Tarefa crb_papers.q023

Veredito

Pergunta feita ao analista

By how much did the State Democracy Index change from 2002 to 2004 in states that newly gained unified Republican control in 2003 versus states that did not?

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_0012— | Variable Name | Description | | --- | --- | | index | Sequential row identifier from the file export; included for reference and not used as a substantive measure. | | Republican | Binary indicator (0/1) for unified Republican control of state government in a given year. Equals 1 when the governorship and both legislative chambers were held by Republicans; 0 otherwise (including divided or other control). Compiled from state partisan balance data with updates from legislative tracking organizations. | | state | U.S. state name (50 states). | | year | Calendar year of observation (2000–2016). | | democracy_mcmc | Continuous State Democracy Index score for the state-year, estimated via Bayesian factor analysis (MCMC) from 51 state-level indicators of electoral rules, administrative performance, districting metrics, and related practices. The scale is approximately standardized across the panel (values can be negative or positive around zero); higher values indicate stronger electoral-democracy performance. Sources feeding the indicators include election administration data (e.g., MIT Election Lab), legislative policy compilations (e.g., NCSL), gerrymandering diagnostics, and measures of policy–opinion congruence. | Dataset Context: This file is a state–year panel that assembles administrative records, policy codings, and quantitative diagnostics to summarize how U.S. states structure and administer electoral democracy. The period 2000–2016 spans consequential developments in state election law and administration, including reforms that expanded access (e.g., early voting, online and same‑day registration, and the beginnings of automatic voter registration late in the period) and policies that increased participation costs for some voters (e.g., voter ID requirements). It also covers the 2010 redistricting cycle and subsequent evaluations of district plans using established gerrymandering metrics. During these years, federal–state relations affecting elections shifted as well, including changes to Voting Rights Act oversight after 2013, which altered how and where states implemented election-law changes. The State Democracy Index (reported here as democracy_mcmc) is constructed by combining 51 indicators into a single latent score using Bayesian factor analysis designed for mixed data types (binary, ordinal, and continuous). Indicator families include: election access rules (registration and voting modalities), administrative performance (e.g., wait times, provisional ballot handling, data completeness), post‑election audit requirements, districting fairness diagnostics (efficiency gap, partisan symmetry, declination, compactness), and measures of policy–opinion alignment. Indicator inputs draw on sources such as the MIT Election Lab’s compilations of election administration statistics, the National Conference of State Legislatures’ inventories of state election laws, quantitative assessments of district plans, and state policy–opinion datasets. Scores are standardized to facilitate comparison across states and years, with higher values reflecting stronger performance on these electoral-democracy dimensions. The unified-party-control variable is built from long‑running state partisan balance datasets and contemporary updates that track which party simultaneously held the governorship and majorities in both legislative chambers in each year. Together with state names and calendar years, these fields allow analysts to align institutional configurations with contemporaneous features of election law, administration, and districting captured in the index.
