Tarefa crb_papers.q026

Veredito

Pergunta feita ao analista

Among districts observed in 2008 and 2010, how did the share of general-election contributions from Asian donors change when a district went from having no Asian major-party nominee to having one, relative to districts with no Asian nominee in either election?

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
   ds_0014— | Variable Name | Description | | --- | --- | | cycle | Even-numbered year of the U.S. House general-election cycle (range: 1980–2012). Serves as the time index for the panel. | | district_final | Congressional district identifier (state postal abbreviation + district number, e.g., AK1 for at‑large). District codes are reindexed after each decennial redistricting to reflect new boundaries. | | general_sharetotal_A_all | Proportion (0–1) of all itemized individual dollars contributed to candidates in the district’s general-election contest that are attributed to donors classified as Asian. Donor ethnoracial identity is inferred with a surname–geography Bayesian method and aggregated to the district–cycle level. | | general_sharetotal_B_all | Proportion (0–1) of all itemized individual dollars contributed to candidates in the district’s general-election contest that are attributed to donors classified as Black. Donor ethnoracial identity is inferred with a surname–geography Bayesian method and aggregated to the district–cycle level. | | general_sharetotal_H_all | Proportion (0–1) of all itemized individual dollars contributed to candidates in the district’s general-election contest that are attributed to donors classified as Latino/Hispanic. Donor ethnoracial identity is inferred with a surname–geography Bayesian method and aggregated to the district–cycle level. | | cand_A_all | Indicator (1/0) that at least one major‑party nominee (Democratic or Republican) in the district’s general election is identified as Asian, based on verified public sources and candidate records. | | cand_B_all | Indicator (1/0) that at least one major‑party nominee in the district’s general election is identified as Black, based on verified public sources and candidate records. | | cand_H_all | Indicator (1/0) that at least one major‑party nominee in the district’s general election is identified as Latino/Hispanic, based on verified public sources and candidate records. | | dfe | Constant column equal to 1 for all rows, included for convenience in some analysis workflows; it carries no substantive information about districts or elections. | Dataset Context: This panel assembles district–election year information for U.S. House general elections from 1980 through 2012. Monetary figures come from the DIME compilation of Federal Election Commission disclosures and related sources, limited to itemized individual contributions reported under federal law (generally contributions exceeding the cycle-level reporting threshold). Dollars are aggregated to the general-election period for candidates competing in a given district and cycle. To characterize the ethnoracial composition of contributors, the data employ a widely used Bayesian surname–geography approach (implemented via the wru methodology). This procedure combines U.S. Census surname frequencies with donors’ geocoded locations (Census tract or, if unavailable, state) to estimate the probability that a donor belongs to an ethnoracial group; donors are then assigned to the most probable category, and contributions are summed to compute district-level shares for Asian, Black, and Latino/Hispanic contributors. These shares are fractions of total itemized individual dollars given to general-election candidates in the district; the remainder of contributions reflects donors in other categories. Candidate ethnoracial identity is coded from verified public records, including congressional caucus memberships and other documentation, and linked to each district’s major‑party general-election nominees. District identifiers reflect changes from decennial redistricting (e.g., around 1982, 1992, 2002, and 2012), so the same geographic area may appear under different labels across redistricting periods. Because federal disclosures only require itemization above the reporting threshold, very small donations are not included; all shares and totals therefore describe the universe of reported itemized individual contributions.
   ref_complete
   ref_panel
   ref_post
   ref_pre
   Transformação "select" aplicada à base "ref_pre" (7007 → 7007 linhas). {"columns":["cycle","district_final","cand_A_all","cand_B_all","cand_H_all","general_sharetotal_A_all"]}
   Transformação "filter" aplicada à base "ref_pre" (7007 → 401 linhas). {"query":"cycle == 2008"}
   Transformação "select" aplicada à base "ref_pre" (401 → 401 linhas). {"columns":["district_final","cand_A_all"]}
   Transformação "rename" aplicada à base "ref_pre" (401 → 401 linhas). {"map":{"cand_A_all":"a_pre"}}
   Transformação "select" aplicada à base "ref_post" (7007 → 7007 linhas). {"columns":["cycle","district_final","cand_A_all","cand_B_all","cand_H_all","general_sharetotal_A_all"]}
   Transformação "filter" aplicada à base "ref_post" (7007 → 410 linhas). {"query":"cycle == 2010"}
   Transformação "select" aplicada à base "ref_post" (410 → 410 linhas). {"columns":["district_final","cand_A_all"]}
   Transformação "rename" aplicada à base "ref_post" (410 → 410 linhas). {"map":{"cand_A_all":"a_post"}}
   Transformação "select" aplicada à base "ref_complete" (7007 → 7007 linhas). {"columns":["cycle","district_final","cand_A_all","cand_B_all","cand_H_all","general_sharetotal_A_all"]}
   Transformação "filter" aplicada à base "ref_complete" (7007 → 811 linhas). {"query":"cycle in [2008, 2010]"}
   Transformação "dropna" aplicada à base "ref_complete" (811 → 811 linhas). {"columns":["general_sharetotal_A_all"]}
   Transformação "collapse" aplicada à base "ref_complete" (811 → 811 linhas). {"aggs":[{"column":"general_sharetotal_A_all","fn":"count","name":"n"}],"by":["district_final","cycle"]}
   Transformação "collapse" aplicada à base "ref_complete" (811 → 410 linhas). {"aggs":[{"column":"n","fn":"count","name":"ncycles"}],"by":["district_final"]}
   Transformação "select" aplicada à base "ref_panel" (7007 → 7007 linhas). {"columns":["cycle","district_final","cand_A_all","cand_B_all","cand_H_all","general_sharetotal_A_all"]}
   Transformação "filter" aplicada à base "ref_panel" (7007 → 811 linhas). {"query":"cycle in [2008, 2010]"}
   Transformação "merge" aplicada à base "ref_panel" (811 → 802 linhas). {"how":"inner","on":["district_final"],"with":"ref_pre"}
   Transformação "merge" aplicada à base "ref_panel" (802 → 802 linhas). {"how":"inner","on":["district_final"],"with":"ref_post"}
   Transformação "filter" aplicada à base "ref_panel" (802 → 780 linhas). {"query":"a_pre == 0 and (a_post == 1 or a_post == 0)"}
   Transformação "merge" aplicada à base "ref_panel" (780 → 780 linhas). {"how":"left","on":["district_final"],"with":"ref_complete"}
   Transformação "filter" aplicada à base "ref_panel" (780 → 780 linhas). {"query":"ncycles == 2"}
   Transformação "derive" aplicada à base "ref_panel" (780 → 780 linhas). {"args":{"cmp":"eq","column":"a_post","value":1},"name":"Treated","op":"indicator"}
   Transformação "derive" aplicada à base "ref_panel" (780 → 780 linhas). {"args":{"cmp":"eq","column":"cycle","value":2010},"name":"Post","op":"indicator"}
   Transformação "derive" aplicada à base "ref_panel" (780 → 780 linhas). {"args":{"columns":["Treated","Post"]},"name":"Treated_Post","op":"interaction"}
2. 2Chamada que gerou a resposta finalinfer.ols(Regressão linear por mínimos quadrados ordinários (MQO))citada na respostacoincide com a referência

