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Tarefa nist_anova.smls06.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 SmLs06: generated data with 7 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.smls06.f

Fonte
NIST/ITL StRD, Analysis of Variance: SmLs06 (SmLs06.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
0c0f09a2df05f2e9db5324a6aaeed85647651e874e9f5c3892b8f29fc4a200f7 (identifica a cópia lida, não um arquivo canônico)
Endereço
https://www.itl.nist.gov/div898/strd/anova/SmLs06.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_cdb3f988:F']
obrigatóriapassou—["call_cdb3f988:F"]——call_cdb3f988
2ferramenta chamada
answer call is infer.linear_hypothesis
obrigatóriapassou—"infer.linear_hypothesis"——call_cdb3f988
3argumento igual
infer.linear_hypothesis.se = 'classical'
obrigatóriapassou"classical""classical"——call_cdb3f988
4diagnóstico
8 vs 8
obrigatóriapassou8888default rel 1e-6 (abs floor 1e-6)—call_cdb3f988
5diagnóstico
18000 vs 18000
obrigatóriapassou18.0001800018.00018000default rel 1e-6 (abs floor 1e-6)—call_cdb3f988
6dígitos corretos (LRE)
LRE 10.19 (min 9.0)
obrigatóriapassou2.001200110,19110.191LRE >= 9.0 · LRE ≥ 9nist_anova.smls06.fcall_cdb3f988

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_cdb3f988)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 SmLs06: generated data with 7 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 SmLs06: 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 ae23a4613f8b46098984fa422b7db1ef16270f7f20c2787f128d57f0e2b778df · 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: 700eb25f65a6 → 3c6294d25176
  3. 2Turno do modelo
    call_f354ee0freferenceFim: 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_cdb3f988decidido 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_cdb3f988
    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 2001.0000001288806——
    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": 1000000.4043818121,
          "ci_low": 1000000.395618188,
          "coef": 1000000.4,
          "p": 0,
          "se": 0.0022355091701072006,
          "t": 447325563.8455048,
          "term": "Intercept"
        },
        {
          "ci_high": -0.0938031819294916,
          "ci_low": -0.10619681814029942,
          "coef": -0.10000000003489551,
          "p": 9.559125210513519e-214,
          "se": 0.0031614873871750556,
          "t": -31.630681318090097,
          "term": "treatment_2"
        },
        {
          "ci_high": 0.10619681808212067,
          "ci_low": 0.0938031818713132,
          "coef": 0.09999999997671694,
          "p": 9.559130486672132e-214,
          "se": 0.0031614873871749667,
          "t": 31.630681299688707,
          "term": "treatment_3"
        },
        {
          "ci_high": -0.09380318192949195,
          "ci_low": -0.10619681814029906,
          "coef": -0.10000000003489551,
          "p": 9.559125209979459e-214,
          "se": 0.003161487387174869,
          "t": -31.630681318091963,
          "term": "treatment_4"
        },
        {
          "ci_high": 0.10619681808212107,
          "ci_low": 0.0938031818713128,
          "coef": 0.09999999997671694,
          "p": 9.559130487249695e-214,
          "se": 0.0031614873871751684,
          "t": 31.630681299686692,
          "term": "treatment_5"
        },
        {
          "ci_high": -0.09380318192949134,
          "ci_low": -0.10619681814029967,
          "coef": -0.10000000003489551,
          "p": 9.55912521087506e-214,
          "se": 0.0031614873871751814,
          "t": -31.63068131808884,
          "term": "treatment_6"
        },
        {
          "ci_high": 0.1061968180821212,
          "ci_low": 0.09380318187131267,
          "coef": 0.09999999997671694,
          "p": 9.559130487441719e-214,
          "se": 0.003161487387175235,
          "t": 31.630681299686024,
          "term": "treatment_7"
        },
        {
          "ci_high": -0.09380318192949152,
          "ci_low": -0.10619681814029949,
          "coef": -0.10000000003489551,
          "p": 9.559125210615533e-214,
          "se": 0.0031614873871750908,
          "t": -31.630681318089746,
          "term": "treatment_8"
        },
        {
          "ci_high": 0.10619681808212084,
          "ci_low": 0.09380318187131303,
          "coef": 0.09999999997671694,
          "p": 9.559130486910658e-214,
          "se": 0.00316148738717505,
          "t": 31.630681299687875,
          "term": "treatment_9"
        }
      ],
      "restrictions": [
        {
          "estimate": -0.10000000003489551,
          "hypothesized": 0,
          "restriction": "treatment_2 = 0"
        },
        {
          "estimate": 0.09999999997671694,
          "hypothesized": 0,
          "restriction": "treatment_3 = 0"
        },
        {
          "estimate": -0.10000000003489551,
          "hypothesized": 0,
          "restriction": "treatment_4 = 0"
        },
        {
          "estimate": 0.09999999997671694,
          "hypothesized": 0,
          "restriction": "treatment_5 = 0"
        },
        {
          "estimate": -0.10000000003489551,
          "hypothesized": 0,
          "restriction": "treatment_6 = 0"
        },
        {
          "estimate": 0.09999999997671694,
          "hypothesized": 0,
          "restriction": "treatment_7 = 0"
        },
        {
          "estimate": -0.10000000003489551,
          "hypothesized": 0,
          "restriction": "treatment_8 = 0"
        },
        {
          "estimate": 0.09999999997671694,
          "hypothesized": 0,
          "restriction": "treatment_9 = 0"
        }
      ]
    }
    Digests dos dados
    d 3c6294d25176826fcdbeeb6ba8f4f88752b3e4c9c29006551a440c2b2ee29b0a
    sha256 do resultado
    6657554eea35dc1f2619c8263cfcf2afad260319981582e59e8a9208002cdfb2
  5. 4Turno do modelo
    call_a75ecd32referenceFim: 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_cdb3f988: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_cdb3f988:F · Parada: answered
    Figura da resposta: call_cdb3f988:F = 2.0012001.0000001288806
  8. 7Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
  9. 8Pontuação
    #1resposta citapassouobrigatóriacited ['call_cdb3f988: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 10.19 (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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