Tarefa crb_papers.q029

Veredito

Pergunta feita ao analista

For municipalities that first moved to council-based naturalization decisions in 2006 or 2009, how much did ordinary naturalization rates change from 1991 to 2009 relative to municipalities that never changed their local decision regime?

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_0015— | Variable Name | Description | | --- | --- | | bfs | Swiss Federal Statistical Office (BFS) municipality code: a stable numeric identifier for each municipality in the panel. | | year | Calendar year of the observation (1991–2009). Institutional status is referenced as of January 1 of the given year, and annual rates are computed for the same year. | | nat_rate_ord | Ordinary naturalization rate (percent). Computed as the number of ordinary naturalizations recorded during the year divided by the stock of foreign residents who met statutory eligibility at the start of the year in the same municipality, multiplied by 100. Ordinary naturalizations refer to the standard pathway (as opposed to federally handled “facilitated” cases). | | indirect | Local decision procedure in place on January 1: 1 if applications were decided by the elected municipal council (representative/indirect democracy); 0 if applications were decided by citizens in a local vote (direct democracy). | Dataset Context: This dataset is an annual municipality–year panel for Switzerland that spans a period of institutional change in how local authorities decide on citizenship applications. Switzerland’s “triple citizenship” system assigns a central role to municipalities in evaluating and deciding ordinary naturalization requests. During the 1990s and 2000s, municipalities used different decision procedures: in some, citizens voted on applications (often at a citizens’ assembly and, historically in a few places, by ballot); in others, elected municipal councils rendered the decisions. A series of Swiss Federal Court rulings in 2003–2005 curtailed secret-ballot referendums for naturalizations and required municipalities to provide formal justifications for rejections. Several cantons subsequently updated their laws, and many municipalities revised or formalized their procedures, frequently shifting decision authority to municipal councils. The indicator in this file captures, for each municipality and year, which procedure was in force as of January 1. The panel combines two types of information. First, administrative registers from the Swiss Federal Statistical Office (PETRA: Foreign Population Structure and Migration Statistics) are used to derive annual ordinary naturalization rates. These rates are constructed as ordinary naturalizations completed within a year divided by the number of foreign residents who met the legal eligibility criteria at the start of that year, providing a comparable percentage measure across municipalities and over time. Second, the coding of local decision procedures relies on a nationwide survey of municipal head secretaries and systematic compilation of cantonal regulations and court decisions. The measurement convention aligns the institutional status to January 1 to enhance comparability across municipalities, recognizing that formal switches may occur during a calendar year. Ordinary naturalizations represent the standard route to citizenship and, in the study period, typically occurred after lengthy processing that could extend several years from initial application to final local vote. Facilitated naturalizations—handled centrally under federal law (for example, for certain spouses of Swiss citizens)—are not included in the numerator of the ordinary naturalization rate. The resulting dataset provides a consistent view of local institutions and annual naturalization activity across more than a decade and a half, covering the pre- and post-reform environment in which many municipalities updated their decision-making arrangements.
