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Tarefa nist_anova.smls03.agent

nist_anovaagentT2publicépoca 1, tentativa 1Execução: 20261015T171223Z-suite-nist_anova-scripted-7361e5

Veredito

passouPassou em todas as verificações obrigatórias.

Família do método (das chamadas de ferramentas): other (not an estimator in the table)

Pergunta feita ao analista

NIST Statistical Reference Datasets, one-way analysis of variance, problem SmLs03: generated data with 1 constant leading digits; one factor (treatment) with 9 levels and 18009 observations in a balanced design, model y_ij = mu + tau_i + e_ij. Dataset d holds the factor treatment (levels 1 to 9), the response response, and indicator columns treatment_2 to treatment_9 (1 when the row is at that level; level 1 is the reference). Compute the ANOVA F statistic for the hypothesis that all 9 treatment effects are equal: the between-treatment mean square divided by the within-treatment mean square.

Resposta

Estimativa (infer.linear_hypothesis): F = 2001.

Valores esperados e de onde vêm

nist_anova.smls03.f

Fonte
NIST/ITL StRD, Analysis of Variance: SmLs03 (SmLs03.dat), certified values
Tipo
certified
Localização
Certified Values block (lines 41-47), row 'Between Treatment F Statistic'
Trecho
Between Treatment F Statistic: 2.00100000000000E+03
Valor como publicado
2.00100000000000E+03
Valor
2.0012001
Conferido por
fonte primária · Parsed from the certified block of the file as itl.nist.gov served it (CRLF); the pinned mirror the suite fetches has the same bytes after CRLF -> LF. Re-derived from the data with exact rational arithmetic in tests/test_suite_nist_extra.py.
sha256 da cópia lida
e18404d0628b78670580b230a3a3b6cd997d959d00f92a2883a4da942b19377a (identifica a cópia lida, não um arquivo canônico)
Endereço
https://www.itl.nist.gov/div898/strd/anova/SmLs03.dat
Licença
Public domain: a work of the US Government (NIST/ITL Statistical Reference Datasets)
  • #6 dígitos corretos (LRE): 2.0012001 · LRE >= 9.0 · LRE ≥ 9

Verificações

Cada verificação, o que esperava, o que observou e a chamada que leu
#VerificaçãoObrigatóriaResultadoEsperadoObservadoTolerânciaFonteChamada
0resposta com lastro
every number backed
obrigatóriapassou—[]———
1resposta cita
cited ['call_92aa4938:F']
obrigatóriapassou—["call_92aa4938:F"]——call_92aa4938
2ferramenta chamada
answer call is infer.linear_hypothesis
obrigatóriapassou—"infer.linear_hypothesis"——call_92aa4938
3argumento igual
infer.linear_hypothesis.se = 'classical'
obrigatóriapassou"classical""classical"——call_92aa4938
4diagnóstico
8 vs 8
obrigatóriapassou8888default rel 1e-6 (abs floor 1e-6)—call_92aa4938
5diagnóstico
18000 vs 18000
obrigatóriapassou18.0001800018.00018000default rel 1e-6 (abs floor 1e-6)—call_92aa4938
6dígitos corretos (LRE)
LRE 13.61 (min 9.0)
obrigatóriapassou2.001200113,61413.614LRE >= 9.0 · LRE ≥ 9nist_anova.smls03.fcall_92aa4938

Caminho de referência e caminho do agente

A chamada de referência ao lado da chamada que deu a resposta, argumento por argumento.

caminho de referência do item · metadata_rule

Referência: infer.linear_hypothesisChamada do agente: infer.linear_hypothesis (call_92aa4938)igual
Argumentos da referência e do agente
ArgumentoReferênciaAgente
cluster——não fixado pela referência
dataset"d""d"igual
hypothesis"treatment_2 = 0, treatment_3 = 0, treatment_4 = 0, treatment_5 = 0, treatment_6 = 0, treatment_7 = 0, treatment_8 = 0, treatment_9 = 0""treatment_2 = 0, treatment_3 = 0, treatment_4 = 0, treatment_5 = 0, treatment_6 = 0, treatment_7 = 0, treatment_8 = 0, treatment_9 = 0"igual
se"classical""classical"igual
subset——não fixado pela referência
x["treatment_2","treatment_3","treatment_4","treatment_5","treatment_6","treatment_7","treatment_8","treatment_9"]["treatment_2","treatment_3","treatment_4","treatment_5","treatment_6","treatment_7","treatment_8","treatment_9"]igual
y"response""response"igual

Trajetória

Cada evento na ordem em que aconteceu: turnos do modelo, chamadas de ferramentas, guardas, transformações e pontuação.