   Ver chamada call_d315cafd nos detalhes

   smf.ols('general_sharetotal_A_all ~ Treated + Post + Treated:Post + cand_B_all + cand_H_all').fit(cov_type='cluster', groups=district_final) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

   Por que este é o caminho de referência: smf.ols('general_sharetotal_A_all ~ Treated + Post + Treated:Post + cand_B_all + cand_H_all').fit(cov_type='cluster', groups=district_final) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)
   Argumentos principais

   cluster:

   "district_final"

   controls:

   ["Treated","Post","cand_B_all","cand_H_all"]

   dataset:

   "ref_panel"

   intercept:

   true

   level:

   0,950.95

   se:

   "HC1"

   x:

   ["Treated_Post"]

   y:

   "general_sharetotal_A_all"

   Resultado calculado

   • coef.Treated_Post = 0,112335 0.11233472802593727
     Coefficient on Treated_Post in the regression of general_sharetotal_A_all
   Diagnósticos: n_obs = 780780 (observations used) · k = 66 (parameters estimated, fixed-effect dummies included) · r2 = 0,1230010.12300087743349097 · df_resid = 774774 · se_type = "cluster" (cluster-robust standard errors (CR1) by 'district_final') · n_clusters = 390390 (390 clusters of 'district_final')
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: 0,1123
   Figuras citadas: call_d315cafd:coef.Treated_Post
   • ✓
     #0resposta com lastro

     every number traced to a tool call or the question
   • ✓
     #1figura da resposta próxima