   ref_first
   ref_max
   ref_panel
   ref_years
   Transformação "select" aplicada à base "ref_first" (22971 → 22971 linhas). {"columns":["bfs","year","nat_rate_ord","indirect"]}
   Transformação "dropna" aplicada à base "ref_first" (22971 → 22971 linhas). {"columns":["bfs","year","nat_rate_ord"]}
   Transformação "filter" aplicada à base "ref_first" (22971 → 7836 linhas). {"query":"indirect == 1"}
   Transformação "collapse" aplicada à base "ref_first" (7836 → 797 linhas). {"aggs":[{"column":"year","fn":"min","name":"sw"}],"by":["bfs"]}
   Transformação "select" aplicada à base "ref_years" (22971 → 22971 linhas). {"columns":["bfs","year","nat_rate_ord","indirect"]}
   Transformação "dropna" aplicada à base "ref_years" (22971 → 22971 linhas). {"columns":["bfs","year","nat_rate_ord"]}
   Transformação "filter" aplicada à base "ref_years" (22971 → 2418 linhas). {"query":"year in [1991, 2009]"}
   Transformação "collapse" aplicada à base "ref_years" (2418 → 2418 linhas). {"aggs":[{"column":"nat_rate_ord","fn":"count","name":"n"}],"by":["bfs","year"]}
   Transformação "collapse" aplicada à base "ref_years" (2418 → 1209 linhas). {"aggs":[{"column":"n","fn":"count","name":"nyears"}],"by":["bfs"]}
   Transformação "select" aplicada à base "ref_max" (22971 → 22971 linhas). {"columns":["bfs","year","nat_rate_ord","indirect"]}
   Transformação "dropna" aplicada à base "ref_max" (22971 → 22971 linhas). {"columns":["bfs","year","nat_rate_ord"]}
   Transformação "collapse" aplicada à base "ref_max" (22971 → 1209 linhas). {"aggs":[{"column":"indirect","fn":"max","name":"imax"}],"by":["bfs"]}
   Transformação "select" aplicada à base "ref_panel" (22971 → 22971 linhas). {"columns":["bfs","year","nat_rate_ord","indirect"]}
   Transformação "dropna" aplicada à base "ref_panel" (22971 → 22971 linhas). {"columns":["bfs","year","nat_rate_ord"]}
   Transformação "filter" aplicada à base "ref_panel" (22971 → 2418 linhas). {"query":"year in [1991, 2009]"}
   Transformação "merge" aplicada à base "ref_panel" (2418 → 2418 linhas). {"how":"left","on":["bfs"],"with":"ref_first"}
   Transformação "merge" aplicada à base "ref_panel" (2418 → 2418 linhas). {"how":"left","on":["bfs"],"with":"ref_years"}
   Transformação "merge" aplicada à base "ref_panel" (2418 → 2418 linhas). {"how":"left","on":["bfs"],"with":"ref_max"}
   Transformação "filter" aplicada à base "ref_panel" (2418 → 1378 linhas). {"query":"nyears == 2 and (sw in [2006, 2009] or imax == 0)"}
   Transformação "derive" aplicada à base "ref_panel" (1378 → 1378 linhas). {"args":{"cmp":"in","column":"sw","value":[2006,2009]},"name":"group_treated","op":"indicator"}
   Transformação "derive" aplicada à base "ref_panel" (1378 → 1378 linhas). {"args":{"column":"group_treated","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"group_treated","op":"map"}
   Transformação "derive" aplicada à base "ref_panel" (1378 → 1378 linhas). {"args":{"cmp":"eq","column":"year","value":2009},"name":"post","op":"indicator"}
   Transformação "derive" aplicada à base "ref_panel" (1378 → 1378 linhas). {"args":{"columns":["group_treated","post"]},"name":"g_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_a45fb98d nos detalhes

