Pular para o conteúdo

Tarefa nist_anova.smls02.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 SmLs02: generated data with 1 constant leading digits; one factor (treatment) with 9 levels and 1809 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 = 201,0.

Valores esperados e de onde vêm

nist_anova.smls02.f

Fonte
NIST/ITL StRD, Analysis of Variance: SmLs02 (SmLs02.dat), certified values
Tipo
certified
Localização
Certified Values block (lines 41-47), row 'Between Treatment F Statistic'
Trecho
Between Treatment F Statistic: 2.01000000000000E+02
Valor como publicado
2.01000000000000E+02
Valor
201201
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
708e32eaf36769abe7533e7dcd1b6dc67091b60df938dd37c4f0e65660ef7b0b (identifica a cópia lida, não um arquivo canônico)
Endereço
https://www.itl.nist.gov/div898/strd/anova/SmLs02.dat
Licença
Public domain: a work of the US Government (NIST/ITL Statistical Reference Datasets)
  • #6 dígitos corretos (LRE): 201201 · 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_54c732f7:F']
obrigatóriapassou—["call_54c732f7:F"]——call_54c732f7
2ferramenta chamada
answer call is infer.linear_hypothesis
obrigatóriapassou—"infer.linear_hypothesis"——call_54c732f7
3argumento igual
infer.linear_hypothesis.se = 'classical'
obrigatóriapassou"classical""classical"——call_54c732f7
4diagnóstico
8 vs 8
obrigatóriapassou8888default rel 1e-6 (abs floor 1e-6)—call_54c732f7
5diagnóstico
1800 vs 1800
obrigatóriapassou1.80018001.8001800default rel 1e-6 (abs floor 1e-6)—call_54c732f7
6dígitos corretos (LRE)
LRE 14.59 (min 9.0)
obrigatóriapassou20120114,59414.594LRE >= 9.0 · LRE ≥ 9nist_anova.smls02.fcall_54c732f7

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_54c732f7)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 SmLs02: generated data with 1 constant leading digits; one factor (treatment) with 9 levels and 1809 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 SmLs02: 1809 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 9c23f2c51935740a123d6882ee41b874b697b3618bdfe2915d1b76370817a080 · frame —
    1. dummies {"column":"treatment","drop_first":true,"prefix":"treatment"}
  2. 1Transformação
    d · dummies {"column":"treatment","drop_first":true,"prefix":"treatment"}
    1809 → 1809 linhas
    Frame antes → depois: 6cb2bb68727f → 40265bb6a424
  3. 2Turno do modelo
    call_c5d6ecc5referenceFim: 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_54c732f7decidido 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_54c732f7
    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
    201 200.9999999999995——
    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
    0,00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000403714 probability4.037141885758291e-243——
    Diagnósticos
    • statistic = "F"
    • df_numerator = 88
    • df_denominator = 1.8001800
    • se_type = "classical"
    • n_obs = 1.8091809
    • n_clusters = —
    Tabelas (2)
    {
      "coefficients": [
        {
          "ci_high": 1.4138338221356335,
          "ci_low": 1.3861661778643664,
          "coef": 1.4,
          "p": 0,
          "se": 0.007053456158586019,
          "t": 198.48425630260851,
          "term": "Intercept"
        },
        {
          "ci_high": -0.08043602111632987,
          "ci_low": -0.1195639788836703,
          "coef": -0.10000000000000009,
          "p": 4.7048753115723204e-23,
          "se": 0.009975093361076447,
          "t": -10.024968827881601,
          "term": "treatment_2"
        },
        {
          "ci_high": 0.11956397888367021,
          "ci_low": 0.08043602111632975,
          "coef": 0.09999999999999998,
          "p": 4.704875311573031e-23,
          "se": 0.009975093361076453,
          "t": 10.024968827881585,
          "term": "treatment_3"
        },
        {
          "ci_high": -0.08043602111633012,
          "ci_low": -0.11956397888367006,
          "coef": -0.10000000000000009,
          "p": 4.704875311566536e-23,
          "se": 0.009975093361076319,
          "t": -10.024968827881729,
          "term": "treatment_4"
        },
        {
          "ci_high": 0.11956397888366986,
          "ci_low": 0.0804360211163301,
          "coef": 0.09999999999999998,
          "p": 4.7048753115651787e-23,
          "se": 0.009975093361076279,
          "t": 10.024968827881759,
          "term": "treatment_5"
        },
        {
          "ci_high": -0.08043602111632998,
          "ci_low": -0.1195639788836702,
          "coef": -0.10000000000000009,
          "p": 4.7048753115697243e-23,
          "se": 0.00997509336107639,
          "t": -10.024968827881658,
          "term": "treatment_6"
        },
        {
          "ci_high": 0.11956397888367007,
          "ci_low": 0.08043602111632989,
          "coef": 0.09999999999999998,
          "p": 4.7048753115697243e-23,
          "se": 0.00997509336107638,
          "t": 10.024968827881658,
          "term": "treatment_7"
        },
        {
          "ci_high": -0.08043602111633008,
          "ci_low": -0.1195639788836701,
          "coef": -0.10000000000000009,
          "p": 4.704875311567541e-23,
          "se": 0.009975093361076343,
          "t": -10.024968827881706,
          "term": "treatment_8"
        },
        {
          "ci_high": 0.11956397888366994,
          "ci_low": 0.08043602111633001,
          "coef": 0.09999999999999998,
          "p": 4.704875311567068e-23,
          "se": 0.00997509336107632,
          "t": 10.024968827881716,
          "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 40265bb6a4240c34fa765dd90a3fe6242ebe37ae0ae601c4865ceef0c45d0cd1
    sha256 do resultado
    6ab73cb1e801e4618823f52c940745782f7f2ed5a1e6eec82fab521678245fd7
  5. 4Turno do modelo
    call_39f78169referenceFim: 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 = 201,0.",
        "caveats": [],
        "figure_refs": [
          "call_54c732f7:F"
        ]
      }
  6. 5Guarda
    resposta aceita
    Números encontrados
    201,0
    Sem lastro
    —
    Não citados
    —
    1 nova(s) tentativa(s) restante(s)
  7. 6Resposta

