio=Inf,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
Inf))),
|
||||
(Parameters(table=[[0, 5], [1, 4]],
|
||||
confidence_level=0.99,
|
||||
alternative='less'),
|
||||
RResults(pvalue=0.5,
|
||||
conditional_odds_ratio=0,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
99.00009507969122))),
|
||||
(Parameters(table=[[5, 1], [0, 4]],
|
||||
confidence_level=0.99,
|
||||
alternative='less'),
|
||||
RResults(pvalue=1,
|
||||
conditional_odds_ratio=Inf,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
Inf))),
|
||||
(Parameters(table=[[0, 1], [3, 2]],
|
||||
confidence_level=0.99,
|
||||
alternative='less'),
|
||||
RResults(pvalue=0.4999999999999999,
|
||||
conditional_odds_ratio=0,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
99.00009507969123))),
|
||||
(Parameters(table=[[200, 7], [8, 300]],
|
||||
confidence_level=0.99,
|
||||
alternative='less'),
|
||||
RResults(pvalue=1,
|
||||
conditional_odds_ratio=977.7866978606228,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
4503.078257659934))),
|
||||
(Parameters(table=[[28, 21], [6, 1957]],
|
||||
confidence_level=0.99,
|
||||
alternative='less'),
|
||||
RResults(pvalue=1,
|
||||
conditional_odds_ratio=425.2403028434684,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
1811.766127544222))),
|
||||
(Parameters(table=[[190, 800], [200, 900]],
|
||||
confidence_level=0.99,
|
||||
alternative='less'),
|
||||
RResults(pvalue=0.7416227010368963,
|
||||
conditional_odds_ratio=1.068697577856801,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
1.396522811516685))),
|
||||
(Parameters(table=[[100, 2], [1000, 5]],
|
||||
confidence_level=0.95,
|
||||
alternative='greater'),
|
||||
RResults(pvalue=0.979790445314723,
|
||||
conditional_odds_ratio=0.25055839934223,
|
||||
conditional_odds_ratio_ci=(0.05119649909830196,
|
||||
Inf))),
|
||||
(Parameters(table=[[2, 7], [8, 2]],
|
||||
confidence_level=0.95,
|
||||
alternative='greater'),
|
||||
RResults(pvalue=0.9990149169715733,
|
||||
conditional_odds_ratio=0.0858623513573622,
|
||||
conditional_odds_ratio_ci=(0.007163749169069961,
|
||||
Inf))),
|
||||
(Parameters(table=[[5, 1], [10, 10]],
|
||||
confidence_level=0.95,
|
||||
alternative='greater'),
|
||||
RResults(pvalue=0.1652173913043478,
|
||||
conditional_odds_ratio=4.725646047336587,
|
||||
conditional_odds_ratio_ci=(0.5493234651081089,
|
||||
Inf))),
|
||||
(Parameters(table=[[5, 15], [20, 20]],
|
||||
confidence_level=0.95,
|
||||
alternative='greater'),
|
||||
RResults(pvalue=0.9849086665340765,
|
||||
conditional_odds_ratio=0.3394396617440851,
|
||||
conditional_odds_ratio_ci=(0.1003538933958604,
|
||||
Inf))),
|
||||
(Parameters(table=[[5, 16], [16, 25]],
|
||||
confidence_level=0.95,
|
||||
alternative='greater'),
|
||||
RResults(pvalue=0.9330176609214881,
|
||||
conditional_odds_ratio=0.4937791394540491,
|
||||
conditional_odds_ratio_ci=(0.146507416280863,
|
||||
Inf))),
|
||||
(Parameters(table=[[10, 5], [10, 1]],
|
||||
confidence_level=0.95,
|
||||
alternative='greater'),
|
||||
RResults(pvalue=0.9782608695652174,
|
||||
conditional_odds_ratio=0.2116112781158479,
|
||||
conditional_odds_ratio_ci=(0.007821681994077808,
|
||||
Inf))),
|
||||
(Parameters(table=[[10, 5], [10, 0]],
|
||||
confidence_level=0.95,
|
||||
alternative='greater'),
|
||||
RResults(pvalue=1,
|
||||
conditional_odds_ratio=0,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
Inf))),
|
||||
(Parameters(table=[[5, 0], [1, 4]],
|
||||
confidence_level=0.95,
|
||||
alternative='greater'),
|
||||
RResults(pvalue=0.02380952380952382,
|
||||
conditional_odds_ratio=Inf,
|
||||
conditional_odds_ratio_ci=(1.487678929918272,
|
||||
Inf))),
|
||||
(Parameters(table=[[0, 5], [1, 4]],
|
||||
confidence_level=0.95,
|
||||
alternative='greater'),
|
||||
RResults(pvalue=1,
|
||||
conditional_odds_ratio=0,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
Inf))),
|
||||
(Parameters(table=[[5, 1], [0, 4]],
|
||||
confidence_level=0.95,
|
||||
alternative='greater'),
|
||||
RResults(pvalue=0.0238095238095238,
|
||||
conditional_odds_ratio=Inf,
|
||||
conditional_odds_ratio_ci=(1.487678929918272,
|
||||
Inf))),
|
||||
(Parameters(table=[[0, 1], [3, 2]],
|
||||
confidence_level=0.95,
|
||||
alternative='greater'),
|
||||
RResults(pvalue=1,
|
||||
conditional_odds_ratio=0,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
Inf))),
|
||||
(Parameters(table=[[200, 7], [8, 300]],
|
||||
confidence_level=0.95,
|
||||
alternative='greater'),
|
||||
RResults(pvalue=2.005657880388915e-122,
|
||||
conditional_odds_ratio=977.7866978606228,
|
||||
conditional_odds_ratio_ci=(397.784359748113,
|
||||
Inf))),
|
||||
(Parameters(table=[[28, 21], [6, 1957]],
|
||||
confidence_level=0.95,
