Tarefa nist_anova.smls01.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 SmLs01: generated data with 1 constant leading digits; one factor (treatment) with 9 levels and 189 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 = 21,00.
Valores esperados e de onde vêm
nist_anova.smls01.f
- Fonte
- NIST/ITL StRD, Analysis of Variance: SmLs01 (SmLs01.dat), certified values
- Tipo
- certified
- Localização
- Certified Values block (lines 41-47), row 'Between Treatment F Statistic'
- Trecho
Between Treatment F Statistic: 2.10000000000000E+01
- Valor como publicado
2.10000000000000E+01- Valor
- 21
21 - 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
ad28cac0d4fb79ffea2522b55349083bdea10f12a6f8c46cfbabcccdb4d6b896(identifica a cópia lida, não um arquivo canônico)- Endereço
https://www.itl.nist.gov/div898/strd/anova/SmLs01.dat- Licença
- Public domain: a work of the US Government (NIST/ITL Statistical Reference Datasets)
- #6 dígitos corretos (LRE): 21
21· 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_60821375:F'] | obrigatória | passou | — | ["call_60821375:F"] | — | — | call_60821375 |
| 2 | ferramenta chamada answer call is infer.linear_hypothesis | obrigatória | passou | — | "infer.linear_hypothesis" | — | — | call_60821375 |
| 3 | argumento igual infer.linear_hypothesis.se = 'classical' | obrigatória | passou | "classical" | "classical" | — | — | call_60821375 |
| 4 | diagnóstico 8 vs 8 | obrigatória | passou | 88 | 88 | default rel 1e-6 (abs floor 1e-6) | — | call_60821375 |
| 5 | diagnóstico 180 vs 180 | obrigatória | passou | 180180 | 180180 | default rel 1e-6 (abs floor 1e-6) | — | call_60821375 |
| 6 | dígitos corretos (LRE) LRE 15.00 (min 9.0) | obrigatória | passou | 2121 | 1515 | LRE >= 9.0 · LRE ≥ 9 | nist_anova.smls01.f | call_60821375 |
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 SmLs01: generated data with 1 constant leading digits; one factor (treatment) with 9 levels and 189 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 SmLs01: 189 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)
file29d9c979d46d845ed72c0fb0ed79083c8313833bbcf72040326640d87e2e40e4· frame —dummies{"column":"treatment","drop_first":true,"prefix":"treatment"}
- 1Transformação
d·dummies{"column":"treatment","drop_first":true,"prefix":"treatment"}189 → 189 linhasFrame antes → depois:dc5f63a03dbd→c22685aa85f5 - 2Turno do modelo
call_65c75765referenceFim: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_60821375decidido 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_60821375 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 = 021 20.99999999999999— — 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 = 00,000000000000000000000258326 probability 2.5832643372690353e-22— — Diagnósticosstatistic="F"df_numerator= 88df_denominator= 180180se_type="classical"n_obs= 189189n_clusters= —
Tabelas (2)
{ "coefficients": [ { "ci_high": 1.4430594267159302, "ci_low": 1.3569405732840696, "coef": 1.4, "p": 6.048277422611796e-126, "se": 0.02182178902359925, "t": 64.15605972938172, "term": "Intercept" }, { "ci_high": -0.039104774750320995, "ci_low": -0.16089522524967914, "coef": -0.10000000000000007, "p": 0.0014220432123559668, "se": 0.030860669992418387, "t": -3.240370349203932, "term": "treatment_2" }, { "ci_high": 0.16089522524967903, "ci_low": 0.03910477475032091, "coef": 0.09999999999999998, "p": 0.0014220432123559763, "se": 0.03086066999241838, "t": 