    Estimativa (infer.linear_hypothesis): F = 201,0.

    Figuras citadas: call_54c732f7:F · Parada: answered
    Figura da resposta: call_54c732f7:F = 201200.9999999999995
  8. 7Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
  9. 8Pontuação
    #1resposta citapassouobrigatóriacited ['call_54c732f7: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
    #5diagnósticopassouobrigatória1800 vs 1800
  14. 13Pontuação
    #6dígitos corretos (LRE)passouobrigatóriaLRE 14.59 (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.

Downloads desta tarefa

Pacote da tarefa (.zip)
42,3 KB · sha256 9e62bfe464c64ccec3011383135c8558d698b960f5d7dcc89907700b477f9fff

O hash de um zip é só de transporte: o que ancora a integridade é o RUN_SHA256SUMS que vai dentro dele, cópia exata do SHA256SUMS da execução.

Downloads desta tarefa
ArquivoTamanhosha256
attachments/433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420.json2,4 KB433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420
attachments/52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b.json64,0 KB52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b
attachments/64dafc339f5a91e73db345541b83c329e73c0bb33ace22972cece72c303dea8a.json681 B64dafc339f5a91e73db345541b83c329e73c0bb33ace22972cece72c303dea8a
attachments/7c5a1060ea78f57b6e5e311278f2d0d2ebc40319f05ec940305043d08c9ccb72.json622 B7c5a1060ea78f57b6e5e311278f2d0d2ebc40319f05ec940305043d08c9ccb72
attachments/86844b2933123c67b2469f4d2aff664e4c120645ca2872a6a042fa409ccd95aa.json2,5 KB86844b2933123c67b2469f4d2aff664e4c120645ca2872a6a042fa409ccd95aa
attachments/a06fe87ae1dacfb13d298631938e8f7b28bb6af732ad0d5923100d04fd6fa558.json2,6 KBa06fe87ae1dacfb13d298631938e8f7b28bb6af732ad0d5923100d04fd6fa558
attachments/ea561c2acfc25b0d50a3954e14b116d696afca6de808dcdf3eb659b3405e30a5.json524 Bea561c2acfc25b0d50a3954e14b116d696afca6de808dcdf3eb659b3405e30a5
sources/nist_anova.smls02.f.json1,2 KB6242118874f8283982f44712457bfc42b1c32413bef7f45c8f1d0a8cba31e692
specs/nist_anova.smls02.agent.json6,8 KB9ebcc0fa858d8b6cb30513ddeb2586bfefca083707fd8cce910f462848ab4834
traces/nist_anova.smls02.agent.e1.a1.jsonl15,5 KB163fd9e8f377b9b91473f212096369669c0b65657f20946cf66c98afedf2b98a
trajectories/nist_anova.smls02.agent.e1.a1.jsonl14,4 KBd44e53547f171b5da1d7088dd6969fa1b6a786951d86799bf908d971526e8a52