|
||||
alternative='greater'),
|
||||
RResults(pvalue=5.728437460831983e-44,
|
||||
conditional_odds_ratio=425.2403028434684,
|
||||
conditional_odds_ratio_ci=(174.7148056880929,
|
||||
Inf))),
|
||||
(Parameters(table=[[190, 800], [200, 900]],
|
||||
confidence_level=0.95,
|
||||
alternative='greater'),
|
||||
RResults(pvalue=0.2959825901308897,
|
||||
conditional_odds_ratio=1.068697577856801,
|
||||
conditional_odds_ratio_ci=(0.8828406663967776,
|
||||
Inf))),
|
||||
(Parameters(table=[[100, 2], [1000, 5]],
|
||||
confidence_level=0.99,
|
||||
alternative='greater'),
|
||||
RResults(pvalue=0.979790445314723,
|
||||
conditional_odds_ratio=0.25055839934223,
|
||||
conditional_odds_ratio_ci=(0.03045407081240429,
|
||||
Inf))),
|
||||
(Parameters(table=[[2, 7], [8, 2]],
|
||||
confidence_level=0.99,
|
||||
alternative='greater'),
|
||||
RResults(pvalue=0.9990149169715733,
|
||||
conditional_odds_ratio=0.0858623513573622,
|
||||
conditional_odds_ratio_ci=(0.002768053063547901,
|
||||
Inf))),
|
||||
(Parameters(table=[[5, 1], [10, 10]],
|
||||
confidence_level=0.99,
|
||||
alternative='greater'),
|
||||
RResults(pvalue=0.1652173913043478,
|
||||
conditional_odds_ratio=4.725646047336587,
|
||||
conditional_odds_ratio_ci=(0.2998184792279909,
|
||||
Inf))),
|
||||
(Parameters(table=[[5, 15], [20, 20]],
|
||||
confidence_level=0.99,
|
||||
alternative='greater'),
|
||||
RResults(pvalue=0.9849086665340765,
|
||||
conditional_odds_ratio=0.3394396617440851,
|
||||
conditional_odds_ratio_ci=(0.06180414342643172,
|
||||
Inf))),
|
||||
(Parameters(table=[[5, 16], [16, 25]],
|
||||
confidence_level=0.99,
|
||||
alternative='greater'),
|
||||
RResults(pvalue=0.9330176609214881,
|
||||
conditional_odds_ratio=0.4937791394540491,
|
||||
conditional_odds_ratio_ci=(0.09037094010066403,
|
||||
Inf))),
|
||||
(Parameters(table=[[10, 5], [10, 1]],
|
||||
confidence_level=0.99,
|
||||
alternative='greater'),
|
||||
RResults(pvalue=0.9782608695652174,
|
||||
conditional_odds_ratio=0.2116112781158479,
|
||||
conditional_odds_ratio_ci=(0.001521592095430679,
|
||||
Inf))),
|
||||
(Parameters(table=[[10, 5], [10, 0]],
|
||||
confidence_level=0.99,
|
||||
alternative='greater'),
|
||||
RResults(pvalue=1,
|
||||
conditional_odds_ratio=0,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
Inf))),
|
||||
(Parameters(table=[[5, 0], [1, 4]],
|
||||
confidence_level=0.99,
|
||||
alternative='greater'),
|
||||
RResults(pvalue=0.02380952380952382,
|
||||
conditional_odds_ratio=Inf,
|
||||
conditional_odds_ratio_ci=(0.6661157890359722,
|
||||
Inf))),
|
||||
(Parameters(table=[[0, 5], [1, 4]],
|
||||
confidence_level=0.99,
|
||||
alternative='greater'),
|
||||
RResults(pvalue=1,
|
||||
conditional_odds_ratio=0,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
Inf))),
|
||||
(Parameters(table=[[5, 1], [0, 4]],
|
||||
confidence_level=0.99,
|
||||
alternative='greater'),
|
||||
RResults(pvalue=0.0238095238095238,
|
||||
conditional_odds_ratio=Inf,
|
||||
conditional_odds_ratio_ci=(0.6661157890359725,
|
||||
Inf))),
|
||||
(Parameters(table=[[0, 1], [3, 2]],
|
||||
confidence_level=0.99,
|
||||
alternative='greater'),
|
||||
RResults(pvalue=1,
|
||||
conditional_odds_ratio=0,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
Inf))),
|
||||
(Parameters(table=[[200, 7], [8, 300]],
|
||||
confidence_level=0.99,
|
||||
alternative='greater'),
|
||||
RResults(pvalue=2.005657880388915e-122,
|
||||
conditional_odds_ratio=977.7866978606228,
|
||||
conditional_odds_ratio_ci=(297.9619252357688,
|
||||
Inf))),
|
||||
(Parameters(table=[[28, 21], [6, 1957]],
|
||||
confidence_level=0.99,
|
||||
alternative='greater'),
|
||||
RResults(pvalue=5.728437460831983e-44,
|
||||
conditional_odds_ratio=425.2403028434684,
|
||||
conditional_odds_ratio_ci=(130.3213490295859,
|
||||
Inf))),
|
||||
(Parameters(table=[[190, 800], [200, 900]],
|
||||
confidence_level=0.99,
|
||||
alternative='greater'),
|
||||
RResults(pvalue=0.2959825901308897,
|
||||
conditional_odds_ratio=1.068697577856801,
|
||||
conditional_odds_ratio_ci=(0.8176272148267533,
|
||||
Inf))),
|
||||
]
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
108
.CondaPkg/env/lib/python3.11/site-packages/scipy/stats/tests/data/nist_anova/AtmWtAg.dat
vendored
Normal file
108
.CondaPkg/env/lib/python3.11/site-packages/scipy/stats/tests/data/nist_anova/AtmWtAg.dat
vendored
Normal file
@@ -0,0 +1,108 @@
|
||||
NIST/ITL StRD
|
||||
Dataset Name: AtmWtAg (AtmWtAg.dat)
|
||||
|
||||
|
||||
File Format: ASCII
|
||||
Certified Values (lines 41 to 47)
|
||||
Data (lines 61 to 108)
|
||||
|
||||
|
||||
Procedure: Analysis of Variance
|
||||
|
||||
|
||||
Reference: Powell, L.J., Murphy, T.J. and Gramlich, J.W. (1982).
