Tarefa nist_anova.smls02.agent
nist_anovaagentT2publicépoca 1, tentativa 1Execução: 20261015T171223Z-suite-nist_anova-scripted-7361e5Veredito
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.
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
- 201
201 - 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): 201
201· LRE >= 9.0 · LRE ≥ 9
Verificações
| # | Verificação | Obrigatória | Resultado | Esperado | Observado | Tolerância | Fonte | Chamada |
|---|---|---|---|---|---|---|---|---|
| 0 | resposta com lastro every number backed | obrigatória | passou | — | [] | — | — | — |
| 1 | resposta cita cited ['call_54c732f7:F'] | obrigatória | passou | — | ["call_54c732f7:F"] | — | — | call_54c732f7 |
| 2 | ferramenta chamada answer call is infer.linear_hypothesis | obrigatória | passou | — | "infer.linear_hypothesis" | — | — | call_54c732f7 |
| 3 | argumento igual infer.linear_hypothesis.se = 'classical' | obrigatória | passou | "classical" | "classical" | — | — | call_54c732f7 |
| 4 | diagnóstico 8 vs 8 | obrigatória | passou | 88 | 88 | default rel 1e-6 (abs floor 1e-6) | — | call_54c732f7 |
| 5 | diagnóstico 1800 vs 1800 | obrigatória | passou | 1.8001800 | 1.8001800 | default rel 1e-6 (abs floor 1e-6) | — | call_54c732f7 |
| 6 | dígitos corretos (LRE) LRE 14.59 (min 9.0) | obrigatória | passou | 201201 | 14,59414.594 | LRE >= 9.0 · LRE ≥ 9 | nist_anova.smls02.f | call_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
| Argumento | Referência | Agente | |
|---|---|---|---|
| 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.
- 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)
file9c23f2c51935740a123d6882ee41b874b697b3618bdfe2915d1b76370817a080· frame —dummies{"column":"treatment","drop_first":true,"prefix":"treatment"}
- 1Transformação
d·dummies{"column":"treatment","drop_first":true,"prefix":"treatment"}1809 → 1809 linhasFrame antes → depois:6cb2bb68727f→40265bb6a424 - 2Turno do modelo
call_c5d6ecc5referenceFim:tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cacheTexto visívelWith classical errors, the joint test that every indicator's coefficient is zero is the one-way ANOVA F.
Chamadas pedidasinfer.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" }
- 3Ferramenta
infer.linear_hypothesisv0.1.0call_54c732f7decidido pelo modeloreexecutávelMotivo: 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 Chave Valor Valor bruto Erro-padrão IC 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 = 0201 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 = 00,00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000403714 probability 4.037141885758291e-243— — Diagnósticosstatistic="F"df_numerator= 88df_denominator= 1.8001800se_type="classical"n_obs= 1.8091809n_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
d40265bb6a4240c34fa765dd90a3fe6242ebe37ae0ae601c4865ceef0c45d0cd1- sha256 do resultado
6ab73cb1e801e4618823f52c940745782f7f2ed5a1e6eec82fab521678245fd7
- 4Turno do modelo
call_39f78169referenceFim:tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cacheChamadas pedidassubmit_answer(meta-ferramenta){ "answer": "Estimativa (infer.linear_hypothesis): F = 201,0.", "caveats": [], "figure_refs": [ "call_54c732f7:F" ] }
- 5Guardaresposta aceita
- Números encontrados
- 201,0
- Sem lastro
- —
- Não citados
- —
1 nova(s) tentativa(s) restante(s) - 6Resposta
Estimativa (infer.linear_hypothesis): F = 201,0.
Figuras citadas:call_54c732f7:F· Parada:answeredFigura da resposta:call_54c732f7:F= 201200.9999999999995 - 7Pontuação#0resposta com lastropassouobrigatória
every number backed - 8Pontuação#1resposta citapassouobrigatória
cited ['call_54c732f7:F'] - 9Pontuação#2ferramenta chamadapassouobrigatória
answer call is infer.linear_hypothesis - 10Pontuação#3argumento igualpassouobrigatória
infer.linear_hypothesis.se = 'classical' - 11Pontuação#4diagnósticopassouobrigatória
8 vs 8 - 12Pontuação#5diagnósticopassouobrigatória
1800 vs 1800 - 13Pontuação#6dígitos corretos (LRE)passouobrigatória
LRE 14.59 (min 9.0)
Épocas × verificações
| # | 0 | 1 | 2 | 3 | 4 | 5 | 6 |
|---|---|---|---|---|---|---|---|
| Época 1 | passou | passou | passou | passou | passou | passou | passou |
Tentativas
Cada tentativa é seu próprio arquivo e é publicada; só a última de cada época é pontuada.
- época 1, tentativa 1passoupontuada(exibindo)
Downloads desta tarefa
9e62bfe464c64ccec3011383135c8558d698b960f5d7dcc89907700b477f9fffO 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.
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| sources/nist_anova.smls02.f.json | 1,2 KB | 6242118874f8283982f44712457bfc42b1c32413bef7f45c8f1d0a8cba31e692 |
| specs/nist_anova.smls02.agent.json | 6,8 KB | 9ebcc0fa858d8b6cb30513ddeb2586bfefca083707fd8cce910f462848ab4834 |
| traces/nist_anova.smls02.agent.e1.a1.jsonl | 15,5 KB | 163fd9e8f377b9b91473f212096369669c0b65657f20946cf66c98afedf2b98a |
| trajectories/nist_anova.smls02.agent.e1.a1.jsonl | 14,4 KB | d44e53547f171b5da1d7088dd6969fa1b6a786951d86799bf908d971526e8a52 |