Tarefa paper_repro.callaway_santanna_2021.mpdta_att_gt.agent

Veredito

Pergunta feita ao analista

Callaway and Sant'Anna (2021), 'Difference-in-Differences with Multiple Time Periods', as in the did package's documented example on its county panel (2003-2007): estimate the group-time average treatment effects ATT(g,t) of a state minimum-wage increase on log county teen employment, for each adoption cohort (2004, 2006, 2007) and each period from 2004 to 2007, using the never-treated counties as the comparison group, no covariates and the package's default (varying) base period. Report the group-time effects.

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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

   Callaway and Sant'Anna (2021), 'Difference-in-Differences with Multiple Time Periods', as in the did package's documented example on its county panel (2003-2007): estimate the group-time average treatment effects ATT(g,t) of a state minimum-wage increase on log county teen employment, for each adoption cohort (2004, 2006, 2007) and each period from 2004 to 2007, using the never-treated counties as the comparison group, no covariates and the package's default (varying) base period. Report the group-time effects.

   mpdta (agent)

   The did package's example panel mpdta (Callaway and Sant'Anna 2021): county-level teen employment in 500 U.S. counties, 2003-2007, one row per county and year. Some states first raised their minimum wage in 2004, some in 2006 and some in 2007; the others did not in the window. Columns: countyreal, the county id; year; lemp, the log of county teen employment; lpop, the log of county population; first_treat, the year the county's state first raised its minimum wage (2004, 2006 or 2007; 0 for counties never treated in the window); treat, 1 for counties in states that raised it at some point in the window.

   file 0be3bc5eaaedaa50e047deaf45ad0e193e7210df1644103c63a6c1baa75965d4 · frame —

   1. rename {"map":{"first.treat":"first_treat"}}
2. 1Transformação
   mpdta · rename {"map":{"first.treat":"first_treat"}}

   2500 → 2500 linhas

   Frame antes → depois: 76542064d0d8 → ede9279f5a99
3. 2Turno do modelo
   call_d2c4653cgemini-3.8-flashFim: STOP7.701 ms14378 de entrada · 54 de saída · 275 de raciocínio · 0 em cache

   Texto visível

   I will inspect the staggered difference-in-differences procedure to ensure the parameters and diagnostics align with Callaway and Sant'Anna (2021).

   Resumo do raciocínio, como declarado pelo modelo

   **My Thought Process on Estimating Group-Time Average Treatment Effects** Right, so the core task here is to estimate the group-time average treatment effects, or ATT(g,t) as Callaway and Sant'Anna (2021) refer to them. I'm looking at the impact of state minimum wage increases on log county teen employment (`lemp`), using the `mpdta` county panel dataset. This is a standard staggered adoption difference-in-differences setup, and I need to be precise about the parameters. My adoption cohorts of interest are explicitly stated: 2004, 2006, and 2007. For the counterfactual, I'll be using the `never_treated` group as my comparison. The dependent variable is `lemp`, and my units of observation are `countyreal` over `year`. The crucial variable for identifying the timing of treatment for each county is `first_treat`. Now, I need to figure out the best tool for this job within the available framework. I'm aware of `infer.did_staggered` and that seems like the most direct and appropriate function for this type of analysis, given its specific design for staggered adoption DiD. I've already mentally (or actually, if I were in an interactive session) pulled up the arguments for `infer.did_staggered` to ensure I'm specifying it correctly: * `dataset`: This will be `'mpdta'`, as that's the data I'm working with. * `y`: The outcome variable is clearly `'lemp'`. * `unit`: My observational units are counties, specified as `'countyreal'`. * `time`: The time dimension is `'year'`. * `first_treat`: This is the key variable indicating when each county adopted the policy, so it's `'first_treat'`. * `control_group`: The instructions clearly state to use `'never_treated'` as the comparison group. * `level`: I'll set the confidence level to `0.95` for standard hypothesis testing. * `n_boot`: The default number of bootstrap iterations is usually sufficient, but I'll note it's a parameter I can adjust if needed for precision or computational time. * `seed`: Setting a `seed` to `0` is good practice for reproducibility of any random processes, like bootstrapping. My next step, or concurrent thought, is to ensure I have the necessary skill loaded. I'd either load the `differences-in-differences` skill explicitly or verify that `infer.did_staggered` is accessible through my current environment. Essentially, I'm confirming I have the right tool and know how to use it with the correct inputs to get the ATT(g,t) estimates I need.

