Tarefa qrdata_socsci.391

Veredito

Pergunta feita ao analista

Fit a linear regression model using the Democratic margin in the two-party vote share as the response variable and the perceived competence for Democratic candidates as the predictor. What is the estimated slope of the model? Please round to the nearest thousandth.

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
   face— Several psychologists have reported the intriguing result of an experiment showing that facial appearance predicts election outcomes better than chance. In their experiment, the researchers briefly showed student subjects the black-and-white head shots of two candidates from a US congressional election (winner and runner-up). The exposure of subjects to facial pictures lasted less than a second, and the subjects were then asked to evaluate the two candidates in terms of their perceived competence. The researchers used these competence measures to predict election outcomes. The key hypothesis is whether or not a within-a-second evaluation of facial appearance can predict election outcomes. The CSV data set, face.csv, contains the data from the experiment. Note that we include data only from subjects who did not know the candidates’ political parties, their policies, or even which candidate was the incumbent or challenger. They were simply making snap judgments about which candidate appeared more competent based on their facial expression alone. Variable Description congress: session of Congress year: year of the election state: state of the election winner: name of the winner loser: name of the runner-up w.party: party of the winner l.party: party of the loser d.votes: number of votes for the Democratic candidate r.votes: number of votes for the Republican candidate d.comp: competence measure for the Democratic candidate r.comp: competence measure for the Republican candidate
   face_margin
   Transformação "derive" aplicada à base "face_margin" (119 → 119 linhas). {"args":{"left":"d.votes","right":"r.votes"},"name":"d_minus_r","op":"diff"}
   Transformação "derive" aplicada à base "face_margin" (119 → 119 linhas). {"args":{"columns":["d.votes","r.votes"]},"name":"two_party","op":"sum"}
   Transformação "derive" aplicada à base "face_margin" (119 → 119 linhas). {"args":{"left":"d_minus_r","right":"two_party"},"name":"d_margin","op":"ratio"}
2. 2Chamada que gerou a resposta finalinfer.ols(Regressão linear por mínimos quadrados ordinários (MQO))citada na respostacoincide com a referência

   Ver chamada call_736e4030 nos detalhes

   OLS of the Democratic two-party margin on the Democratic candidate's perceived competence.

   Por que este é o caminho de referência: OLS of the Democratic two-party margin on the Democratic candidate's perceived competence.
   Argumentos principais

   dataset:

   "face_margin"

   intercept:

   true

   level:

   0,950.95

   se:

   "HC1"

   x:

   ["d.comp"]

   y:

   "d_margin"

   Resultado calculado

   • coef.d.comp = 0,660381 0.6603814567765842
     Coefficient on d.comp in the regression of d_margin
   Diagnósticos: n_obs = 119119 (observations used) · k = 22 (parameters estimated, fixed-effect dummies included) · r2 = 0,1872940.18729363431996082 · df_resid = 117117 · 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: 0,6604
   Figuras citadas: call_736e4030:coef.d.comp
   • ✓
     #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

   Fit a linear regression model using the Democratic margin in the two-party vote share as the response variable and the perceived competence for Democratic candidates as the predictor. What is the estimated slope of the model? Please round to the nearest thousandth.

   face (agent)

   Several psychologists have reported the intriguing result of an experiment showing that facial appearance predicts election outcomes better than chance. In their experiment, the researchers briefly showed student subjects the black-and-white head shots of two candidates from a US congressional election (winner and runner-up). The exposure of subjects to facial pictures lasted less than a second, and the subjects were then asked to evaluate the two candidates in terms of their perceived competence. The researchers used these competence measures to predict election outcomes. The key hypothesis is whether or not a within-a-second evaluation of facial appearance can predict election outcomes. The CSV data set, face.csv, contains the data from the experiment. Note that we include data only from subjects who did not know the candidates’ political parties, their policies, or even which candidate was the incumbent or challenger. They were simply making snap judgments about which candidate appeared more competent based on their facial expression alone. Variable Description congress: session of Congress year: year of the election state: state of the election winner: name of the winner loser: name of the runner-up w.party: party of the winner l.party: party of the loser d.votes: number of votes for the Democratic candidate r.votes: number of votes for the Republican candidate d.comp: competence measure for the Democratic candidate r.comp: competence measure for the Republican candidate

   file 3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970 · frame —

   face_margin (reference)

   file 3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970 · frame —

   1. derive {"args":{"left":"d.votes","right":"r.votes"},"name":"d_minus_r","op":"diff"}
   2. derive {"args":{"columns":["d.votes","r.votes"]},"name":"two_party","op":"sum"}
   3. derive {"args":{"left":"d_minus_r","right":"two_party"},"name":"d_margin","op":"ratio"}
2. 1Transformação
   face_margin · derive {"args":{"left":"d.votes","right":"r.votes"},"name":"d_minus_r","op":"diff"}