|
||||
"The Absolute Isotopic Abundance & Atomic Weight
|
||||
of a Reference Sample of Silver".
|
||||
NBS Journal of Research, 87, pp. 9-19.
|
||||
|
||||
|
||||
Data: 1 Factor
|
||||
2 Treatments
|
||||
24 Replicates/Cell
|
||||
48 Observations
|
||||
7 Constant Leading Digits
|
||||
Average Level of Difficulty
|
||||
Observed Data
|
||||
|
||||
|
||||
Model: 3 Parameters (mu, tau_1, tau_2)
|
||||
y_{ij} = mu + tau_i + epsilon_{ij}
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Certified Values:
|
||||
|
||||
Source of Sums of Mean
|
||||
Variation df Squares Squares F Statistic
|
||||
|
||||
|
||||
Between Instrument 1 3.63834187500000E-09 3.63834187500000E-09 1.59467335677930E+01
|
||||
Within Instrument 46 1.04951729166667E-08 2.28155932971014E-10
|
||||
|
||||
Certified R-Squared 2.57426544538321E-01
|
||||
|
||||
Certified Residual
|
||||
Standard Deviation 1.51048314446410E-05
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Data: Instrument AgWt
|
||||
1 107.8681568
|
||||
1 107.8681465
|
||||
1 107.8681572
|
||||
1 107.8681785
|
||||
1 107.8681446
|
||||
1 107.8681903
|
||||
1 107.8681526
|
||||
1 107.8681494
|
||||
1 107.8681616
|
||||
1 107.8681587
|
||||
1 107.8681519
|
||||
1 107.8681486
|
||||
1 107.8681419
|
||||
1 107.8681569
|
||||
1 107.8681508
|
||||
1 107.8681672
|
||||
1 107.8681385
|
||||
1 107.8681518
|
||||
1 107.8681662
|
||||
1 107.8681424
|
||||
1 107.8681360
|
||||
1 107.8681333
|
||||
1 107.8681610
|
||||
1 107.8681477
|
||||
2 107.8681079
|
||||
2 107.8681344
|
||||
2 107.8681513
|
||||
2 107.8681197
|
||||
2 107.8681604
|
||||
2 107.8681385
|
||||
2 107.8681642
|
||||
2 107.8681365
|
||||
2 107.8681151
|
||||
2 107.8681082
|
||||
2 107.8681517
|
||||
2 107.8681448
|
||||
2 107.8681198
|
||||
2 107.8681482
|
||||
2 107.8681334
|
||||
2 107.8681609
|
||||
2 107.8681101
|
||||
2 107.8681512
|
||||
2 107.8681469
|
||||
2 107.8681360
|
||||
2 107.8681254
|
||||
2 107.8681261
|
||||
2 107.8681450
|
||||
2 107.8681368
|
||||
85
.CondaPkg/env/lib/python3.11/site-packages/scipy/stats/tests/data/nist_anova/SiRstv.dat
vendored
Normal file
85
.CondaPkg/env/lib/python3.11/site-packages/scipy/stats/tests/data/nist_anova/SiRstv.dat
vendored
Normal file
@@ -0,0 +1,85 @@
|
||||
NIST/ITL StRD
|
||||
Dataset Name: SiRstv (SiRstv.dat)
|
||||
|
||||
|
||||
File Format: ASCII
|
||||
Certified Values (lines 41 to 47)
|
||||
Data (lines 61 to 85)
|
||||
|
||||
|
||||
Procedure: Analysis of Variance
|
||||
|
||||
|
||||
Reference: Ehrstein, James and Croarkin, M. Carroll.
|
||||
Unpublished NIST dataset.
|
||||
|
||||
|
||||
Data: 1 Factor
|
||||
5 Treatments
|
||||
5 Replicates/Cell
|
||||
25 Observations
|
||||
3 Constant Leading Digits
|
||||
Lower Level of Difficulty
|
||||
Observed Data
|
||||
|
||||
|
||||
Model: 6 Parameters (mu,tau_1, ... , tau_5)
|
||||
y_{ij} = mu + tau_i + epsilon_{ij}
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Certified Values:
|
||||
|
||||
Source of Sums of Mean
|
||||
Variation df Squares Squares F Statistic
|
||||
|
||||
Between Instrument 4 5.11462616000000E-02 1.27865654000000E-02 1.18046237440255E+00
|
||||
Within Instrument 20 2.16636560000000E-01 1.08318280000000E-02
|
||||
|
||||
Certified R-Squared 1.90999039051129E-01
|
||||
|
||||
Certified Residual
|
||||
Standard Deviation 1.04076068334656E-01
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Data: Instrument Resistance
|
||||
1 196.3052
|
||||
1 196.1240
|
||||
1 196.1890
|
||||
1 196.2569
|
||||
1 196.3403
|
||||
2 196.3042
|
||||
2 196.3825
|
||||
2 196.1669
|
||||
2 196.3257
|
||||
2 196.0422
|
||||
3 196.1303
|
||||
3 196.2005
|
||||
3 196.2889
|
||||
3 196.0343
|
||||
3 196.1811
|
||||
4 196.2795
|
||||
4 196.1748
|
||||
4 196.1494
|
||||
4 196.1485
|
||||
4 195.9885
|
||||
5 196.2119
|
||||
5 196.1051
|
||||
5 196.1850
|
||||
5 196.0052
|
||||
5 196.2090
|
||||
249
.CondaPkg/env/lib/python3.11/site-packages/scipy/stats/tests/data/nist_anova/SmLs01.dat
vendored
Normal file
249
.CondaPkg/env/lib/python3.11/site-packages/scipy/stats/tests/data/nist_anova/SmLs01.dat
vendored
Normal file
@@ -0,0 +1,249 @@
|
||||
NIST/ITL StRD
|
||||
Dataset Name: SmLs01 (SmLs01.dat)
|
||||
|
||||
|
||||
File Format: ASCII
|
||||
Certified Values (lines 41 to 47)
|
||||
Data (lines 61 to 249)
|
||||
|
||||
|
||||
Procedure: Analysis of Variance
|
||||
|
||||
|
||||
Reference: Simon, Stephen D. and Lesage, James P. (1989).