   ref_panel
   ref_post
   ref_pre
   ref_switch
   Transformação "select" aplicada à base "ref_switch" (833 → 833 linhas). {"columns":["Republican","state","year","democracy_mcmc"]}
   Transformação "dropna" aplicada à base "ref_switch" (833 → 833 linhas). {"columns":["state","year","Republican","democracy_mcmc"]}
   Transformação "derive" aplicada à base "ref_switch" (833 → 833 linhas). {"args":{"by":["state"],"column":"Republican","order":["year"],"periods":1},"name":"r_prev","op":"lag"}
   Transformação "filter" aplicada à base "ref_switch" (833 → 27 linhas). {"query":"Republican == 1 and r_prev == 0"}
   Transformação "collapse" aplicada à base "ref_switch" (27 → 24 linhas). {"aggs":[{"column":"year","fn":"min","name":"syear"}],"by":["state"]}
   Transformação "select" aplicada à base "ref_pre" (833 → 833 linhas). {"columns":["Republican","state","year","democracy_mcmc"]}
   Transformação "dropna" aplicada à base "ref_pre" (833 → 833 linhas). {"columns":["state","year","Republican","democracy_mcmc"]}
   Transformação "filter" aplicada à base "ref_pre" (833 → 49 linhas). {"query":"year == 2002"}
   Transformação "select" aplicada à base "ref_pre" (49 → 49 linhas). {"columns":["state","Republican"]}
   Transformação "rename" aplicada à base "ref_pre" (49 → 49 linhas). {"map":{"Republican":"r_pre"}}
   Transformação "select" aplicada à base "ref_post" (833 → 833 linhas). {"columns":["Republican","state","year","democracy_mcmc"]}
   Transformação "dropna" aplicada à base "ref_post" (833 → 833 linhas). {"columns":["state","year","Republican","democracy_mcmc"]}
   Transformação "filter" aplicada à base "ref_post" (833 → 49 linhas). {"query":"year == 2004"}
   Transformação "select" aplicada à base "ref_post" (49 → 49 linhas). {"columns":["state","Republican"]}
   Transformação "rename" aplicada à base "ref_post" (49 → 49 linhas). {"map":{"Republican":"r_post"}}
   Transformação "select" aplicada à base "ref_panel" (833 → 833 linhas). {"columns":["Republican","state","year","democracy_mcmc"]}
   Transformação "dropna" aplicada à base "ref_panel" (833 → 833 linhas). {"columns":["state","year","Republican","democracy_mcmc"]}
   Transformação "filter" aplicada à base "ref_panel" (833 → 98 linhas). {"query":"year in [2002, 2004]"}
   Transformação "merge" aplicada à base "ref_panel" (98 → 98 linhas). {"how":"left","on":["state"],"with":"ref_switch"}
   Transformação "merge" aplicada à base "ref_panel" (98 → 98 linhas). {"how":"inner","on":["state"],"with":"ref_pre"}
   Transformação "merge" aplicada à base "ref_panel" (98 → 98 linhas). {"how":"inner","on":["state"],"with":"ref_post"}
   Transformação "filter" aplicada à base "ref_panel" (98 → 76 linhas). {"query":"r_pre == 0 and ((syear == 2003 and r_post == 1) or r_post == 0)"}
   Transformação "derive" aplicada à base "ref_panel" (76 → 76 linhas). {"args":{"cmp":"eq","column":"r_post","value":1},"name":"Tgroup","op":"indicator"}
   Transformação "derive" aplicada à base "ref_panel" (76 → 76 linhas). {"args":{"cmp":"eq","column":"year","value":2004},"name":"Post","op":"indicator"}
   Transformação "derive" aplicada à base "ref_panel" (76 → 76 linhas). {"args":{"columns":["Tgroup","Post"]},"name":"DID","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_c334014c nos detalhes