     CRB estimate_within_solution_ci: the answer figure inside the gold 95% interval effect ± 1.96·|SE| of source crb_papers.q026.gold (closed, evaluate_result.py; no --auto-rescale)
   • ✓
     #2figura da resposta próxima

     replication cell: the gold effect to rel 1e-6 (abs floor 1e-6·SE); never required
   • ✓
     #3figura da resposta próxima

     replication cell: the gold SE to rel 1e-6; never required

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

   Among districts observed in 2008 and 2010, how did the share of general-election contributions from Asian donors change when a district went from having no Asian major-party nominee to having one, relative to districts with no Asian nominee in either election?

   ds_0014 (agent)

   | Variable Name | Description | | --- | --- | | cycle | Even-numbered year of the U.S. House general-election cycle (range: 1980–2012). Serves as the time index for the panel. | | district_final | Congressional district identifier (state postal abbreviation + district number, e.g., AK1 for at‑large). District codes are reindexed after each decennial redistricting to reflect new boundaries. | | general_sharetotal_A_all | Proportion (0–1) of all itemized individual dollars contributed to candidates in the district’s general-election contest that are attributed to donors classified as Asian. Donor ethnoracial identity is inferred with a surname–geography Bayesian method and aggregated to the district–cycle level. | | general_sharetotal_B_all | Proportion (0–1) of all itemized individual dollars contributed to candidates in the district’s general-election contest that are attributed to donors classified as Black. Donor ethnoracial identity is inferred with a surname–geography Bayesian method and aggregated to the district–cycle level. | | general_sharetotal_H_all | Proportion (0–1) of all itemized individual dollars contributed to candidates in the district’s general-election contest that are attributed to donors classified as Latino/Hispanic. Donor ethnoracial identity is inferred with a surname–geography Bayesian method and aggregated to the district–cycle level. | | cand_A_all | Indicator (1/0) that at least one major‑party nominee (Democratic or Republican) in the district’s general election is identified as Asian, based on verified public sources and candidate records. | | cand_B_all | Indicator (1/0) that at least one major‑party nominee in the district’s general election is identified as Black, based on verified public sources and candidate records. | | cand_H_all | Indicator (1/0) that at least one major‑party nominee in the district’s general election is identified as Latino/Hispanic, based on verified public sources and candidate records. | | dfe | Constant column equal to 1 for all rows, included for convenience in some analysis workflows; it carries no substantive information about districts or elections. | Dataset Context: This panel assembles district–election year information for U.S. House general elections from 1980 through 2012. Monetary figures come from the DIME compilation of Federal Election Commission disclosures and related sources, limited to itemized individual contributions reported under federal law (generally contributions exceeding the cycle-level reporting threshold). Dollars are aggregated to the general-election period for candidates competing in a given district and cycle. To characterize the ethnoracial composition of contributors, the data employ a widely used Bayesian surname–geography approach (implemented via the wru methodology). This procedure combines U.S. Census surname frequencies with donors’ geocoded locations (Census tract or, if unavailable, state) to estimate the probability that a donor belongs to an ethnoracial group; donors are then assigned to the most probable category, and contributions are summed to compute district-level shares for Asian, Black, and Latino/Hispanic contributors. These shares are fractions of total itemized individual dollars given to general-election candidates in the district; the remainder of contributions reflects donors in other categories. Candidate ethnoracial identity is coded from verified public records, including congressional caucus memberships and other documentation, and linked to each district’s major‑party general-election nominees. District identifiers reflect changes from decennial redistricting (e.g., around 1982, 1992, 2002, and 2012), so the same geographic area may appear under different labels across redistricting periods. Because federal disclosures only require itemization above the reporting threshold, very small donations are not included; all shares and totals therefore describe the universe of reported itemized individual contributions.