|
||||
"Assessing the Accuracy of ANOVA Calculations in
|
||||
Statistical Software".
|
||||
Computational Statistics & Data Analysis, 8, pp. 325-332.
|
||||
|
||||
|
||||
Data: 1 Factor
|
||||
9 Treatments
|
||||
21 Replicates/Cell
|
||||
189 Observations
|
||||
1 Constant Leading Digit
|
||||
Lower Level of Difficulty
|
||||
Generated Data
|
||||
|
||||
|
||||
Model: 10 Parameters (mu,tau_1, ... , tau_9)
|
||||
y_{ij} = mu + tau_i + epsilon_{ij}
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Certified Values:
|
||||
|
||||
Source of Sums of Mean
|
||||
Variation df Squares Squares F Statistic
|
||||
|
||||
Between Treatment 8 1.68000000000000E+00 2.10000000000000E-01 2.10000000000000E+01
|
||||
Within Treatment 180 1.80000000000000E+00 1.00000000000000E-02
|
||||
|
||||
Certified R-Squared 4.82758620689655E-01
|
||||
|
||||
Certified Residual
|
||||
Standard Deviation 1.00000000000000E-01
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Data: Treatment Response
|
||||
1 1.4
|
||||
1 1.3
|
||||
1 1.5
|
||||
1 1.3
|
||||
1 1.5
|
||||
1 1.3
|
||||
1 1.5
|
||||
1 1.3
|
||||
1 1.5
|
||||
1 1.3
|
||||
1 1.5
|
||||
1 1.3
|
||||
1 1.5
|
||||
1 1.3
|
||||
1 1.5
|
||||
1 1.3
|
||||
1 1.5
|
||||
1 1.3
|
||||
1 1.5
|
||||
1 1.3
|
||||
1 1.5
|
||||
2 1.3
|
||||
2 1.2
|
||||
2 1.4
|
||||
2 1.2
|
||||
2 1.4
|
||||
2 1.2
|
||||
2 1.4
|
||||
2 1.2
|
||||
2 1.4
|
||||
2 1.2
|
||||
2 1.4
|
||||
2 1.2
|
||||
2 1.4
|
||||
2 1.2
|
||||
2 1.4
|
||||
2 1.2
|
||||
2 1.4
|
||||
2 1.2
|
||||
2 1.4
|
||||
2 1.2
|
||||
2 1.4
|
||||
3 1.5
|
||||
3 1.4
|
||||
3 1.6
|
||||
3 1.4
|
||||
3 1.6
|
||||
3 1.4
|
||||
3 1.6
|
||||
3 1.4
|
||||
3 1.6
|
||||
3 1.4
|
||||
3 1.6
|
||||
3 1.4
|
||||
3 1.6
|
||||
3 1.4
|
||||
3 1.6
|
||||
3 1.4
|
||||
3 1.6
|
||||
3 1.4
|
||||
3 1.6
|
||||
3 1.4
|
||||
3 1.6
|
||||
4 1.3
|
||||
4 1.2
|
||||
4 1.4
|
||||
4 1.2
|
||||
4 1.4
|
||||
4 1.2
|
||||
4 1.4
|
||||
4 1.2
|
||||
4 1.4
|
||||
4 1.2
|
||||
4 1.4
|
||||
4 1.2
|
||||
4 1.4
|
||||
4 1.2
|
||||
4 1.4
|
||||
4 1.2
|
||||
4 1.4
|
||||
4 1.2
|
||||
4 1.4
|
||||
4 1.2
|
||||
4 1.4
|
||||
5 1.5
|
||||
5 1.4
|
||||
5 1.6
|
||||
5 1.4
|
||||
5 1.6
|
||||
5 1.4
|
||||
5 1.6
|
||||
5 1.4
|
||||
5 1.6
|
||||
5 1.4
|
||||
5 1.6
|
||||
5 1.4
|
||||
5 1.6
|
||||
5 1.4
|
||||
5 1.6
|
||||
5 1.4
|
||||
5 1.6
|
||||
5 1.4
|
||||
5 1.6
|
||||
5 1.4
|
||||
5 1.6
|
||||
6 1.3
|
||||
6 1.2
|
||||
6 1.4
|
||||
6 1.2
|
||||
6 1.4
|
||||
6 1.2
|
||||
6 1.4
|
||||
6 1.2
|
||||
6 1.4
|
||||
6 1.2
|
||||
6 1.4
|
||||
6 1.2
|
||||
6 1.4
|
||||
6 1.2
|
||||
6 1.4
|
||||
6 1.2
|
||||
6 1.4
|
||||
6 1.2
|
||||
6 1.4
|
||||
6 1.2
|
||||
6 1.4
|
||||
7 1.5
|
||||
7 1.4
|
||||
7 1.6
|
||||
7 1.4
|
||||
7 1.6
|
||||
7 1.4
|
||||
7 1.6
|
||||
7 1.4
|
||||
7 1.6
|
||||
7 1.4
|
||||
7 1.6
|
||||
7 1.4
|
||||
7 1.6
|
||||
7 1.4
|
||||
7 1.6
|
||||
7 1.4
|
||||
7 1.6
|
||||
7 1.4
|
||||
7 1.6
|
||||
7 1.4
|
||||
7 1.6
|
||||
8 1.3
|
||||
8 1.2
|
||||
8 1.4
|
||||
8 1.2
|
||||
8 1.4
|
||||
8 1.2
|
||||
8 1.4
|
||||
8 1.2
|
||||
8 1.4
|
||||
8 1.2
|
||||
8 1.4
|
||||
8 1.2
|
||||
8 1.4
|
||||
8 1.2
|
||||
8 1.4
|
||||
8 1.2
|
||||
8 1.4
|
||||
8 1.2
|
||||
8 1.4
|
||||
8 1.2
|
||||
8 1.4
|
||||
9 1.5
|
||||
9 1.4
|
||||
9 1.6
|
||||
9 1.4
|
||||
9 1.6
|
||||
9 1.4
|
||||
9 1.6
|
||||
9 1.4
|
||||
9 1.6
|
||||
9 1.4
|
||||
9 1.6
|
||||
9 1.4
|
||||
9 1.6
|
||||
9 1.4
|
||||
9 1.6
|
||||
9 1.4
|
||||
9 1.6
|
||||
9 1.4
|
||||
9 1.6
|
||||