   sm.OLS(y, add_constant([group_treated, post, g_post])).fit(cov_type='cluster', groups=bfs) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

   Por que este é o caminho de referência: sm.OLS(y, add_constant([group_treated, post, g_post])).fit(cov_type='cluster', groups=bfs) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)
   Argumentos principais

   cluster:

   "bfs"

   controls:

   ["group_treated","post"]

   dataset:

   "ref_panel"

   intercept:

   true

   level:

   0,950.95

   se:

   "HC1"

   x:

   ["g_post"]

   y:

   "nat_rate_ord"

   Resultado calculado

   • coef.g_post = 0,815272 0.8152716551072007
     Coefficient on g_post in the regression of nat_rate_ord
   Diagnósticos: n_obs = 1.3781378 (observations used) · k = 44 (parameters estimated, fixed-effect dummies included) · r2 = 0,0681330.0681330291116381 · df_resid = 1.3741374 · se_type = "cluster" (cluster-robust standard errors (CR1) by 'bfs') · n_clusters = 689689 (689 clusters of 'bfs')
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: 0,8153
   Figuras citadas: call_a45fb98d:coef.g_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.q029.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

   For municipalities that first moved to council-based naturalization decisions in 2006 or 2009, how much did ordinary naturalization rates change from 1991 to 2009 relative to municipalities that never changed their local decision regime?

   ds_0015 (agent)

   | Variable Name | Description | | --- | --- | | bfs | Swiss Federal Statistical Office (BFS) municipality code: a stable numeric identifier for each municipality in the panel. | | year | Calendar year of the observation (1991–2009). Institutional status is referenced as of January 1 of the given year, and annual rates are computed for the same year. | | nat_rate_ord | Ordinary naturalization rate (percent). Computed as the number of ordinary naturalizations recorded during the year divided by the stock of foreign residents who met statutory eligibility at the start of the year in the same municipality, multiplied by 100. Ordinary naturalizations refer to the standard pathway (as opposed to federally handled “facilitated” cases). | | indirect | Local decision procedure in place on January 1: 1 if applications were decided by the elected municipal council (representative/indirect democracy); 0 if applications were decided by citizens in a local vote (direct democracy). | Dataset Context: This dataset is an annual municipality–year panel for Switzerland that spans a period of institutional change in how local authorities decide on citizenship applications. Switzerland’s “triple citizenship” system assigns a central role to municipalities in evaluating and deciding ordinary naturalization requests. During the 1990s and 2000s, municipalities used different decision procedures: in some, citizens voted on applications (often at a citizens’ assembly and, historically in a few places, by ballot); in others, elected municipal councils rendered the decisions. A series of Swiss Federal Court rulings in 2003–2005 curtailed secret-ballot referendums for naturalizations and required municipalities to provide formal justifications for rejections. Several cantons subsequently updated their laws, and many municipalities revised or formalized their procedures, frequently shifting decision authority to municipal councils. The indicator in this file captures, for each municipality and year, which procedure was in force as of January 1. The panel combines two types of information. First, administrative registers from the Swiss Federal Statistical Office (PETRA: Foreign Population Structure and Migration Statistics) are used to derive annual ordinary naturalization rates. These rates are constructed as ordinary naturalizations completed within a year divided by the number of foreign residents who met the legal eligibility criteria at the start of that year, providing a comparable percentage measure across municipalities and over time. Second, the coding of local decision procedures relies on a nationwide survey of municipal head secretaries and systematic compilation of cantonal regulations and court decisions. The measurement convention aligns the institutional status to January 1 to enhance comparability across municipalities, recognizing that formal switches may occur during a calendar year. Ordinary naturalizations represent the standard route to citizenship and, in the study period, typically occurred after lengthy processing that could extend several years from initial application to final local vote. Facilitated naturalizations—handled centrally under federal law (for example, for certain spouses of Swiss citizens)—are not included in the numerator of the ordinary naturalization rate. The resulting dataset provides a consistent view of local institutions and annual naturalization activity across more than a decade and a half, covering the pre- and post-reform environment in which many municipalities updated their decision-making arrangements.

   file e9ce75e30cb08926da17f27cb91ecb80f964f077f9a592e052d672b61239e8f9 · frame —

   ref_first (reference)

   file e9ce75e30cb08926da17f27cb91ecb80f964f077f9a592e052d672b61239e8f9 · frame —

   1. select {"columns":["bfs","year","nat_rate_ord","indirect"]}
   2. dropna {"columns":["bfs","year","nat_rate_ord"]}
   3. filter {"query":"indirect == 1"}
   4. collapse {"aggs":[{"column":"year","fn":"min","name":"sw"}],"by":["bfs"]}

   ref_max (reference)

   file e9ce75e30cb08926da17f27cb91ecb80f964f077f9a592e052d672b61239e8f9 · frame —

   1. select {"columns":["bfs","year","nat_rate_ord","indirect"]}
   2. dropna {"columns":["bfs","year","nat_rate_ord"]}
   3. collapse {"aggs":[{"column":"indirect","fn":"max","name":"imax"}],"by":["bfs"]}

