Tarefa qrdata_socsci.354

Veredito

Pergunta feita ao analista

What percentage increase in hourly wage can be expected for each additional year of education, based on the data described? Please use linear regression and take all provided variables into consideration. Please provide your answer to the nearest hundredth of a percent.

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
   wage— To estimate the impact of an additional year of education on hourly wage, we look at a sample size representing individuals with varying levels of education and their hourly wages. The dataset wage.csv contains the necessary data for our analysis. The columns in this dataset include 'wage', representing the total income; 'hours', representing the total hours worked; and 'educ', representing the years of education; and other variables like the parents' education and the person's IQ score.
   wage_hourly
   Transformação "derive" aplicada à base "wage_hourly" (935 → 935 linhas). {"args":{"left":"wage","right":"hours"},"name":"hwage","op":"ratio"}
   Transformação "derive" aplicada à base "wage_hourly" (935 → 935 linhas). {"args":{"column":"hwage"},"name":"lhwage","op":"log"}
2. 2Chamada que gerou a resposta finalinfer.ols(Regressão linear por mínimos quadrados ordinários (MQO))citada na respostacoincide com a referência

   Ver chamada call_eb7b0866 nos detalhes

   OLS of log hourly wage on education with every other column of wage.csv as a control, wage and hours excepted because they build the outcome (Brave and True, ch. 5).

   Por que este é o caminho de referência: OLS of log hourly wage on education with every other column of wage.csv as a control, wage and hours excepted because they build the outcome (Brave and True, ch. 5).
   Argumentos principais

   controls:

   ["IQ","exper","tenure","age","married","black","south","urban","sibs","brthord","meduc","feduc"]

   dataset:

   "wage_hourly"

   intercept:

   true

   level:

   0,950.95

   se:

   "HC1"

   x:

   ["educ"]

   y:

   "lhwage"

   Resultado calculado

   • coef.educ = 0,0411472 0.041147191010061784
     Coefficient on educ in the regression of lhwage
   Diagnósticos: n_obs = 663663 (observations used) · k = 1414 (parameters estimated, fixed-effect dummies included) · r2 = 0,2148270.21482651654538643 · df_resid = 649649 · se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))
3. 3Conferência de lastro e resposta final

   Todos os 1 números escritos na resposta foram conferidos contra as ferramentas e o enunciado (nenhum número sem lastro).

   Números encontrados: 0,04115
   Figuras citadas: call_eb7b0866:coef.educ
   • ✓
     #0resposta com lastro

     every number of the answer is stated by a tool call
   • ✓
     #1figura da resposta próxima

     QRData eval.py: strictly inside gold ± 3% (a '%' gold divided by 100), on the answer figure after meta.answer_format

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

   What percentage increase in hourly wage can be expected for each additional year of education, based on the data described? Please use linear regression and take all provided variables into consideration. Please provide your answer to the nearest hundredth of a percent.

   wage (agent)

   To estimate the impact of an additional year of education on hourly wage, we look at a sample size representing individuals with varying levels of education and their hourly wages. The dataset wage.csv contains the necessary data for our analysis. The columns in this dataset include 'wage', representing the total income; 'hours', representing the total hours worked; and 'educ', representing the years of education; and other variables like the parents' education and the person's IQ score.

   file 3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970 · frame —

   wage_hourly (reference)

   file 3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970 · frame —

   1. derive {"args":{"left":"wage","right":"hours"},"name":"hwage","op":"ratio"}
   2. derive {"args":{"column":"hwage"},"name":"lhwage","op":"log"}
2. 1Transformação
   wage_hourly · derive {"args":{"left":"wage","right":"hours"},"name":"hwage","op":"ratio"}

   935 → 935 linhas

   Frame antes → depois: 5be148e1f4d2 → a0482fd036fd
3. 2Transformação
   wage_hourly · derive {"args":{"column":"hwage"},"name":"lhwage","op":"log"}

   935 → 935 linhas

   Frame antes → depois: a0482fd036fd → c1a0def8ce69
4. 3Turno do modelo
   call_f9651ac3referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   OLS of log hourly wage on education with every other column of wage.csv as a control, wage and hours excepted because they build the outcome (Brave and True, ch. 5).