9 1.4
|
||||
9 1.6
|
||||
1869
.CondaPkg/env/lib/python3.11/site-packages/scipy/stats/tests/data/nist_anova/SmLs02.dat
vendored
Normal file
1869
.CondaPkg/env/lib/python3.11/site-packages/scipy/stats/tests/data/nist_anova/SmLs02.dat
vendored
Normal file
File diff suppressed because it is too large
Load Diff
18069
.CondaPkg/env/lib/python3.11/site-packages/scipy/stats/tests/data/nist_anova/SmLs03.dat
vendored
Normal file
18069
.CondaPkg/env/lib/python3.11/site-packages/scipy/stats/tests/data/nist_anova/SmLs03.dat
vendored
Normal file
File diff suppressed because it is too large
Load Diff
249
.CondaPkg/env/lib/python3.11/site-packages/scipy/stats/tests/data/nist_anova/SmLs04.dat
vendored
Normal file
249
.CondaPkg/env/lib/python3.11/site-packages/scipy/stats/tests/data/nist_anova/SmLs04.dat
vendored
Normal file
@@ -0,0 +1,249 @@
|
||||
NIST/ITL StRD
|
||||
Dataset Name: SmLs04 (SmLs04.dat)
|
||||
|
||||
|
||||
File Format: ASCII
|
||||
Certified Values (lines 41 to 47)
|
||||
Data (lines 61 to 249)
|
||||
|
||||
|
||||
Procedure: Analysis of Variance
|
||||
|
||||
|
||||
Reference: Simon, Stephen D. and Lesage, James P. (1989).
|
||||
"Assessing the Accuracy of ANOVA Calculations in
|
||||
Statistical Software".
|
||||
Computational Statistics & Data Analysis, 8, pp. 325-332.
|
||||
|
||||
|
||||
Data: 1 Factor
|
||||
9 Treatments
|
||||
21 Replicates/Cell
|
||||
189 Observations
|
||||
7 Constant Leading Digits
|
||||
Average Level of Difficulty
|
||||
Generated Data
|
||||
|
||||
|
||||
Model: 10 Parameters (mu,tau_1, ... , tau_9)
|
||||
y_{ij} = mu + tau_i + epsilon_{ij}
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Certified Values:
|
||||
|
||||
Source of Sums of Mean
|
||||
Variation df Squares Squares F Statistic
|
||||
|
||||
Between Treatment 8 1.68000000000000E+00 2.10000000000000E-01 2.10000000000000E+01
|
||||
Within Treatment 180 1.80000000000000E+00 1.00000000000000E-02
|
||||
|
||||
Certified R-Squared 4.82758620689655E-01
|
||||
|
||||
Certified Residual
|
||||
Standard Deviation 1.00000000000000E-01
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Data: Treatment Response
|
||||
1 1000000.4
|
||||
1 1000000.3
|
||||
1 1000000.5
|
||||
1 1000000.3
|
||||
1 1000000.5
|
||||
1 1000000.3
|
||||
1 1000000.5
|
||||
1 1000000.3
|
||||
1 1000000.5
|
||||
1 1000000.3
|
||||
1 1000000.5
|
||||
1 1000000.3
|
||||
1 1000000.5
|
||||
1 1000000.3
|
||||
1 1000000.5
|
||||
1 1000000.3
|
||||
1 1000000.5
|
||||
1 1000000.3
|
||||
1 1000000.5
|
||||
1 1000000.3
|
||||
1 1000000.5
|
||||
2 1000000.3
|
||||
2 1000000.2
|
||||
2 1000000.4
|
||||
2 1000000.2
|
||||
2 1000000.4
|
||||
2 1000000.2
|
||||
2 1000000.4
|
||||
2 1000000.2
|
||||
2 1000000.4
|
||||
2 1000000.2
|
||||
2 1000000.4
|
||||
2 1000000.2
|
||||
2 1000000.4
|
||||
2 1000000.2
|
||||
2 1000000.4
|
||||
2 1000000.2
|
||||
2 1000000.4
|
||||
2 1000000.2
|
||||
2 1000000.4
|
||||
2 1000000.2
|
||||
2 1000000.4
|
||||
3 1000000.5
|
||||
3 1000000.4
|
||||
3 1000000.6
|
||||
3 1000000.4
|
||||
3 1000000.6
|
||||
3 1000000.4
|
||||
3 1000000.6
|
||||
3 1000000.4
|
||||
3 1000000.6
|
||||
3 1000000.4
|
||||
3 1000000.6
|
||||
3 1000000.4
|
||||
3 1000000.6
|
||||
3 1000000.4
|
||||
3 1000000.6
|
||||
3 1000000.4
|
||||
3 1000000.6
|
||||
3 1000000.4
|
||||
3 1000000.6
|
||||
3 1000000.4
|
||||
3 1000000.6
|
||||
4 1000000.3
|
||||
4 1000000.2
|
||||
4 1000000.4
|
||||
4 1000000.2
|
||||
4 1000000.4