   file a9a7b57b90e4f8ca919421bfb08da2403ef1dd534ec517c0236f12c81f504038 · frame —

   ref_complete (reference)

   file a9a7b57b90e4f8ca919421bfb08da2403ef1dd534ec517c0236f12c81f504038 · frame —

   1. select {"columns":["cycle","district_final","cand_A_all","cand_B_all","cand_H_all","general_sharetotal_A_all"]}
   2. filter {"query":"cycle in [2008, 2010]"}
   3. dropna {"columns":["general_sharetotal_A_all"]}
   4. collapse {"aggs":[{"column":"general_sharetotal_A_all","fn":"count","name":"n"}],"by":["district_final","cycle"]}
   5. collapse {"aggs":[{"column":"n","fn":"count","name":"ncycles"}],"by":["district_final"]}

   ref_panel (reference)

   file a9a7b57b90e4f8ca919421bfb08da2403ef1dd534ec517c0236f12c81f504038 · frame —

   1. select {"columns":["cycle","district_final","cand_A_all","cand_B_all","cand_H_all","general_sharetotal_A_all"]}
   2. filter {"query":"cycle in [2008, 2010]"}
   3. merge {"how":"inner","on":["district_final"],"with":"ref_pre"}
   4. merge {"how":"inner","on":["district_final"],"with":"ref_post"}
   5. filter {"query":"a_pre == 0 and (a_post == 1 or a_post == 0)"}
   6. merge {"how":"left","on":["district_final"],"with":"ref_complete"}
   7. filter {"query":"ncycles == 2"}
   8. derive {"args":{"cmp":"eq","column":"a_post","value":1},"name":"Treated","op":"indicator"}
   9. derive {"args":{"cmp":"eq","column":"cycle","value":2010},"name":"Post","op":"indicator"}
   10. derive {"args":{"columns":["Treated","Post"]},"name":"Treated_Post","op":"interaction"}

   ref_post (reference)

   file a9a7b57b90e4f8ca919421bfb08da2403ef1dd534ec517c0236f12c81f504038 · frame —

   1. select {"columns":["cycle","district_final","cand_A_all","cand_B_all","cand_H_all","general_sharetotal_A_all"]}
   2. filter {"query":"cycle == 2010"}
   3. select {"columns":["district_final","cand_A_all"]}
   4. rename {"map":{"cand_A_all":"a_post"}}

   ref_pre (reference)

   file a9a7b57b90e4f8ca919421bfb08da2403ef1dd534ec517c0236f12c81f504038 · frame —

   1. select {"columns":["cycle","district_final","cand_A_all","cand_B_all","cand_H_all","general_sharetotal_A_all"]}
   2. filter {"query":"cycle == 2008"}
   3. select {"columns":["district_final","cand_A_all"]}
   4. rename {"map":{"cand_A_all":"a_pre"}}
2. 1Transformação
   ref_pre · select {"columns":["cycle","district_final","cand_A_all","cand_B_all","cand_H_all","general_sharetotal_A_all"]}

   7007 → 7007 linhas

   Frame antes → depois: 3acd08e209f7 → 72f6007995a7
3. 2Transformação
   ref_pre · filter {"query":"cycle == 2008"}

   7007 → 401 linhas

   Frame antes → depois: 72f6007995a7 → f3d7fbac1b2e
4. 3Transformação
   ref_pre · select {"columns":["district_final","cand_A_all"]}

   401 → 401 linhas

   Frame antes → depois: f3d7fbac1b2e → 59de7e69fc23
5. 4Transformação
   ref_pre · rename {"map":{"cand_A_all":"a_pre"}}

   401 → 401 linhas

   Frame antes → depois: 59de7e69fc23 → d503209b32c0
6. 5Transformação
   ref_post · select {"columns":["cycle","district_final","cand_A_all","cand_B_all","cand_H_all","general_sharetotal_A_all"]}

   7007 → 7007 linhas

   Frame antes → depois: 3acd08e209f7 → 72f6007995a7
7. 6Transformação
   ref_post · filter {"query":"cycle == 2010"}

   7007 → 410 linhas

   Frame antes → depois: 72f6007995a7 → 7ffad156d3e7
8. 7Transformação
   ref_post · select {"columns":["district_final","cand_A_all"]}

   410 → 410 linhas

   Frame antes → depois: 7ffad156d3e7 → f15e06cc19a8
9. 8Transformação
   ref_post · rename {"map":{"cand_A_all":"a_post"}}

   410 → 410 linhas

   Frame antes → depois: f15e06cc19a8 → 61fb64a23ffc
10. 9Transformação
    ref_complete · select {"columns":["cycle","district_final","cand_A_all","cand_B_all","cand_H_all","general_sharetotal_A_all"]}

    7007 → 7007 linhas

    Frame antes → depois: 3acd08e209f7 → 72f6007995a7
11. 10Transformação
    ref_complete · filter {"query":"cycle in [2008, 2010]"}