   sm.OLS(y, add_constant([Tgroup, Post, DID])).fit(cov_type='cluster', groups=state) (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([Tgroup, Post, DID])).fit(cov_type='cluster', groups=state) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)
   Argumentos principais

   cluster:

   "state"

   controls:

   ["Tgroup","Post"]

   dataset:

   "ref_panel"

   intercept:

   true

   level:

   0,950.95

   se:

   "HC1"

   x:

   ["DID"]

   y:

   "democracy_mcmc"

   Resultado calculado

   • coef.DID = -0,0908634 -0.0908634274898238
     Coefficient on DID in the regression of democracy_mcmc
   Diagnósticos: n_obs = 7676 (observations used) · k = 44 (parameters estimated, fixed-effect dummies included) · r2 = 0,04421560.04421557708787305 · df_resid = 7272 · se_type = "cluster" (cluster-robust standard errors (CR1) by 'state') · n_clusters = 3838 (38 clusters of 'state'; below about 40, cluster-robust errors are too small — confirm with infer.randomization_inference or a wild cluster bootstrap)
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: -0,09086
   Figuras citadas: call_c334014c:coef.DID
   • ✓
     #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.q023.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

   By how much did the State Democracy Index change from 2002 to 2004 in states that newly gained unified Republican control in 2003 versus states that did not?

   ds_0012 (agent)

   | Variable Name | Description | | --- | --- | | index | Sequential row identifier from the file export; included for reference and not used as a substantive measure. | | Republican | Binary indicator (0/1) for unified Republican control of state government in a given year. Equals 1 when the governorship and both legislative chambers were held by Republicans; 0 otherwise (including divided or other control). Compiled from state partisan balance data with updates from legislative tracking organizations. | | state | U.S. state name (50 states). | | year | Calendar year of observation (2000–2016). | | democracy_mcmc | Continuous State Democracy Index score for the state-year, estimated via Bayesian factor analysis (MCMC) from 51 state-level indicators of electoral rules, administrative performance, districting metrics, and related practices. The scale is approximately standardized across the panel (values can be negative or positive around zero); higher values indicate stronger electoral-democracy performance. Sources feeding the indicators include election administration data (e.g., MIT Election Lab), legislative policy compilations (e.g., NCSL), gerrymandering diagnostics, and measures of policy–opinion congruence. | Dataset Context: This file is a state–year panel that assembles administrative records, policy codings, and quantitative diagnostics to summarize how U.S. states structure and administer electoral democracy. The period 2000–2016 spans consequential developments in state election law and administration, including reforms that expanded access (e.g., early voting, online and same‑day registration, and the beginnings of automatic voter registration late in the period) and policies that increased participation costs for some voters (e.g., voter ID requirements). It also covers the 2010 redistricting cycle and subsequent evaluations of district plans using established gerrymandering metrics. During these years, federal–state relations affecting elections shifted as well, including changes to Voting Rights Act oversight after 2013, which altered how and where states implemented election-law changes. The State Democracy Index (reported here as democracy_mcmc) is constructed by combining 51 indicators into a single latent score using Bayesian factor analysis designed for mixed data types (binary, ordinal, and continuous). Indicator families include: election access rules (registration and voting modalities), administrative performance (e.g., wait times, provisional ballot handling, data completeness), post‑election audit requirements, districting fairness diagnostics (efficiency gap, partisan symmetry, declination, compactness), and measures of policy–opinion alignment. Indicator inputs draw on sources such as the MIT Election Lab’s compilations of election administration statistics, the National Conference of State Legislatures’ inventories of state election laws, quantitative assessments of district plans, and state policy–opinion datasets. Scores are standardized to facilitate comparison across states and years, with higher values reflecting stronger performance on these electoral-democracy dimensions. The unified-party-control variable is built from long‑running state partisan balance datasets and contemporary updates that track which party simultaneously held the governorship and majorities in both legislative chambers in each year. Together with state names and calendar years, these fields allow analysts to align institutional configurations with contemporaneous features of election law, administration, and districting captured in the index.

   file c41bdacd62be74293e7a28a6c5d82a460ccf5f046e080a7443363bc59ff73fb0 · frame —

   ref_panel (reference)

   file c41bdacd62be74293e7a28a6c5d82a460ccf5f046e080a7443363bc59ff73fb0 · frame —

   1. select {"columns":["Republican","state","year","democracy_mcmc"]}
   2. dropna {"columns":["state","year","Republican","democracy_mcmc"]}
   3. filter {"query":"year in [2002, 2004]"}
   4. merge {"how":"left","on":["state"],"with":"ref_switch"}
   5. merge {"how":"inner","on":["state"],"with":"ref_pre"}
   6. merge {"how":"inner","on":["state"],"with":"ref_post"}
   7. filter {"query":"r_pre == 0 and ((syear == 2003 and r_post == 1) or r_post == 0)"}
   8. derive {"args":{"cmp":"eq","column":"r_post","value":1},"name":"Tgroup","op":"indicator"}
   9. derive {"args":{"cmp":"eq","column":"year","value":2004},"name":"Post","op":"indicator"}
   10. derive {"args":{"columns":["Tgroup","Post"]},"name":"DID","op":"interaction"}