   ref_panel (reference)

   file e9ce75e30cb08926da17f27cb91ecb80f964f077f9a592e052d672b61239e8f9 · frame —

   1. select {"columns":["bfs","year","nat_rate_ord","indirect"]}
   2. dropna {"columns":["bfs","year","nat_rate_ord"]}
   3. filter {"query":"year in [1991, 2009]"}
   4. merge {"how":"left","on":["bfs"],"with":"ref_first"}
   5. merge {"how":"left","on":["bfs"],"with":"ref_years"}
   6. merge {"how":"left","on":["bfs"],"with":"ref_max"}
   7. filter {"query":"nyears == 2 and (sw in [2006, 2009] or imax == 0)"}
   8. derive {"args":{"cmp":"in","column":"sw","value":[2006,2009]},"name":"group_treated","op":"indicator"}
   9. derive {"args":{"column":"group_treated","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"group_treated","op":"map"}
   10. derive {"args":{"cmp":"eq","column":"year","value":2009},"name":"post","op":"indicator"}
   11. derive {"args":{"columns":["group_treated","post"]},"name":"g_post","op":"interaction"}

   ref_years (reference)

   file e9ce75e30cb08926da17f27cb91ecb80f964f077f9a592e052d672b61239e8f9 · frame —

   1. select {"columns":["bfs","year","nat_rate_ord","indirect"]}
   2. dropna {"columns":["bfs","year","nat_rate_ord"]}
   3. filter {"query":"year in [1991, 2009]"}
   4. collapse {"aggs":[{"column":"nat_rate_ord","fn":"count","name":"n"}],"by":["bfs","year"]}
   5. collapse {"aggs":[{"column":"n","fn":"count","name":"nyears"}],"by":["bfs"]}
2. 1Transformação
   ref_first · select {"columns":["bfs","year","nat_rate_ord","indirect"]}

   22971 → 22971 linhas

   Frame antes → depois: b496d06ec0c7 → b496d06ec0c7
3. 2Transformação
   ref_first · dropna {"columns":["bfs","year","nat_rate_ord"]}

   22971 → 22971 linhas

   Frame antes → depois: b496d06ec0c7 → b496d06ec0c7
4. 3Transformação
   ref_first · filter {"query":"indirect == 1"}

   22971 → 7836 linhas

   Frame antes → depois: b496d06ec0c7 → 8ab632303c72
5. 4Transformação
   ref_first · collapse {"aggs":[{"column":"year","fn":"min","name":"sw"}],"by":["bfs"]}

   7836 → 797 linhas

   Frame antes → depois: 8ab632303c72 → 8781603036ea
6. 5Transformação
   ref_years · select {"columns":["bfs","year","nat_rate_ord","indirect"]}

   22971 → 22971 linhas

   Frame antes → depois: b496d06ec0c7 → b496d06ec0c7
7. 6Transformação
   ref_years · dropna {"columns":["bfs","year","nat_rate_ord"]}

   22971 → 22971 linhas

   Frame antes → depois: b496d06ec0c7 → b496d06ec0c7
8. 7Transformação
   ref_years · filter {"query":"year in [1991, 2009]"}

   22971 → 2418 linhas

   Frame antes → depois: b496d06ec0c7 → a774e1115874
9. 8Transformação
   ref_years · collapse {"aggs":[{"column":"nat_rate_ord","fn":"count","name":"n"}],"by":["bfs","year"]}

   2418 → 2418 linhas

   Frame antes → depois: a774e1115874 → 88999a19f8f6
10. 9Transformação
    ref_years · collapse {"aggs":[{"column":"n","fn":"count","name":"nyears"}],"by":["bfs"]}

    2418 → 1209 linhas

    Frame antes → depois: 88999a19f8f6 → fe46190a8274
11. 10Transformação
    ref_max · select {"columns":["bfs","year","nat_rate_ord","indirect"]}