  1. 0Início

    NIST Statistical Reference Datasets, one-way analysis of variance, problem SmLs03: generated data with 1 constant leading digits; one factor (treatment) with 9 levels and 18009 observations in a balanced design, model y_ij = mu + tau_i + e_ij. Dataset d holds the factor treatment (levels 1 to 9), the response response, and indicator columns treatment_2 to treatment_9 (1 when the row is at that level; level 1 is the reference). Compute the ANOVA F statistic for the hypothesis that all 9 treatment effects are equal: the between-treatment mean square divided by the within-treatment mean square.

    d (agent)

    NIST StRD SmLs03: 18009 observations; treatment = the treatment level (1 to 9), response = the response, treatment_2 … treatment_9 = 1 when the row is at that treatment level (level 1 is the reference)

    file eb23c78904a4d0a238a144503a4f7db7eb849be21937ef1e20f1dbff2d264355 · frame —
    1. dummies {"column":"treatment","drop_first":true,"prefix":"treatment"}
  2. 1Transformação
    d · dummies {"column":"treatment","drop_first":true,"prefix":"treatment"}
    18009 → 18009 linhas
    Frame antes → depois: 45db46fdb9a1 → 215a38263b07
  3. 2Turno do modelo
    call_6d6afcf4referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache
    Texto visível

    With classical errors, the joint test that every indicator's coefficient is zero is the one-way ANOVA F.

    Chamadas pedidas
    • infer.linear_hypothesis
      {
        "dataset": "d",
        "hypothesis": "treatment_2 = 0, treatment_3 = 0, treatment_4 = 0, treatment_5 = 0, treatment_6 = 0, treatment_7 = 0, treatment_8 = 0, treatment_9 = 0",
        "se": "classical",
        "x": [
          "treatment_2",
          "treatment_3",
          "treatment_4",
          "treatment_5",
          "treatment_6",
          "treatment_7",
          "treatment_8",
          "treatment_9"
        ],
        "y": "response"
      }
  4. 3Ferramenta
    infer.linear_hypothesisv0.1.0call_92aa4938decidido pelo modeloreexecutável

    Motivo: With classical errors, the joint test that every indicator's coefficient is zero is the one-way ANOVA F.