   ref_post (reference)

   file c41bdacd62be74293e7a28a6c5d82a460ccf5f046e080a7443363bc59ff73fb0 · frame —

   1. select {"columns":["Republican","state","year","democracy_mcmc"]}
   2. dropna {"columns":["state","year","Republican","democracy_mcmc"]}
   3. filter {"query":"year == 2004"}
   4. select {"columns":["state","Republican"]}
   5. rename {"map":{"Republican":"r_post"}}

   ref_pre (reference)

   file c41bdacd62be74293e7a28a6c5d82a460ccf5f046e080a7443363bc59ff73fb0 · frame —

   1. select {"columns":["Republican","state","year","democracy_mcmc"]}
   2. dropna {"columns":["state","year","Republican","democracy_mcmc"]}
   3. filter {"query":"year == 2002"}
   4. select {"columns":["state","Republican"]}
   5. rename {"map":{"Republican":"r_pre"}}

   ref_switch (reference)

   file c41bdacd62be74293e7a28a6c5d82a460ccf5f046e080a7443363bc59ff73fb0 · frame —

   1. select {"columns":["Republican","state","year","democracy_mcmc"]}
   2. dropna {"columns":["state","year","Republican","democracy_mcmc"]}
   3. derive {"args":{"by":["state"],"column":"Republican","order":["year"],"periods":1},"name":"r_prev","op":"lag"}
   4. filter {"query":"Republican == 1 and r_prev == 0"}
   5. collapse {"aggs":[{"column":"year","fn":"min","name":"syear"}],"by":["state"]}
2. 1Transformação
   ref_switch · select {"columns":["Republican","state","year","democracy_mcmc"]}

   833 → 833 linhas

   Frame antes → depois: 5bc3cfb33d70 → 5bc3cfb33d70
3. 2Transformação
   ref_switch · dropna {"columns":["state","year","Republican","democracy_mcmc"]}

   833 → 833 linhas

   Frame antes → depois: 5bc3cfb33d70 → 5bc3cfb33d70
4. 3Transformação
   ref_switch · derive {"args":{"by":["state"],"column":"Republican","order":["year"],"periods":1},"name":"r_prev","op":"lag"}

   833 → 833 linhas

   Frame antes → depois: 5bc3cfb33d70 → abbd3d6b245e
5. 4Transformação
   ref_switch · filter {"query":"Republican == 1 and r_prev == 0"}

   833 → 27 linhas

   Frame antes → depois: abbd3d6b245e → c56928f5bd8e
6. 5Transformação
   ref_switch · collapse {"aggs":[{"column":"year","fn":"min","name":"syear"}],"by":["state"]}

   27 → 24 linhas

   Frame antes → depois: c56928f5bd8e → 08613c2c5326
7. 6Transformação
   ref_pre · select {"columns":["Republican","state","year","democracy_mcmc"]}

   833 → 833 linhas

   Frame antes → depois: 5bc3cfb33d70 → 5bc3cfb33d70
8. 7Transformação
   ref_pre · dropna {"columns":["state","year","Republican","democracy_mcmc"]}

   833 → 833 linhas

   Frame antes → depois: 5bc3cfb33d70 → 5bc3cfb33d70
9. 8Transformação
   ref_pre · filter {"query":"year == 2002"}

   833 → 49 linhas

   Frame antes → depois: 5bc3cfb33d70 → 0f3725d2d987
10. 9Transformação
    ref_pre · select {"columns":["state","Republican"]}

    49 → 49 linhas

    Frame antes → depois: 0f3725d2d987 → 6f0fae04313e
11. 10Transformação
    ref_pre · rename {"map":{"Republican":"r_pre"}}

    49 → 49 linhas

    Frame antes → depois: 6f0fae04313e → 906714a592f6
12. 11Transformação
    ref_post · select {"columns":["Republican","state","year","democracy_mcmc"]}