|
||||
4 1000000.2
|
||||
4 1000000.4
|
||||
4 1000000.2
|
||||
4 1000000.4
|
||||
4 1000000.2
|
||||
4 1000000.4
|
||||
4 1000000.2
|
||||
4 1000000.4
|
||||
4 1000000.2
|
||||
4 1000000.4
|
||||
4 1000000.2
|
||||
4 1000000.4
|
||||
4 1000000.2
|
||||
4 1000000.4
|
||||
4 1000000.2
|
||||
4 1000000.4
|
||||
5 1000000.5
|
||||
5 1000000.4
|
||||
5 1000000.6
|
||||
5 1000000.4
|
||||
5 1000000.6
|
||||
5 1000000.4
|
||||
5 1000000.6
|
||||
5 1000000.4
|
||||
5 1000000.6
|
||||
5 1000000.4
|
||||
5 1000000.6
|
||||
5 1000000.4
|
||||
5 1000000.6
|
||||
5 1000000.4
|
||||
5 1000000.6
|
||||
5 1000000.4
|
||||
5 1000000.6
|
||||
5 1000000.4
|
||||
5 1000000.6
|
||||
5 1000000.4
|
||||
5 1000000.6
|
||||
6 1000000.3
|
||||
6 1000000.2
|
||||
6 1000000.4
|
||||
6 1000000.2
|
||||
6 1000000.4
|
||||
6 1000000.2
|
||||
6 1000000.4
|
||||
6 1000000.2
|
||||
6 1000000.4
|
||||
6 1000000.2
|
||||
6 1000000.4
|
||||
6 1000000.2
|
||||
6 1000000.4
|
||||
6 1000000.2
|
||||
6 1000000.4
|
||||
6 1000000.2
|
||||
6 1000000.4
|
||||
6 1000000.2
|
||||
6 1000000.4
|
||||
6 1000000.2
|
||||
6 1000000.4
|
||||
7 1000000.5
|
||||
7 1000000.4
|
||||
7 1000000.6
|
||||
7 1000000.4
|
||||
7 1000000.6
|
||||
7 1000000.4
|
||||
7 1000000.6
|
||||
7 1000000.4
|
||||
7 1000000.6
|
||||
7 1000000.4
|
||||
7 1000000.6
|
||||
7 1000000.4
|
||||
7 1000000.6
|
||||
7 1000000.4
|
||||
7 1000000.6
|
||||
7 1000000.4
|
||||
7 1000000.6
|
||||
7 1000000.4
|
||||
7 1000000.6
|
||||
7 1000000.4
|
||||
7 1000000.6
|
||||
8 1000000.3
|
||||
8 1000000.2
|
||||
8 1000000.4
|
||||
8 1000000.2
|
||||
8 1000000.4
|
||||
8 1000000.2
|
||||
8 1000000.4
|
||||
8 1000000.2
|
||||
8 1000000.4
|
||||
8 1000000.2
|
||||
8 1000000.4
|
||||
8 1000000.2
|
||||
8 1000000.4
|
||||
8 1000000.2
|
||||
8 1000000.4
|
||||
8 1000000.2
|
||||
8 1000000.4
|
||||
8 1000000.2
|
||||
8 1000000.4
|
||||
8 1000000.2
|
||||
8 1000000.4
|
||||
9 1000000.5
|
||||
9 1000000.4
|
||||
9 1000000.6
|
||||
9 1000000.4
|
||||
9 1000000.6
|
||||
9 1000000.4
|
||||
9 1000000.6
|
||||
9 1000000.4
|
||||
9 1000000.6
|
||||
9 1000000.4
|
||||
9 1000000.6
|
||||
9 1000000.4
|
||||
9 1000000.6
|
||||
9 1000000.4
|
||||
9 1000000.6
|
||||
9 1000000.4
|
||||
9 1000000.6
|
||||
9 1000000.4
|
||||
9 1000000.6
|
||||
9 1000000.4
|
||||
9 1000000.6
|
||||
1869
.CondaPkg/env/lib/python3.11/site-packages/scipy/stats/tests/data/nist_anova/SmLs05.dat
vendored
Normal file
1869
.CondaPkg/env/lib/python3.11/site-packages/scipy/stats/tests/data/nist_anova/SmLs05.dat
vendored
Normal file
File diff suppressed because it is too large
Load Diff
18069
.CondaPkg/env/lib/python3.11/site-packages/scipy/stats/tests/data/nist_anova/SmLs06.dat
vendored
Normal file
18069
.CondaPkg/env/lib/python3.11/site-packages/scipy/stats/tests/data/nist_anova/SmLs06.dat
vendored
Normal file
File diff suppressed because it is too large
Load Diff
249
.CondaPkg/env/lib/python3.11/site-packages/scipy/stats/tests/data/nist_anova/SmLs07.dat
vendored
Normal file
249
.CondaPkg/env/lib/python3.11/site-packages/scipy/stats/tests/data/nist_anova/SmLs07.dat
vendored
Normal file
@@ -0,0 +1,249 @@
|
||||
NIST/ITL StRD
|
||||
Dataset Name: SmLs07 (SmLs07.dat)
|
||||
|
||||
|
||||
File Format: ASCII
|
||||
Certified Values (lines 41 to 47)
|
||||
Data (lines 61 to 249)
|
||||
|
||||
|
||||
Procedure: Analysis of Variance
|
||||
|
||||
|
||||
Reference: Simon, Stephen D. and Lesage, James P. (1989).