    Argumentos
    {
      "cluster": null,
      "dataset": "d",
      "hypothesis": "treatment_2 = 0, treatment_3 = 0, treatment_4 = 0, treatment_5 = 0, treatment_6 = 0, treatment_7 = 0, treatment_8 = 0, treatment_9 = 0",
      "se": "classical",
      "subset": null,
      "x": [
        "treatment_2",
        "treatment_3",
        "treatment_4",
        "treatment_5",
        "treatment_6",
        "treatment_7",
        "treatment_8",
        "treatment_9"
      ],
      "y": "response"
    }
    Figuras call_92aa4938
    ChaveValorValor brutoErro-padrãoIC
    F
    F statistic of treatment_2 = 0; treatment_3 = 0; treatment_4 = 0; treatment_5 = 0; treatment_6 = 0; treatment_7 = 0; treatment_8 = 0; treatment_9 = 0
    2.001 2001.0000000000487——
    p_value
    p-value of treatment_2 = 0; treatment_3 = 0; treatment_4 = 0; treatment_5 = 0; treatment_6 = 0; treatment_7 = 0; treatment_8 = 0; treatment_9 = 0
    0 probability0——
    Diagnósticos
    • statistic = "F"
    • df_numerator = 88
    • df_denominator = 18.00018000
    • se_type = "classical"
    • n_obs = 18.00918009
    • n_clusters = —
    Tabelas (2)
    {
      "coefficients": [
        {
          "ci_high": 1.4043818121039973,
          "ci_low": 1.3956181878960026,
          "coef": 1.4,
          "p": 0,
          "se": 0.002235509170049368,
          "t": 626.2555388976924,
          "term": "Intercept"
        },
        {
          "ci_high": -0.09380318189475649,
          "ci_low": -0.10619681810524369,
          "coef": -0.10000000000000009,
          "p": 9.559128140673934e-214,
          "se": 0.0031614873870932677,
          "t": -31.630681307870727,
          "term": "treatment_2"
        },
        {
          "ci_high": 0.10619681810524341,
          "ci_low": 0.09380318189475655,
          "coef": 0.09999999999999998,
          "p": 9.559128140429405e-214,
          "se": 0.003161487387093179,
          "t": 31.63068130787158,
          "term": "treatment_3"
        },
        {
          "ci_high": -0.09380318189475685,
          "ci_low": -0.10619681810524333,
          "coef": -0.10000000000000009,
          "p": 9.55912814013987e-214,
          "se": 0.0031614873870930817,
          "t": -31.63068130787259,
          "term": "treatment_4"
        },
        {
          "ci_high": 0.1061968181052438,
          "ci_low": 0.09380318189475616,
          "coef": 0.09999999999999998,
          "p": 9.559128141008471e-214,
          "se": 0.003161487387093381,
          "t": 31.63068130786956,
          "term": "treatment_5"
        },
        {
          "ci_high": -0.09380318189475624,
          "ci_low": -0.10619681810524394,
          "coef": -0.10000000000000009,
          "p": 9.559128141036975e-214,
          "se": 0.003161487387093394,
          "t": -31.630681307869462,
          "term": "treatment_6"
        },
        {
          "ci_high": 0.10619681810524394,
          "ci_low": 0.09380318189475602,
          "coef": 0.09999999999999998,
          "p": 9.559128141200495e-214,
          "se": 0.0031614873870934477,
          "t": 31.63068130786889,
          "term": "treatment_7"
        },
        {
          "ci_high": -0.09380318189475642,
          "ci_low": -0.10619681810524376,
          "coef": -0.10000000000000009,
          "p": 9.559128140774446e-214,
          "se": 0.003161487387093303,
          "t": -31.630681307870375,
          "term": "treatment_8"
        },
        {
          "ci_high": 0.10619681810524356,
          "ci_low": 0.0938031818947564,
          "coef": 0.09999999999999998,
          "p": 9.559128140666433e-214,
          "se": 0.003161487387093262,
          "t": 31.63068130787075,
          "term": "treatment_9"
        }
      ],
      "restrictions": [
        {
          "estimate": -0.10000000000000009,
          "hypothesized": 0,
          "restriction": "treatment_2 = 0"
        },
        {
          "estimate": 0.09999999999999998,
          "hypothesized": 0,
          "restriction": "treatment_3 = 0"
        },
        {
          "estimate": -0.10000000000000009,
          "hypothesized": 0,
          "restriction": "treatment_4 = 0"
        },
        {
          "estimate": 0.09999999999999998,
          "hypothesized": 0,
          "restriction": "treatment_5 = 0"
        },
        {
          "estimate": -0.10000000000000009,
          "hypothesized": 0,
          "restriction": "treatment_6 = 0"
        },
        {
          "estimate": 0.09999999999999998,
          "hypothesized": 0,
          "restriction": "treatment_7 = 0"
        },
        {
          "estimate": -0.10000000000000009,
          "hypothesized": 0,
          "restriction": "treatment_8 = 0"
        },
        {
          "estimate": 0.09999999999999998,
          "hypothesized": 0,
          "restriction": "treatment_9 = 0"
        }
      ]
    }
    Digests dos dados
    d 215a38263b0761fdb14d43943252b3e81604c46f1c8220b30b2a5e0e04db5593
    sha256 do resultado
    5abe7b0d0850e1358b2283900bc31856dca86e055dec909970634182b1e76da3
  5. 4Turno do modelo
    call_2ef81fd6referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache
    Chamadas pedidas
    • submit_answer (meta-ferramenta)
      {
        "answer": "Estimativa (infer.linear_hypothesis): F = 2001.",
        "caveats": [],
        "figure_refs": [
          "call_92aa4938:F"
        ]
      }
  6. 5Guarda
    resposta aceita
    Números encontrados
    2001
    Sem lastro
    —
    Não citados
    —
    1 nova(s) tentativa(s) restante(s)
  7. 6Resposta

    Estimativa (infer.linear_hypothesis): F = 2001.

    Figuras citadas: call_92aa4938:F · Parada: answered
    Figura da resposta: call_92aa4938:F = 2.0012001.0000000000487
  8. 7Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
  9. 8Pontuação
    #1resposta citapassouobrigatóriacited ['call_92aa4938:F']
  10. 9Pontuação
    #2ferramenta chamadapassouobrigatóriaanswer call is infer.linear_hypothesis
  11. 10Pontuação
    #3argumento igualpassouobrigatóriainfer.linear_hypothesis.se = 'classical'
  12. 11Pontuação
    #4diagnósticopassouobrigatória8 vs 8
  13. 12Pontuação
    #5diagnósticopassouobrigatória18000 vs 18000
  14. 13Pontuação
    #6dígitos corretos (LRE)passouobrigatóriaLRE 13.61 (min 9.0)

Épocas × verificações

Resultado de cada verificação em cada época pontuada
#0123456
Época 1passoupassoupassoupassoupassoupassoupassou

Tentativas

Cada tentativa é seu próprio arquivo e é publicada; só a última de cada época é pontuada.

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