    833 → 833 linhas

    Frame antes → depois: 5bc3cfb33d70 → 5bc3cfb33d70
13. 12Transformação
    ref_post · dropna {"columns":["state","year","Republican","democracy_mcmc"]}

    833 → 833 linhas

    Frame antes → depois: 5bc3cfb33d70 → 5bc3cfb33d70
14. 13Transformação
    ref_post · filter {"query":"year == 2004"}

    833 → 49 linhas

    Frame antes → depois: 5bc3cfb33d70 → b42f69e0cbb1
15. 14Transformação
    ref_post · select {"columns":["state","Republican"]}

    49 → 49 linhas

    Frame antes → depois: b42f69e0cbb1 → 6f91e0a276f6
16. 15Transformação
    ref_post · rename {"map":{"Republican":"r_post"}}

    49 → 49 linhas

    Frame antes → depois: 6f91e0a276f6 → 9951234bb04f
17. 16Transformação
    ref_panel · select {"columns":["Republican","state","year","democracy_mcmc"]}

    833 → 833 linhas

    Frame antes → depois: 5bc3cfb33d70 → 5bc3cfb33d70
18. 17Transformação
    ref_panel · dropna {"columns":["state","year","Republican","democracy_mcmc"]}

    833 → 833 linhas

    Frame antes → depois: 5bc3cfb33d70 → 5bc3cfb33d70
19. 18Transformação
    ref_panel · filter {"query":"year in [2002, 2004]"}

    833 → 98 linhas

    Frame antes → depois: 5bc3cfb33d70 → c076c232193a
20. 19Transformação
    ref_panel · merge {"how":"left","on":["state"],"with":"ref_switch"}

    98 → 98 linhas

    Frame antes → depois: c076c232193a → 403573f3118a
21. 20Transformação
    ref_panel · merge {"how":"inner","on":["state"],"with":"ref_pre"}

    98 → 98 linhas

    Frame antes → depois: 403573f3118a → f152f859ec4f
22. 21Transformação
    ref_panel · merge {"how":"inner","on":["state"],"with":"ref_post"}

    98 → 98 linhas

    Frame antes → depois: f152f859ec4f → e47a2569062e
23. 22Transformação
    ref_panel · filter {"query":"r_pre == 0 and ((syear == 2003 and r_post == 1) or r_post == 0)"}

    98 → 76 linhas

    Frame antes → depois: e47a2569062e → 70f03d153697
24. 23Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"r_post","value":1},"name":"Tgroup","op":"indicator"}

    76 → 76 linhas

    Frame antes → depois: 70f03d153697 → 1d87793fdb7b
25. 24Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"year","value":2004},"name":"Post","op":"indicator"}

    76 → 76 linhas

    Frame antes → depois: 1d87793fdb7b → e2d4b73781d5
26. 25Transformação
    ref_panel · derive {"args":{"columns":["Tgroup","Post"]},"name":"DID","op":"interaction"}

    76 → 76 linhas

    Frame antes → depois: e2d4b73781d5 → 71d34ad836af
27. 26Turno do modelo
    call_f778390areferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

    Texto visível

    sm.OLS(y, add_constant([Tgroup, Post, DID])).fit(cov_type='cluster', groups=state) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

    Chamadas pedidas

    • infer.ols

      {
        "cluster": "state",
        "controls": [
          "Tgroup",
          "Post"
        ],
        "dataset": "ref_panel",
        "x": [
          "DID"
        ],
        "y": "democracy_mcmc"
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
28. 27Ferramenta
    infer.ols— Regressão linear por mínimos quadrados ordinários (MQO)v0.1.0call_c334014cdecidido pelo modeloreexecutável

    Motivo: sm.OLS(y, add_constant([Tgroup, Post, DID])).fit(cov_type='cluster', groups=state) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

    Argumentos

    {
      "cluster": "state",
      "controls": [
        "Tgroup",
        "Post"
      ],
      "dataset": "ref_panel",
      "fe": [],
      "intercept": true,
      "level": 0.95,
      "se": "HC1",
      "subset": null,
      "weights": null,
      "x": [
        "DID"
      ],
      "y": "democracy_mcmc"
    }

