Tarefa nist_anova.smls06.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 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.
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
- 2.001
2001 - 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): 2.001
2001· 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_cdb3f988:F'] | obrigatória | passou | — | ["call_cdb3f988:F"] | — | — | call_cdb3f988 |
| 2 | ferramenta chamada answer call is infer.linear_hypothesis | obrigatória | passou | — | "infer.linear_hypothesis" | — | — | call_cdb3f988 |
| 3 | argumento igual infer.linear_hypothesis.se = 'classical' | obrigatória | passou | "classical" | "classical" | — | — | call_cdb3f988 |
| 4 | diagnóstico 8 vs 8 | obrigatória | passou | 88 | 88 | default rel 1e-6 (abs floor 1e-6) | — | call_cdb3f988 |
| 5 | diagnóstico 18000 vs 18000 | obrigatória | passou | 18.00018000 | 18.00018000 | default rel 1e-6 (abs floor 1e-6) | — | call_cdb3f988 |
| 6 | dígitos corretos (LRE) LRE 10.19 (min 9.0) | obrigatória | passou | 2.0012001 | 10,19110.191 | LRE >= 9.0 · LRE ≥ 9 | nist_anova.smls06.f | call_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
| 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 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)
fileae23a4613f8b46098984fa422b7db1ef16270f7f20c2787f128d57f0e2b778df· frame —dummies{"column":"treatment","drop_first":true,"prefix":"treatment"}
- 1Transformação
d·dummies{"column":"treatment","drop_first":true,"prefix":"treatment"}18009 → 18009 linhasFrame antes → depois:700eb25f65a6→3c6294d25176 - 2Turno do modelo
call_f354ee0freferenceFim: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_cdb3f988decidido 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_cdb3f988 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 = 02.001 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 = 00 probability 0— — Diagnósticosstatistic="F"df_numerator= 88df_denominator= 18.00018000se_type="classical"n_obs= 18.00918009n_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
d3c6294d25176826fcdbeeb6ba8f4f88752b3e4c9c29006551a440c2b2ee29b0a- sha256 do resultado
6657554eea35dc1f2619c8263cfcf2afad260319981582e59e8a9208002cdfb2
- 4Turno do modelo
call_a75ecd32referenceFim:tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cacheChamadas pedidassubmit_answer(meta-ferramenta){ "answer": "Estimativa (infer.linear_hypothesis): F = 2001.", "caveats": [], "figure_refs": [ "call_cdb3f988:F" ] }
- 5Guardaresposta aceita
- Números encontrados
- 2001
- Sem lastro
- —
- Não citados
- —
1 nova(s) tentativa(s) restante(s) - 6Resposta
Estimativa (infer.linear_hypothesis): F = 2001.
Figuras citadas:call_cdb3f988:F· Parada:answeredFigura da resposta:call_cdb3f988:F= 2.0012001.0000001288806 - 7Pontuação#0resposta com lastropassouobrigatória
every number backed - 8Pontuação#1resposta citapassouobrigatória
cited ['call_cdb3f988: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
18000 vs 18000 - 13Pontuação#6dígitos corretos (LRE)passouobrigatória
LRE 10.19 (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
f88dd9343533e4f0526c488cfcbfdb1cb708c96af97855d56d570e822f88ef26O 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.smls06.f.json | 1,2 KB | 01e9663e93374309c62f7cea1b86e2456b7c1353403253520b623180d60661f2 |
| specs/nist_anova.smls06.agent.json | 6,9 KB | 55cfd069a2b766283031b2ae4c48dbaa76bf0363d65c541bfc21d1a99fab04eb |
| traces/nist_anova.smls06.agent.e1.a1.jsonl | 15,5 KB | e36af58bd6492cdf0372b2d79ab7e794910a01fb5ffd24784cc52c7acc51786d |
| trajectories/nist_anova.smls06.agent.e1.a1.jsonl | 14,4 KB | 596ad21e37dc6603068fc02b9e1f9b32fcc243085ec78028114293bc76f56c44 |