3.2403703492039297, "term": "treatment_3" }, { "ci_high": -0.03910477475032096, "ci_low": -0.1608952252496792, "coef": -0.10000000000000007, "p": 0.0014220432123559763, "se": 0.030860669992418405, "t": -3.24037034920393, "term": "treatment_4" }, { "ci_high": 0.16089522524967909, "ci_low": 0.03910477475032088, "coef": 0.09999999999999998, "p": 0.0014220432123559857, "se": 0.030860669992418398, "t": 3.240370349203928, "term": "treatment_5" }, { "ci_high": -0.03910477475032095, "ci_low": -0.1608952252496792, "coef": -0.10000000000000007, "p": 0.0014220432123559796, "se": 0.03086066999241841, "t": -3.2403703492039293, "term": "treatment_6" }, { "ci_high": 0.16089522524967909, "ci_low": 0.03910477475032087, "coef": 0.09999999999999998, "p": 0.0014220432123559857, "se": 0.0308606699924184, "t": 3.2403703492039275, "term": "treatment_7" }, { "ci_high": -0.039104774750320974, "ci_low": -0.16089522524967917, "coef": -0.10000000000000007, "p": 0.00142204321235597, "se": 0.030860669992418398, "t": -3.240370349203931, "term": "treatment_8" }, { "ci_high": 0.16089522524967909, "ci_low": 0.039104774750320884, "coef": 0.09999999999999998, "p": 0.0014220432123559857, "se": 0.030860669992418394, "t": 3.240370349203928, "term": "treatment_9" } ], "restrictions": [ { "estimate": -0.10000000000000007, "hypothesized": 0, "restriction": "treatment_2 = 0" }, { "estimate": 0.09999999999999998, "hypothesized": 0, "restriction": "treatment_3 = 0" }, { "estimate": -0.10000000000000007, "hypothesized": 0, "restriction": "treatment_4 = 0" }, { "estimate": 0.09999999999999998, "hypothesized": 0, "restriction": "treatment_5 = 0" }, { "estimate": -0.10000000000000007, "hypothesized": 0, "restriction": "treatment_6 = 0" }, { "estimate": 0.09999999999999998, "hypothesized": 0, "restriction": "treatment_7 = 0" }, { "estimate": -0.10000000000000007, "hypothesized": 0, "restriction": "treatment_8 = 0" }, { "estimate": 0.09999999999999998, "hypothesized": 0, "restriction": "treatment_9 = 0" } ] }- Digests dos dados
dc22685aa85f5542bbedfed4848aef80c1c8027b542d82f83bebd6f0c7955b06e- sha256 do resultado
b1485c0754387cd7fe2a881e64494e6adac8a1da0d4e5752b89200dedb4abb10
- 4Turno do modelo
call_a7ed665creferenceFim:tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cacheChamadas pedidassubmit_answer(meta-ferramenta){ "answer": "Estimativa (infer.linear_hypothesis): F = 21,00.", "caveats": [], "figure_refs": [ "call_60821375:F" ] }
- 5Guardaresposta aceita
- Números encontrados
- 21,00
- Sem lastro
- —
- Não citados
- —
1 nova(s) tentativa(s) restante(s) - 6Resposta
Estimativa (infer.linear_hypothesis): F = 21,00.
Figuras citadas:call_60821375:F· Parada:answeredFigura da resposta:call_60821375:F= 2120.99999999999999 - 7Pontuação#0resposta com lastropassouobrigatória
every number backed - 8Pontuação#1resposta citapassouobrigatória
cited ['call_60821375: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
180 vs 180 - 13Pontuação#6dígitos corretos (LRE)passouobrigatória
LRE 15.00 (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
b84c3d8cf4d05c5801e391f459696deeb403853cf89cb1fe05a965a3aad4b530O 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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| specs/nist_anova.smls01.agent.json | 6,8 KB | cc13a44eaad0340e25eb9e4fddfe6a6f14acfb4fe6464d3ef4b14b0cfa526c7c |
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| trajectories/nist_anova.smls01.agent.e1.a1.jsonl | 14,4 KB | 475b7563510780c1051d9b2ff8da2b99f59cca14857b960f7ff676889af5357c |