Tarefa nist_anova.smls05.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 SmLs05: generated data with 7 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.smls05.f
- Fonte
- NIST/ITL StRD, Analysis of Variance: SmLs05 (SmLs05.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
7b1333e6e56ced74145096df774cfb1f6305213cb28f9a8222c418ca85d0c296(identifica a cópia lida, não um arquivo canônico)- Endereço
https://www.itl.nist.gov/div898/strd/anova/SmLs05.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_efb036e3:F'] | obrigatória | passou | — | ["call_efb036e3:F"] | — | — | call_efb036e3 |
| 2 | ferramenta chamada answer call is infer.linear_hypothesis | obrigatória | passou | — | "infer.linear_hypothesis" | — | — | call_efb036e3 |
| 3 | argumento igual infer.linear_hypothesis.se = 'classical' | obrigatória | passou | "classical" | "classical" | — | — | call_efb036e3 |
| 4 | diagnóstico 8 vs 8 | obrigatória | passou | 88 | 88 | default rel 1e-6 (abs floor 1e-6) | — | call_efb036e3 |
| 5 | diagnóstico 1800 vs 1800 | obrigatória | passou | 1.8001800 | 1.8001800 | default rel 1e-6 (abs floor 1e-6) | — | call_efb036e3 |
| 6 | dígitos corretos (LRE) LRE 10.21 (min 9.0) | obrigatória | passou | 201201 | 10,20910.209 | LRE >= 9.0 · LRE ≥ 9 | nist_anova.smls05.f | call_efb036e3 |
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 SmLs05: generated data with 7 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 SmLs05: 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)
file8894da0165059fb0cf2d377d6d087f10c2842b50cf1b47e737cc64ec14253d11· frame —dummies{"column":"treatment","drop_first":true,"prefix":"treatment"}
- 1Transformação
d·dummies{"column":"treatment","drop_first":true,"prefix":"treatment"}1809 → 1809 linhasFrame antes → depois:5c4c431b8c7d→5dabbf82a5b3 - 2Turno do modelo
call_ece5786ereferenceFim: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_efb036e3decidido 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_efb036e3 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 201.00000001241705— — 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 4.0371417802411765e-243— — Diagnósticosstatistic="F"df_numerator= 88df_denominator= 1.8001800se_type="classical"n_obs= 1.8091809n_clusters= —
Tabelas (2)
{ "coefficients": [ { "ci_high": 1000000.4138338221, "ci_low": 1000000.3861661779, "coef": 1000000.4, "p": 0, "se": 0.00705345615876849, "t": 141774525.49369738, "term": "Intercept" }, { "ci_high": -0.08043602115045867, "ci_low": -0.11956397891881135, "coef": -0.10000000003463501, "p": 4.7048751666506694e-23, "se": 0.009975093361334501, "t": -10.024968831094396, "term": "treatment_2" }, { "ci_high": 0.11956397886089329, "ci_low": 0.08043602109254058, "coef": 0.09999999997671694, "p": 4.7048754285580466e-23, "se": 0.009975093361334507, "t": 10.024968825288122, "term": "treatment_3" }, { "ci_high": -0.08043602115045892, "ci_low": -0.1195639789188111, "coef": -0.10000000003463501, "p": 4.7048751666448255e-23, "se": 0.009975093361334373, "t": -10.024968831094526, "term": "treatment_4" }, { "ci_high": 0.11956397886089294, "ci_low": 0.08043602109254093, "coef": 0.09999999997671694, "p": 4.7048754285501367e-23, "se": 0.009975093361334333, "t": 10.024968825288296, "term": "treatment_5" }, { "ci_high": -0.08043602115045878, "ci_low": -0.11956397891881124, "coef": -0.10000000003463501, "p": 4.70487516664813e-23, "se": 0.009975093361334444, "t": -10.024968831094453, "term": "treatment_6" }, { "ci_high": 0.11956397886089314, "ci_low": 0.08043602109254074, "coef": 0.09999999997671694, "p": 4.70487542855468e-23, "se": 0.009975093361334434, "t": 