    22971 → 22971 linhas

    Frame antes → depois: b496d06ec0c7 → b496d06ec0c7
12. 11Transformação
    ref_max · dropna {"columns":["bfs","year","nat_rate_ord"]}

    22971 → 22971 linhas

    Frame antes → depois: b496d06ec0c7 → b496d06ec0c7
13. 12Transformação
    ref_max · collapse {"aggs":[{"column":"indirect","fn":"max","name":"imax"}],"by":["bfs"]}

    22971 → 1209 linhas

    Frame antes → depois: b496d06ec0c7 → 76a79e73bcae
14. 13Transformação
    ref_panel · select {"columns":["bfs","year","nat_rate_ord","indirect"]}

    22971 → 22971 linhas

    Frame antes → depois: b496d06ec0c7 → b496d06ec0c7
15. 14Transformação
    ref_panel · dropna {"columns":["bfs","year","nat_rate_ord"]}

    22971 → 22971 linhas

    Frame antes → depois: b496d06ec0c7 → b496d06ec0c7
16. 15Transformação
    ref_panel · filter {"query":"year in [1991, 2009]"}

    22971 → 2418 linhas

    Frame antes → depois: b496d06ec0c7 → a774e1115874
17. 16Transformação
    ref_panel · merge {"how":"left","on":["bfs"],"with":"ref_first"}

    2418 → 2418 linhas

    Frame antes → depois: a774e1115874 → fc5a995535da
18. 17Transformação
    ref_panel · merge {"how":"left","on":["bfs"],"with":"ref_years"}

    2418 → 2418 linhas

    Frame antes → depois: fc5a995535da → 239e1bc4971d
19. 18Transformação
    ref_panel · merge {"how":"left","on":["bfs"],"with":"ref_max"}

    2418 → 2418 linhas

    Frame antes → depois: 239e1bc4971d → 0dd836e8cd04
20. 19Transformação
    ref_panel · filter {"query":"nyears == 2 and (sw in [2006, 2009] or imax == 0)"}

    2418 → 1378 linhas

    Frame antes → depois: 0dd836e8cd04 → bd5360b17bf5
21. 20Transformação
    ref_panel · derive {"args":{"cmp":"in","column":"sw","value":[2006,2009]},"name":"group_treated","op":"indicator"}

    1378 → 1378 linhas

    Frame antes → depois: bd5360b17bf5 → 60d8f2cb9601
22. 21Transformação
    ref_panel · derive {"args":{"column":"group_treated","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"group_treated","op":"map"}

    1378 → 1378 linhas

    Frame antes → depois: 60d8f2cb9601 → 25f6ca4b1040
23. 22Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"year","value":2009},"name":"post","op":"indicator"}

    1378 → 1378 linhas

    Frame antes → depois: 25f6ca4b1040 → 9b4f2f375f0b
24. 23Transformação
    ref_panel · derive {"args":{"columns":["group_treated","post"]},"name":"g_post","op":"interaction"}

    1378 → 1378 linhas

    Frame antes → depois: 9b4f2f375f0b → ebdf76679911
25. 24Turno do modelo
    call_fb0107c7referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

    Texto visível

    sm.OLS(y, add_constant([group_treated, post, g_post])).fit(cov_type='cluster', groups=bfs) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

    Chamadas pedidas

    • infer.ols

      {
        "cluster": "bfs",
        "controls": [
          "group_treated",
          "post"
        ],
        "dataset": "ref_panel",
        "x": [
          "g_post"
        ],
        "y": "nat_rate_ord"
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
26. 25Ferramenta
    infer.ols— Regressão linear por mínimos quadrados ordinários (MQO)v0.1.0call_a45fb98ddecidido pelo modeloreexecutável

    Motivo: sm.OLS(y, add_constant([group_treated, post, g_post])).fit(cov_type='cluster', groups=bfs) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