|
||||
"Assessing the Accuracy of ANOVA Calculations in
|
||||
Statistical Software".
|
||||
Computational Statistics & Data Analysis, 8, pp. 325-332.
|
||||
|
||||
|
||||
Data: 1 Factor
|
||||
9 Treatments
|
||||
21 Replicates/Cell
|
||||
189 Observations
|
||||
13 Constant Leading Digits
|
||||
Higher Level of Difficulty
|
||||
Generated Data
|
||||
|
||||
|
||||
Model: 10 Parameters (mu,tau_1, ... , tau_9)
|
||||
y_{ij} = mu + tau_i + epsilon_{ij}
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Certified Values:
|
||||
|
||||
Source of Sums of Mean
|
||||
Variation df Squares Squares F Statistic
|
||||
|
||||
Between Treatment 8 1.68000000000000E+00 2.10000000000000E-01 2.10000000000000E+01
|
||||
Within Treatment 180 1.80000000000000E+00 1.00000000000000E-02
|
||||
|
||||
Certified R-Squared 4.82758620689655E-01
|
||||
|
||||
Certified Residual
|
||||
Standard Deviation 1.00000000000000E-01
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Data: Treatment Response
|
||||
1 1000000000000.4
|
||||
1 1000000000000.3
|
||||
1 1000000000000.5
|
||||
1 1000000000000.3
|
||||
1 1000000000000.5
|
||||
1 1000000000000.3
|
||||
1 1000000000000.5
|
||||
1 1000000000000.3
|
||||
1 1000000000000.5
|
||||
1 1000000000000.3
|
||||
1 1000000000000.5
|
||||
1 1000000000000.3
|
||||
1 1000000000000.5
|
||||
1 1000000000000.3
|
||||
1 1000000000000.5
|
||||
1 1000000000000.3
|
||||
1 1000000000000.5
|
||||
1 1000000000000.3
|
||||
1 1000000000000.5
|
||||
1 1000000000000.3
|
||||
1 1000000000000.5
|
||||
2 1000000000000.3
|
||||
2 1000000000000.2
|
||||
2 1000000000000.4
|
||||
2 1000000000000.2
|
||||
2 1000000000000.4
|
||||
2 1000000000000.2
|
||||
2 1000000000000.4
|
||||
2 1000000000000.2
|
||||
2 1000000000000.4
|
||||
2 1000000000000.2
|
||||
2 1000000000000.4
|
||||
2 1000000000000.2
|
||||
2 1000000000000.4
|
||||
2 1000000000000.2
|
||||
2 1000000000000.4
|
||||
2 1000000000000.2
|
||||
2 1000000000000.4
|
||||
2 1000000000000.2
|
||||
2 1000000000000.4
|
||||
2 1000000000000.2
|
||||
2 1000000000000.4
|
||||
3 1000000000000.5
|
||||
3 1000000000000.4
|
||||
3 1000000000000.6
|
||||
3 1000000000000.4
|
||||
3 1000000000000.6
|
||||
3 1000000000000.4
|
||||
3 1000000000000.6
|
||||
3 1000000000000.4
|
||||
3 1000000000000.6
|
||||
3 1000000000000.4
|
||||
3 1000000000000.6
|
||||
3 1000000000000.4
|
||||
3 1000000000000.6
|
||||
3 1000000000000.4
|
||||
3 1000000000000.6
|
||||
3 1000000000000.4
|
||||
3 1000000000000.6
|
||||
3 1000000000000.4
|
||||
3 1000000000000.6
|
||||
3 1000000000000.4
|
||||
3 1000000000000.6
|
||||
4 1000000000000.3
|
||||
4 1000000000000.2
|
||||
4 1000000000000.4
|
||||
4 1000000000000.2
|
||||
4 1000000000000.4
|
||||
4 1000000000000.2
|
||||
4 1000000000000.4
|
||||
4 1000000000000.2
|
||||
4 1000000000000.4
|
||||
4 1000000000000.2
|
||||
4 1000000000000.4
|
||||
4 1000000000000.2
|
||||
4 1000000000000.4
|
||||
4 1000000000000.2
|
||||
4 1000000000000.4
|
||||
4 1000000000000.2
|
||||
4 1000000000000.4
|
||||
4 1000000000000.2
|
||||
4 1000000000000.4
|
||||
4 1000000000000.2
|
||||
4 1000000000000.4
|
||||
5 1000000000000.5
|
||||
5 1000000000000.4
|
||||
5 1000000000000.6
|
||||
5 1000000000000.4
|
||||
5 1000000000000.6
|
||||
5 1000000000000.4
|
||||
5 1000000000000.6
|
||||
5 1000000000000.4
|
||||
5 1000000000000.6
|
||||
5 1000000000000.4
|
||||
5 1000000000000.6
|
||||
5 1000000000000.4
|
||||
5 1000000000000.6
|
||||
5 1000000000000.4
|
||||
5 1000000000000.6
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||||
5 1000000000000.4
|
||||
5 1000000000000.6
|
||||
5 1000000000000.4
|
||||
5 1000000000000.6
|
||||
5 1000000000000.4
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||||
5 1000000000000.6
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||||
6 1000000000000.3
|
||||
6 1000000000000.2
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||||
6 1000000000000.4
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||||
6 1000000000000.2
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||||
6 1000000000000.4
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||||
6 1000000000000.2
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||||
6 1000000000000.4
|
||||
6 1000000000000.2
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||||
6 1000000000000.4
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||||
6 1000000000000.2
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||||
6 1000000000000.4
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||||
6 1000000000000.2
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||||
6 1000000000000.4
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||||
6 1000000000000.2
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||||
6 1000000000000.4
|
||||
6 1000000000000.2
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||||
6 1000000000000.4
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||||
6 1000000000000.2
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||||
6 1000000000000.4
|
||||
6 1000000000000.2
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||||
6 1000000000000.4
|
||||
7 1000000000000.5
|
||||
7 1000000000000.4
|
||||
7 1000000000000.6
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7 1000000000000.4
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||||
7 1000000000000.6
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||||
7 1000000000000.4
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||||