   Chamadas pedidas

   • infer.ols

     {
       "controls": [
         "IQ",
         "exper",
         "tenure",
         "age",
         "married",
         "black",
         "south",
         "urban",
         "sibs",
         "brthord",
         "meduc",
         "feduc"
       ],
       "dataset": "wage_hourly",
       "x": [
         "educ"
       ],
       "y": "lhwage"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
5. 4Ferramenta
   infer.ols— Regressão linear por mínimos quadrados ordinários (MQO)v0.1.0call_eb7b0866decidido pelo modeloreexecutável

   Motivo: OLS of log hourly wage on education with every other column of wage.csv as a control, wage and hours excepted because they build the outcome (Brave and True, ch. 5).

   Argumentos

   {
     "cluster": null,
     "controls": [
       "IQ",
       "exper",
       "tenure",
       "age",
       "married",
       "black",
       "south",
       "urban",
       "sibs",
       "brthord",
       "meduc",
       "feduc"
     ],
     "dataset": "wage_hourly",
     "fe": [],
     "intercept": true,
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "weights": null,
     "x": [
       "educ"
     ],
     "y": "lhwage"
   }

   Figuras call_eb7b0866

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.educ Coefficient on educ in the regression of lhwage	0,0411472	0.041147191010061784	0,01030390.010303891744381146	0,0209142 – 0,0613802

   Diagnósticos

   • n_obs = 663663 (observations used)
   • k = 1414 (parameters estimated, fixed-effect dummies included)
   • r2 = 0,2148270.21482651654538643
   • df_resid = 649649
   • se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))
   • n_clusters = — (errors not clustered)

   Derivação matemática e pressupostos

   coef.educ

   1. Coeficiente de educ na regressão por mínimos quadrados ordinários de lhwage.→ 0,0411472 (0.041147191010061784)
   2. Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 0,0103039 (0.010303891744381146)
   3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 649 graus de liberdade vezes o erro-padrão.IC = β ± c · ep

   Pressupostos do estimador

   • the conditional mean of the outcome is linear in the regressors
   • errors are independent across observations; their variance may differ

   Tabelas (1)