    Argumentos

    {
      "cluster": "bfs",
      "controls": [
        "group_treated",
        "post"
      ],
      "dataset": "ref_panel",
      "fe": [],
      "intercept": true,
      "level": 0.95,
      "se": "HC1",
      "subset": null,
      "weights": null,
      "x": [
        "g_post"
      ],
      "y": "nat_rate_ord"
    }

    Figuras call_a45fb98d

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.g_post Coefficient on g_post in the regression of nat_rate_ord	0,815272	0.8152716551072007	0,4639210.4639206965118652	-0,0955986 – 1,72614

    Diagnósticos

    • n_obs = 1.3781378 (observations used)
    • k = 44 (parameters estimated, fixed-effect dummies included)
    • r2 = 0,0681330.0681330291116381
    • df_resid = 1.3741374
    • se_type = "cluster" (cluster-robust standard errors (CR1) by 'bfs')
    • n_clusters = 689689 (689 clusters of 'bfs')

    Derivação matemática e pressupostos

    coef.g_post

    1. Coeficiente de g_post na regressão por mínimos quadrados ordinários de nat_rate_ord.→ 0,815272 (0.8152716551072007)
    2. Erro-padrão robusto por conglomerado (CR1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 0,463921 (0.4639206965118652)
    3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 688 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 'bfs' and may be correlated within them

    Tabelas (1)

    {
      "coefficients": [
        {
          "ci_high": 1.2462499871036394,
          "ci_low": 0.6290205627191401,
          "coef": 0.9376352749113898,
          "p": 3.906749099702052e-9,
          "se": 0.1571823770741144,
          "t": 5.96526972275828,
          "term": "Intercept"
        },
        {
          "ci_high": 1.7261419123150346,
          "ci_low": -0.0955986021006332,
          "coef": 0.8152716551072007,
          "p": 0.07930267515443727,
          "se": 0.4639206965118652,
          "t": 1.7573513344782397,
          "term": "g_post"
        },
        {
          "ci_high": 0.27482868552125517,
          "ci_low": -0.754355212649299,
          "coef": -0.2397632635640219,
          "p": 0.36061020028727475,
          "se": 0.26208985698011306,
          "t": -0.9148132107310614,
          "term": "group_treated"
        },
        {
          "ci_high": 2.321744077839199,
          "ci_low": 1.3268314754153963,
          "coef": 1.8242877766272976,
          "p": 1.5788364395652995e-12,
          "se": 0.25336239921794285,
          "t": 7.200309841785329,
          "term": "post"
        }
      ]
    }

    Hashes SHA-256 e metadados de reprodução

    Digests dos dados

    ref_panel ebdf76679911c2cbbd78a3a2b0a2e948b8028561e2f2ecc89ccea5a1e6b3de48

    sha256 do resultado

    16251e64ab7925ecaf4da3ed1f4e0b4674cbf6c66b5b8651e0e6431675ffa366

    Resultado mostrado ao modelo
27. 26Turno do modelo
    call_7cf7089ereferenceFim: 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.g_post = 0,8153. Reference path transcribed from the item's own estimation script.",
        "caveats": [],
        "figure_refs": [
          "call_a45fb98d:coef.g_post"
        ]
      }

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

    Números encontrados

    0,8153

    Sem lastro

    —

    Não citados

    —

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

    Estimativa (infer.ols): coef.g_post = 0,8153. Reference path transcribed from the item's own estimation script.

    Figuras citadas: call_a45fb98d:coef.g_post · Parada: answered

    Figura da resposta: call_a45fb98d:coef.g_post = 0,8152720.8152716551072007 (se 0,4639210.4639206965118652)
30. 29Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
31. 30Pontuação
    #1figura da resposta próximapassouobrigatória0.8152716551 in [-0.09401291006, 1.72455622]?
32. 31Pontuação
    #2figura da resposta próximapassouregistrada0.8152716551 vs 0.8152716551072032
33. 32Pontuação
    #3figura da resposta próximapassouregistrada0.4639206965 vs 0.46392069651186174

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