    7007 → 811 linhas

    Frame antes → depois: 72f6007995a7 → 1e8d4987bfbc
12. 11Transformação
    ref_complete · dropna {"columns":["general_sharetotal_A_all"]}

    811 → 811 linhas

    Frame antes → depois: 1e8d4987bfbc → 1e8d4987bfbc
13. 12Transformação
    ref_complete · collapse {"aggs":[{"column":"general_sharetotal_A_all","fn":"count","name":"n"}],"by":["district_final","cycle"]}

    811 → 811 linhas

    Frame antes → depois: 1e8d4987bfbc → f2f6ad995d72
14. 13Transformação
    ref_complete · collapse {"aggs":[{"column":"n","fn":"count","name":"ncycles"}],"by":["district_final"]}

    811 → 410 linhas

    Frame antes → depois: f2f6ad995d72 → d9ec5c2cdc44
15. 14Transformação
    ref_panel · select {"columns":["cycle","district_final","cand_A_all","cand_B_all","cand_H_all","general_sharetotal_A_all"]}

    7007 → 7007 linhas

    Frame antes → depois: 3acd08e209f7 → 72f6007995a7
16. 15Transformação
    ref_panel · filter {"query":"cycle in [2008, 2010]"}

    7007 → 811 linhas

    Frame antes → depois: 72f6007995a7 → 1e8d4987bfbc
17. 16Transformação
    ref_panel · merge {"how":"inner","on":["district_final"],"with":"ref_pre"}

    811 → 802 linhas

    Frame antes → depois: 1e8d4987bfbc → 6a1f0fad5c38
18. 17Transformação
    ref_panel · merge {"how":"inner","on":["district_final"],"with":"ref_post"}

    802 → 802 linhas

    Frame antes → depois: 6a1f0fad5c38 → bfab9c5e5d01
19. 18Transformação
    ref_panel · filter {"query":"a_pre == 0 and (a_post == 1 or a_post == 0)"}

    802 → 780 linhas

    Frame antes → depois: bfab9c5e5d01 → 0df786906d5f
20. 19Transformação
    ref_panel · merge {"how":"left","on":["district_final"],"with":"ref_complete"}

    780 → 780 linhas

    Frame antes → depois: 0df786906d5f → 0af98e2430de
21. 20Transformação
    ref_panel · filter {"query":"ncycles == 2"}

    780 → 780 linhas

    Frame antes → depois: 0af98e2430de → 0af98e2430de
22. 21Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"a_post","value":1},"name":"Treated","op":"indicator"}

    780 → 780 linhas

    Frame antes → depois: 0af98e2430de → e77240c5ccf8
23. 22Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"cycle","value":2010},"name":"Post","op":"indicator"}

    780 → 780 linhas

    Frame antes → depois: e77240c5ccf8 → f15c1006b37b
24. 23Transformação
    ref_panel · derive {"args":{"columns":["Treated","Post"]},"name":"Treated_Post","op":"interaction"}

    780 → 780 linhas

    Frame antes → depois: f15c1006b37b → e3509e6f9e62
25. 24Turno do modelo
    call_719e94f8referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

    Texto visível

    smf.ols('general_sharetotal_A_all ~ Treated + Post + Treated:Post + cand_B_all + cand_H_all').fit(cov_type='cluster', groups=district_final) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

    Chamadas pedidas

    • infer.ols

      {
        "cluster": "district_final",
        "controls": [
          "Treated",
          "Post",
          "cand_B_all",
          "cand_H_all"
        ],
        "dataset": "ref_panel",
        "x": [
          "Treated_Post"
        ],
        "y": "general_sharetotal_A_all"
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
26. 25Ferramenta
    infer.ols— Regressão linear por mínimos quadrados ordinários (MQO)v0.1.0call_d315cafddecidido pelo modeloreexecutável

    Motivo: smf.ols('general_sharetotal_A_all ~ Treated + Post + Treated:Post + cand_B_all + cand_H_all').fit(cov_type='cluster', groups=district_final) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

    Argumentos

    {
      "cluster": "district_final",
      "controls": [
        "Treated",
        "Post",
        "cand_B_all",
        "cand_H_all"
      ],
      "dataset": "ref_panel",
      "fe": [],
      "intercept": true,
      "level": 0.95,
      "se": "HC1",
      "subset": null,
      "weights": null,
      "x": [
        "Treated_Post"
      ],
      "y": "general_sharetotal_A_all"
    }

