Tarefa nist_anova.smls05.tool

Veredito

Pergunta feita ao analista

Tarefa de ferramenta: nenhuma pergunta; a especificação de referência é chamada direto.

Como o agente resolveu e como foi validado

Resumo passo a passo do que o agente executou, por que o caminho está correto e como cada verificação conferiu o resultado.

1. 1Preparação da base de dados
   d— 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)
   Transformação "dummies" aplicada à base "d" (1809 → 1809 linhas). {"column":"treatment","drop_first":true,"prefix":"treatment"}
2. 2Chamada que gerou a resposta finalinfer.linear_hypothesis(Teste de hipótese linear conjunta (estatística F / Wald))citada na respostacoincide com a referência

   Ver chamada call_a5e6e94c nos detalhes
   Argumentos principais

   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"

   Resultado calculado

   • F = 201 201.00000001241705
     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
   • p_value = 0,00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000403714probabilidade 4.0371417802411765e-243
     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
   Diagnósticos: statistic = "F" (F = W/m against F(m, n − k)) · df_numerator = 88 (number of restrictions) · df_denominator = 1.8001800 (residual degrees of freedom n − k) · se_type = "classical" (classical (homoskedastic) standard errors) · n_obs = 1.8091809
3. 3Conferência de lastro e resposta final
   • ✓
     #0situação da ferramenta

     Verifica a situação de retorno da ferramenta.
   • ✓
     #1diagnósticodf_numerator

     certified between-treatment df
   • ✓
     #2diagnósticodf_denominator

     certified within-treatment df
   • ✓
     #3dígitos corretos (LRE)F

     certified F statistic

Valores esperados e de onde vêm

Verificações

Caminho de referência e caminho do agente

A chamada de referência ao lado da chamada que deu a resposta, argumento por argumento.

Trajetória

Cada evento na ordem em que aconteceu: turnos do modelo, chamadas de ferramentas, guardas, transformações e pontuação.

1. 0Início

   Tarefa de ferramenta: nenhuma pergunta; a especificação de referência é chamada direto.

   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)

   file 8894da0165059fb0cf2d377d6d087f10c2842b50cf1b47e737cc64ec14253d11 · 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: 5c4c431b8c7d → 5dabbf82a5b3
3. 2Ferramenta
   infer.linear_hypothesis— Teste de hipótese linear conjunta (estatística F / Wald)v0.1.0call_a5e6e94cdecidido pelo códigoreexecutável

   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_a5e6e94c

   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 = 0	201	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 = 0	0,00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000403714probabilidade	4.0371417802411765e-243	—	—

   Diagnósticos

   • statistic = "F" (F = W/m against F(m, n − k))
   • df_numerator = 88 (number of restrictions)
   • df_denominator = 1.8001800 (residual degrees of freedom n − k)
   • se_type = "classical" (classical (homoskedastic) standard errors)
   • n_obs = 1.8091809
   • n_clusters = — (errors not clustered)

   Derivação matemática e pressupostos

   F

   1. Estatística de Wald das 8 restrições, com a covariância classical (homoskedastic) standard errors.→ 1.608 (1608.0000000993364)
   2. Estatística F: a de Wald dividida pelo número de restrições.F = W / m→ 201 (201.00000001241705)

   p_value

   1. Probabilidade de uma F(8, 1800) exceder a estatística observada.→ 0,00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000403714 (4.0371417802411765e-243)

   Pressupostos do estimador

   • errors are independent and have the same variance for every observation

   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"
       }
     ]
   }

   Hashes SHA-256 e metadados de reprodução

   Digests dos dados

   d 5dabbf82a5b3632084bdad60fef9ac1e15a82b127b00081972f0445d23f5d1be

   sha256 do resultado

   a1f91df587967fb525d2f311a391e4f68491a47022ae840a72e7413640a23c1a

4. 3Pontuação
   #0situação da ferramentapassouobrigatóriastatus ok
5. 4Pontuação
   #1diagnósticopassouobrigatória8 vs 8
6. 5Pontuação
   #2diagnósticopassouobrigatória1800 vs 1800
7. 6Pontuação
   #3dígitos corretos (LRE)passouobrigatóriaLRE 10.21 (min 9.0)

Épocas × verificações

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