10.024968825288196, "term": "treatment_7" }, { "ci_high": -0.08043602115045888, "ci_low": -0.11956397891881114, "coef": -0.10000000003463501, "p": 4.704875166645947e-23, "se": 0.009975093361334397, "t": -10.024968831094501, "term": "treatment_8" }, { "ci_high": 0.11956397886089302, "ci_low": 0.08043602109254085, "coef": 0.09999999997671694, "p": 4.704875428552025e-23, "se": 0.009975093361334375, "t": 10.024968825288255, "term": "treatment_9" } ], "restrictions": [ { "estimate": -0.10000000003463501, "hypothesized": 0, "restriction": "treatment_2 = 0" }, { "estimate": 0.09999999997671694, "hypothesized": 0, "restriction": "treatment_3 = 0" }, { "estimate": -0.10000000003463501, "hypothesized": 0, "restriction": "treatment_4 = 0" }, { "estimate": 0.09999999997671694, "hypothesized": 0, "restriction": "treatment_5 = 0" }, { "estimate": -0.10000000003463501, "hypothesized": 0, "restriction": "treatment_6 = 0" }, { "estimate": 0.09999999997671694, "hypothesized": 0, "restriction": "treatment_7 = 0" }, { "estimate": -0.10000000003463501, "hypothesized": 0, "restriction": "treatment_8 = 0" }, { "estimate": 0.09999999997671694, "hypothesized": 0, "restriction": "treatment_9 = 0" } ] }- Digests dos dados
d5dabbf82a5b3632084bdad60fef9ac1e15a82b127b00081972f0445d23f5d1be- sha256 do resultado
19c28d970a0ee588aba663d1c49390729679bcef5f2e81fbd7accb02d5e3ae95
- 4Turno do modelo
call_25f868dfreferenceFim: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_efb036e3: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_efb036e3:F· Parada:answeredFigura da resposta:call_efb036e3:F= 201201.00000001241705 - 7Pontuação#0resposta com lastropassouobrigatória
every number backed - 8Pontuação#1resposta citapassouobrigatória
cited ['call_efb036e3: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 10.21 (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
d58785349b89c296adf73fc24b70ff5ac5374e6cdba717f8a14eadc6af535c87O 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.
| Arquivo | Tamanho | sha256 |
|---|---|---|
| attachments/433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420.json | 2,4 KB | 433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420 |
| attachments/510bad294f7419345abcc36f1e9b6fdbc47cf450cdf774a493f08a0063742acb.json | 2,5 KB | 510bad294f7419345abcc36f1e9b6fdbc47cf450cdf774a493f08a0063742acb |
| attachments/52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b.json | 64,0 KB | 52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b |
| attachments/abc3d4859f96d4af20668ae1bc5719b3d71a73558e6b4571c7007cad69dec1b8.json | 681 B | abc3d4859f96d4af20668ae1bc5719b3d71a73558e6b4571c7007cad69dec1b8 |
| attachments/aca4de173094efa6079176cc9dd06098cce968711184478e9b8322f42b2a18c6.json | 2,6 KB | aca4de173094efa6079176cc9dd06098cce968711184478e9b8322f42b2a18c6 |
| attachments/d31dd13b45095fbc71a3c9216c5eb92ac726d3df06edc56d5da76a51c8c3d023.json | 622 B | d31dd13b45095fbc71a3c9216c5eb92ac726d3df06edc56d5da76a51c8c3d023 |
| attachments/ea561c2acfc25b0d50a3954e14b116d696afca6de808dcdf3eb659b3405e30a5.json | 524 B | ea561c2acfc25b0d50a3954e14b116d696afca6de808dcdf3eb659b3405e30a5 |
| sources/nist_anova.smls05.f.json | 1,2 KB | 3fad3999338a84dabf3d585db7f25173185d9638fd19ba41245508ba56e3a8c6 |
| specs/nist_anova.smls05.agent.json | 6,9 KB | 489ba239e0a7036f013fd4f0b25b873bb91c5b8d87e0bde8261cc309d02f6bd1 |
| traces/nist_anova.smls05.agent.e1.a1.jsonl | 15,5 KB | 2bdc3ea4fadc9f828bbd7e78b66d151bc993caa1112e1aaaaf304e1608d9f353 |
| trajectories/nist_anova.smls05.agent.e1.a1.jsonl | 14,4 KB | 81feae64d3a20c2e8055f0088738258d07ccfb7a94b7541c8d67cae8effe0b7a |