    Figuras call_d315cafd

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.Treated_Post Coefficient on Treated_Post in the regression of general_sharetotal_A_all	0,112335	0.11233472802593727	0,05681510.056815119159954824	0,000631598 – 0,224038

    Diagnósticos

    • n_obs = 780780 (observations used)
    • k = 66 (parameters estimated, fixed-effect dummies included)
    • r2 = 0,1230010.12300087743349097
    • df_resid = 774774
    • se_type = "cluster" (cluster-robust standard errors (CR1) by 'district_final')
    • n_clusters = 390390 (390 clusters of 'district_final')

    Derivação matemática e pressupostos

    coef.Treated_Post

    1. Coeficiente de Treated_Post na regressão por mínimos quadrados ordinários de general_sharetotal_A_all.→ 0,112335 (0.11233472802593727)
    2. Erro-padrão robusto por conglomerado (CR1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 0,0568151 (0.056815119159954824)
    3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 389 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 clusters of 'district_final' and may be correlated within them

    Tabelas (1)

    {
      "coefficients": [
        {
          "ci_high": 0.02282008994256342,
          "ci_low": 0.01430759483158921,
          "coef": 0.018563842387076316,
          "p": 2.372379682031364e-16,
          "se": 0.002164838283283516,
          "t": 8.575163572458461,
          "term": "Intercept"
        },
        {
          "ci_high": 0.22403785795788178,
          "ci_low": 0.000631598093992769,
          "coef": 0.11233472802593727,
          "p": 0.048724897903661704,
          "se": 0.056815119159954824,
          "t": 1.9771977897234527,
          "term": "Treated_Post"
        },
        {
          "ci_high": 0.07542077392110502,
          "ci_low": -0.008979031870842653,
          "coef": 0.03322087102513118,
          "p": 0.1224954910352167,
          "se": 0.021463968941908943,
          "t": 1.5477506101057847,
          "term": "Treated"
        },
        {
          "ci_high": 0.004833150551370573,
          "ci_low": -0.0031543426952724313,
          "coef": 0.0008394039280490709,
          "p": 0.6796657569541018,
          "se": 0.002031323477121197,
          "t": 0.4132300628153419,
          "term": "Post"
        },
        {
          "ci_high": 0.04051150917474073,
          "ci_low": 0.006388097096198329,
          "coef": 0.02344980313546953,
          "p": 0.007189744455657686,
          "se": 0.008678027753420173,
          "t": 2.7022042106546067,
          "term": "cand_B_all"
        },
        {
          "ci_high": 0.008616702067460458,
          "ci_low": -0.006644134385338271,
          "coef": 0.0009862838410610937,
          "p": 0.7995298271711794,
          "se": 0.0038810293054214007,
          "t": 0.25412944954663347,
          "term": "cand_H_all"
        }
      ]
    }

    Hashes SHA-256 e metadados de reprodução

    Digests dos dados

    ref_panel e3509e6f9e621ba38c04da17f4ea99a2e7844bc520de635a51be3041a0e0258e

    sha256 do resultado

    48408f241d68e1a3c6afabee27613be8291d8c4a54b10358a985c5a2591d484a

    Resultado mostrado ao modelo
27. 26Turno do modelo
    call_6fad468areferenceFim: 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.Treated_Post = 0,1123. Reference path transcribed from the item's own estimation script.",
        "caveats": [],
        "figure_refs": [
          "call_d315cafd:coef.Treated_Post"
        ]
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
28. 27Guarda
    resposta aceita

    Números encontrados

    0,1123

    Sem lastro

    —

    Não citados

    —

    1 nova(s) tentativa(s) restante(s)
29. 28Resposta

    Estimativa (infer.ols): coef.Treated_Post = 0,1123. Reference path transcribed from the item's own estimation script.

    Figuras citadas: call_d315cafd:coef.Treated_Post · Parada: answered

    Figura da resposta: call_d315cafd:coef.Treated_Post = 0,1123350.11233472802593727 (se 0,05681510.056815119159954824)
30. 29Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
31. 30Pontuação
    #1figura da resposta próximapassouobrigatória0.112334728 in [0.0009770944724, 0.2236923616]?
32. 31Pontuação
    #2figura da resposta próximapassouregistrada0.112334728 vs 0.11233472802593567
33. 32Pontuação
    #3figura da resposta próximapassouregistrada0.05681511916 vs 0.05681511915995378

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