   119 → 119 linhas

   Frame antes → depois: 34b60d8b2348 → d30e42c7976f
3. 2Transformação
   face_margin · derive {"args":{"columns":["d.votes","r.votes"]},"name":"two_party","op":"sum"}

   119 → 119 linhas

   Frame antes → depois: d30e42c7976f → 122bcc68db7b
4. 3Transformação
   face_margin · derive {"args":{"left":"d_minus_r","right":"two_party"},"name":"d_margin","op":"ratio"}

   119 → 119 linhas

   Frame antes → depois: 122bcc68db7b → 4e22178635b2
5. 4Turno do modelo
   call_0f86b602referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   OLS of the Democratic two-party margin on the Democratic candidate's perceived competence.

   Chamadas pedidas

   • infer.ols

     {
       "dataset": "face_margin",
       "x": [
         "d.comp"
       ],
       "y": "d_margin"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
6. 5Ferramenta
   infer.ols— Regressão linear por mínimos quadrados ordinários (MQO)v0.1.0call_736e4030decidido pelo modeloreexecutável

   Motivo: OLS of the Democratic two-party margin on the Democratic candidate's perceived competence.

   Argumentos

   {
     "cluster": null,
     "controls": [],
     "dataset": "face_margin",
     "fe": [],
     "intercept": true,
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "weights": null,
     "x": [
       "d.comp"
     ],
     "y": "d_margin"
   }

   Figuras call_736e4030

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.d.comp Coefficient on d.comp in the regression of d_margin	0,660381	0.6603814567765842	0,1251750.12517514568238694	0,412479 – 0,908284

   Diagnósticos

   • n_obs = 119119 (observations used)
   • k = 22 (parameters estimated, fixed-effect dummies included)
   • r2 = 0,1872940.18729363431996082
   • df_resid = 117117
   • se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))
   • n_clusters = — (errors not clustered)

   Derivação matemática e pressupostos

   coef.d.comp

   1. Coeficiente de d.comp na regressão por mínimos quadrados ordinários de d_margin.→ 0,660381 (0.6603814567765842)
   2. Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 0,125175 (0.12517514568238694)
   3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 117 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": -0.17645418569832133,
         "ci_low": -0.4479975648598276,
         "coef": -0.31222587527907447,
         "p": 0.00001297028928131714,
         "se": 0.06855606261484216,
         "t": -4.554314576570776,
         "term": "Intercept"
       },
       {
         "ci_high": 0.9082842734581125,
         "ci_low": 0.41247864009505597,
         "coef": 0.6603814567765842,
         "p": 6.152843299974919e-7,
         "se": 0.12517514568238694,
         "t": 5.2756595822321035,
         "term": "d.comp"
       }
     ]
   }

   Hashes SHA-256 e metadados de reprodução

   Digests dos dados

   face_margin 4e22178635b245e6c5ef97a118af1ff85fd434bfb578c46c3be31387e7657ee8

   sha256 do resultado

   36796479215316d5e697dcfbc84d5c50fc30704a437a9d72ebfb943a313f1b60

   Resultado mostrado ao modelo
7. 6Turno do modelo
   call_7cde8b0freferenceFim: 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.d.comp = 0,6604. Caminho de referência inferido à mão.",
       "caveats": [],
       "figure_refs": [
         "call_736e4030:coef.d.comp"
       ]
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
8. 7Guarda
   resposta aceita

   Números encontrados

   0,6604

   Sem lastro

   —

   Não citados

   —

   1 nova(s) tentativa(s) restante(s)
9. 8Resposta

   Estimativa (infer.ols): coef.d.comp = 0,6604. Caminho de referência inferido à mão.

   Figuras citadas: call_736e4030:coef.d.comp · Parada: answered

   Figura da resposta: call_736e4030:coef.d.comp = 0,6603810.6603814567765842 (se 0,1251750.12517514568238694)
10. 9Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
11. 10Pontuação
    #1figura da resposta próximapassouobrigatória0.66 in (0.6402, 0.6798)?

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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