    Figuras call_c334014c

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.DID Coefficient on DID in the regression of democracy_mcmc	-0,0908634	-0.0908634274898238	0,0809480.08094800175309347	-0,25488 – 0,0731528

    Diagnósticos

    • n_obs = 7676 (observations used)
    • k = 44 (parameters estimated, fixed-effect dummies included)
    • r2 = 0,04421560.04421557708787305
    • df_resid = 7272
    • se_type = "cluster" (cluster-robust standard errors (CR1) by 'state')
    • n_clusters = 3838 (38 clusters of 'state'; below about 40, cluster-robust errors are too small — confirm with infer.randomization_inference or a wild cluster bootstrap)

    Derivação matemática e pressupostos

    coef.DID

    1. Coeficiente de DID na regressão por mínimos quadrados ordinários de democracy_mcmc.→ -0,0908634 (-0.0908634274898238)
    2. Erro-padrão robusto por conglomerado (CR1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 0,080948 (0.08094800175309347)
    3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 37 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 'state' and may be correlated within them

    Tabelas (1)

    {
      "coefficients": [
        {
          "ci_high": 0.5492834934481965,
          "ci_low": 0.11924866252443975,
          "coef": 0.33426607798631813,
          "p": 0.0032270892983367184,
          "se": 0.1061189494014959,
          "t": 3.1499188398637328,
          "term": "Intercept"
        },
        {
          "ci_high": 0.07315280355956132,
          "ci_low": -0.2548796585392089,
          "coef": -0.0908634274898238,
          "p": 0.2688850665170823,
          "se": 0.08094800175309347,
          "t": -1.1224912971536252,
          "term": "DID"
        },
        {
          "ci_high": 0.7261191945218577,
          "ci_low": -1.4772253082583748,
          "coef": -0.3755530568682585,
          "p": 0.49405464702327406,
          "se": 0.5437155016079482,
          "t": -0.6907161111971661,
          "term": "Tgroup"
        },
        {
          "ci_high": 0.010704225035495327,
          "ci_low": -0.17363042980761256,
          "coef": -0.08146310238605863,
          "p": 0.08149797844768684,
          "se": 0.045487943077118155,
          "t": -1.7908724131128517,
          "term": "Post"
        }
      ]
    }

    Hashes SHA-256 e metadados de reprodução

    Digests dos dados

    ref_panel 71d34ad836af7fee0a177ad2e5a28908b4c02f8b0b81f21aa933f5114bcc3544

    sha256 do resultado

    985310214c76aadefdada70088328e15bfdafdf039849a15918483654ebf4f7d

    Resultado mostrado ao modelo
29. 28Turno do modelo
    call_46fea0c3referenceFim: 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.DID = -0,09086. Reference path transcribed from the item's own estimation script.",
        "caveats": [],
        "figure_refs": [
          "call_c334014c:coef.DID"
        ]
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
30. 29Guarda
    resposta aceita

    Números encontrados

    -0,09086

    Sem lastro

    —

    Não citados

    —

    1 nova(s) tentativa(s) restante(s)
31. 30Resposta

    Estimativa (infer.ols): coef.DID = -0,09086. Reference path transcribed from the item's own estimation script.

    Figuras citadas: call_c334014c:coef.DID · Parada: answered

    Figura da resposta: call_c334014c:coef.DID = -0,0908634-0.0908634274898238 (se 0,0809480.08094800175309347)
32. 31Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
33. 32Pontuação
    #1figura da resposta próximapassouobrigatória-0.09086342749 in [-0.2495215109, 0.06779465595]?
34. 33Pontuação
    #2figura da resposta próximapassouregistrada-0.09086342749 vs -0.09086342748982335
35. 34Pontuação
    #3figura da resposta próximapassouregistrada0.08094800175 vs 0.08094800175309143

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