7 1000000000000.6
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||||
7 1000000000000.4
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7 1000000000000.6
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7 1000000000000.4
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||||
7 1000000000000.6
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7 1000000000000.4
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7 1000000000000.6
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7 1000000000000.4
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7 1000000000000.6
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7 1000000000000.6
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7 1000000000000.4
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||||
7 1000000000000.6
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7 1000000000000.4
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7 1000000000000.6
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8 1000000000000.3
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8 1000000000000.2
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8 1000000000000.4
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8 1000000000000.2
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8 1000000000000.4
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8 1000000000000.2
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8 1000000000000.4
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8 1000000000000.2
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8 1000000000000.4
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8 1000000000000.2
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8 1000000000000.2
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8 1000000000000.4
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8 1000000000000.2
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8 1000000000000.4
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9 1000000000000.5
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9 1000000000000.6
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9 1000000000000.4
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||||
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||||
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||||
9 1000000000000.4
|
||||
9 1000000000000.6
|
||||
9 1000000000000.4
|
||||
9 1000000000000.6
|
||||
1869
.CondaPkg/env/lib/python3.11/site-packages/scipy/stats/tests/data/nist_anova/SmLs08.dat
vendored
Normal file
1869
.CondaPkg/env/lib/python3.11/site-packages/scipy/stats/tests/data/nist_anova/SmLs08.dat
vendored
Normal file
File diff suppressed because it is too large
Load Diff
18069
.CondaPkg/env/lib/python3.11/site-packages/scipy/stats/tests/data/nist_anova/SmLs09.dat
vendored
Normal file
18069
.CondaPkg/env/lib/python3.11/site-packages/scipy/stats/tests/data/nist_anova/SmLs09.dat
vendored
Normal file
File diff suppressed because it is too large
Load Diff
97
.CondaPkg/env/lib/python3.11/site-packages/scipy/stats/tests/data/nist_linregress/Norris.dat
vendored
Normal file
97
.CondaPkg/env/lib/python3.11/site-packages/scipy/stats/tests/data/nist_linregress/Norris.dat
vendored
Normal file
@@ -0,0 +1,97 @@
|
||||
NIST/ITL StRD
|
||||
Dataset Name: Norris (Norris.dat)
|
||||
|
||||
File Format: ASCII
|
||||
Certified Values (lines 31 to 46)
|
||||
Data (lines 61 to 96)
|
||||
|
||||
Procedure: Linear Least Squares Regression
|
||||
|
||||
Reference: Norris, J., NIST.
|
||||
Calibration of Ozone Monitors.
|
||||
|
||||
Data: 1 Response Variable (y)
|
||||
1 Predictor Variable (x)
|
||||
36 Observations
|
||||
Lower Level of Difficulty
|
||||
Observed Data
|
||||
|
||||
Model: Linear Class
|
||||
2 Parameters (B0,B1)
|
||||
|
||||
y = B0 + B1*x + e
|
||||
|
||||
|
||||
|
||||
Certified Regression Statistics
|
||||
|
||||
Standard Deviation
|
||||
Parameter Estimate of Estimate
|
||||
|
||||
B0 -0.262323073774029 0.232818234301152
|
||||
B1 1.00211681802045 0.429796848199937E-03
|
||||
|
||||
Residual
|
||||
Standard Deviation 0.884796396144373
|
||||
|
||||
R-Squared 0.999993745883712
|
||||
|
||||
|
||||
Certified Analysis of Variance Table
|
||||
|
||||
Source of Degrees of Sums of Mean
|
||||
Variation Freedom Squares Squares F Statistic
|
||||
|
||||
Regression 1 4255954.13232369 4255954.13232369 5436385.54079785
|
||||
Residual 34 26.6173985294224 0.782864662630069
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Data: y x
|
||||
0.1 0.2
|
||||
338.8 337.4
|
||||
118.1 118.2
|
||||
888.0 884.6
|
||||
9.2 10.1
|
||||
228.1 226.5
|
||||
668.5 666.3
|
||||
998.5 996.3
|
||||
449.1 448.6
|
||||
778.9 777.0
|
||||
559.2 558.2
|
||||
0.3 0.4
|
||||
0.1 0.6
|
||||
778.1 775.5
|
||||
668.8 666.9
|
||||
339.3 338.0
|
||||
448.9 447.5
|
||||
10.8 11.6
|
||||
557.7 556.0
|
||||
228.3 228.1
|
||||
998.0 995.8
|
||||
888.8 887.6
|
||||
119.6 120.2
|
||||
0.3 0.3
|
||||
0.6 0.3
|
||||
557.6 556.8
|
||||
339.3 339.1
|
||||
888.0 887.2
|
||||
998.5 999.0
|
||||
778.9 779.0
|
||||
10.2 11.1
|
||||
117.6 118.3
|
||||
228.9 229.2
|
||||
668.4 669.1
|
||||
449.2 448.9
|
||||
0.2 0.5
|
||||
|
||||
1499
.CondaPkg/env/lib/python3.11/site-packages/scipy/stats/tests/data/studentized_range_mpmath_ref.json
vendored
Normal file
1499
.CondaPkg/env/lib/python3.11/site-packages/scipy/stats/tests/data/studentized_range_mpmath_ref.json
vendored
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user