   {
     "coefficients": [
       {
         "ci_high": 1.5658123159420887,
         "ci_low": 0.6653960456946,
         "coef": 1.1156041808183443,
         "p": 0.000001432408492699988,
         "se": 0.22927387537211072,
         "t": 4.865814646381855,
         "term": "Intercept"
       },
       {
         "ci_high": 0.06138018039546764,
         "ci_low": 0.020914201624655928,
         "coef": 0.041147191010061784,
         "p": 0.00007259125842923753,
         "se": 0.010303891744381146,
         "t": 3.993364063874207,
         "term": "educ"
       },
       {
         "ci_high": 0.006326918318431711,
         "ci_low": 0.0012550854520398578,
         "coef": 0.003791001885235784,
         "p": 0.003448202409667812,
         "se": 0.001291445762300233,
         "t": 2.935471233792673,
         "term": "IQ"
       },
       {
         "ci_high": 0.025479234574025345,
         "ci_low": 0.005203097823462425,
         "coef": 0.015341166198743886,
         "p": 0.0030740542288399714,
         "se": 0.0051629325279725,
         "t": 2.97140551723778,
         "term": "exper"
       },
       {
         "ci_high": 0.01583520274403824,
         "ci_low": 0.002874311672040478,
         "coef": 0.00935475720803936,
         "p": 0.004731559768830736,
         "se": 0.0033002443675700624,
         "t": 2.834565009780526,
         "term": "tenure"
       },
       {
         "ci_high": 0.02123155244030525,
         "ci_low": -0.004125857370451387,
         "coef": 0.008552847534926933,
         "p": 0.1857601203481472,
         "se": 0.006456782056051669,
         "t": 1.3246300495632668,
         "term": "age"
       },
       {
         "ci_high": 0.2869270179234046,
         "ci_low": 0.0720838440613439,
         "coef": 0.17950543099237423,
         "p": 0.001088679708979609,
         "se": 0.05470572744655065,
         "t": 3.281291363281059,
         "term": "married"
       },
       {
         "ci_high": 0.03499977609755121,
         "ci_low": -0.1951314183070978,
         "coef": -0.08006582110477328,
         "p": 0.17230384751117273,
         "se": 0.058598531066818685,
         "t": -1.3663451906921675,
         "term": "black"
       },
       {
         "ci_high": 0.03344181443942246,
         "ci_low": -0.11279639964073321,
         "coef": -0.039677292600655376,
         "p": 0.28702770022486096,
         "se": 0.037236779451395266,
         "t": -1.0655403927304101,
         "term": "south"
       },
       {
         "ci_high": 0.26081653315208203,
         "ci_low": 0.12428639483381722,
         "coef": 0.19255146399294962,
         "p": 4.4307299595909305e-8,
         "se": 0.03476480262702826,
         "t": 5.53868998074646,
         "term": "urban"
       },
       {
         "ci_high": 0.023485626806684833,
         "ci_low": -0.010540079099261595,
         "coef": 0.006472773853711618,
         "p": 0.4552807121676291,
         "se": 0.008663998767130018,
         "t": 0.7470885012436063,
         "term": "sibs"
       },
       {
         "ci_high": 0.019911436388694637,
         "ci_low": -0.03587024455674613,
         "coef": -0.007979404084025749,
         "p": 0.5744586784552015,
         "se": 0.014203743965684409,
         "t": -0.5617817459469575,
         "term": "brthord"
       },
       {
         "ci_high": 0.022540383372813493,
         "ci_low": -0.0047176346831491155,
         "coef": 0.008911374344832189,
         "p": 0.19962687172504123,
         "se": 0.006940735792052892,
         "t": 1.2839235798365454,
         "term": "meduc"
       },
       {
         "ci_high": 0.01958326098731464,
         "ci_low": -0.005840178783439715,
         "coef": 0.006871541101937463,
         "p": 0.28887022243311333,
         "se": 0.006473595329333792,
         "t": 1.061472142195923,
         "term": "feduc"
       }
     ]
   }

   • 272 rows were dropped because one of 'lhwage', 'educ', 'IQ', 'exper', 'tenure', 'age', 'married', 'black', 'south', 'urban', 'sibs', 'brthord', 'meduc', 'feduc' was missing

   Hashes SHA-256 e metadados de reprodução

   Digests dos dados

   wage_hourly c1a0def8ce69bb798f171f0eb617ba47263fc2daeacb114fbb1c4320538600ba

   sha256 do resultado

   c54f22f34779d8d67e16e3c82daaa4306a235b45498816dc84af5d60cfdcd915

   Resultado mostrado ao modelo
6. 5Turno do modelo
   call_8b5ebe7ereferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Chamadas pedidas

   • submit_answer (meta-ferramenta)

     {
       "answer": "Estimativa (infer.ols): coef.educ = 0,04115. Caminho de referência inferido à mão.",
       "caveats": [],
       "figure_refs": [
         "call_eb7b0866:coef.educ"
       ]
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
7. 6Guarda
   resposta aceita

   Números encontrados

   0,04115

   Sem lastro

   —

   Não citados

   —

   1 nova(s) tentativa(s) restante(s)
8. 7Resposta

   Estimativa (infer.ols): coef.educ = 0,04115. Caminho de referência inferido à mão.

   Figuras citadas: call_eb7b0866:coef.educ · Parada: answered

   Figura da resposta: call_eb7b0866:coef.educ = 0,04114720.041147191010061784 (se 0,01030390.010303891744381146)
9. 8Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
10. 9Pontuação
    #1figura da resposta próximapassouobrigatória0.0411 in (0.039867, 0.042333)?

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)

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