   Chamadas pedidas

   • load_skill (meta-ferramenta)

     {
       "name": "differences-in-differences"
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
4. 3Habilidade
   differences-in-differencesTexto da habilidade
5. 4Turno do modelo
   call_c2f2681agemini-3.8-flashFim: STOP6.255 ms15266 de entrada · 102 de saída · 70 de raciocínio · 0 em cache

   Texto visível

   I will run Callaway and Sant'Anna's staggered difference-in-differences estimator on `mpdta` to obtain the group-time average treatment effects ATT(g,t).

   Resumo do raciocínio, como declarado pelo modelo

   **My Thought Process** So, here's what's going through my head. I've been looking at this `mpdta` dataset, and I want to dive deeper into how the `lemp` variable behaves, specifically within the context of `countyreal` units and across `year` time periods. The key element I'm interested in is how the `first_treat` variable acts as a marker for when treatment begins, and I want to isolate the impact on those who were `never_treated` to serve as my baseline. To do this, I'm going to call the `infer.did_staggered` function. I need to make sure I pass in all the correct arguments: the `dataset` is indeed "mpdta", the outcome variable `y` is "lemp", my unit of observation `unit` is "countyreal", and the temporal dimension `time` is "year". Crucially, the indicator for the start of treatment is "first_treat", and my reference group, the control, is defined by "never_treated". This setup should allow me to properly execute a staggered difference-in-differences analysis, which is exactly what I need here.

   Chamadas pedidas

   • infer.did_staggered

     {
       "control_group": "never_treated",
       "dataset": "mpdta",
       "first_treat": "first_treat",
       "time": "year",
       "unit": "countyreal",
       "y": "lemp"
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
6. 5Ferramenta
   infer.did_staggeredv0.1.0call_e841b472decidido pelo modeloreexecutável

   Motivo: I will run Callaway and Sant'Anna's staggered difference-in-differences estimator on `mpdta` to obtain the group-time average treatment effects ATT(g,t).

   Argumentos

   {
     "control_group": "never_treated",
     "dataset": "mpdta",
     "first_treat": "first_treat",
     "level": 0.95,
     "n_boot": 199,
     "seed": 0,
     "time": "year",
     "unit": "countyreal",
     "y": "lemp"
   }

   Figuras call_e841b472

   Chave	Valor	Valor bruto	Erro-padrão	IC
   att Average effect on lemp across post-adoption cohort-periods (Callaway–Sant'Anna)	-0,0399513	-0.03995127515517701	0,01194110.011941084528484509	-0,0633554 – -0,0165472

   Diagnósticos

   • pretrend_mean = -0,000596958-0.0005969576739334104
   • pretrend_se = 0,006825270.006825271151463481
   • n_cohorts = 33
   • n_never_treated = 309309
   • n_units = 500500
   • control_group = "never_treated"
   • n_boot = 199199
   • n_boot_failed = 00

   Tabelas (3)

   {
     "att_gt": [
       {
         "att": -0.010503246220963533,
         "e": 0,
         "g": 2004,
         "n_control": 309,
         "n_g": 20,
         "post": true,
         "se": 0.022882360180335775,
         "t": 2004
       },
       {
         "att": -0.07042315810314909,
         "e": 1,
         "g": 2004,
         "n_control": 309,
         "n_g": 20,
         "post": true,
         "se": 0.035349338097653976,
         "t": 2005
       },
       {
         "att": -0.13725873888940443,
         "e": 2,
         "g": 2004,
         "n_control": 309,
         "n_g": 20,
         "post": true,
         "se": 0.037707820794609354,
         "t": 2006
       },
       {
         "att": -0.10081136308540525,
         "e": 3,
         "g": 2004,
         "n_control": 309,
         "n_g": 20,
         "post": true,
         "se": 0.03498817563354677,
         "t": 2007
       },
       {
         "att": 0.006520112424232907,
         "e": -2,
         "g": 2006,
         "n_control": 309,
         "n_g": 40,
         "post": false,
         "se": 0.02331943920931109,
         "t": 2004
       },
       {
         "att": -0.002750818750518684,
         "e": -1,
         "g": 2006,
         "n_control": 309,
         "n_g": 40,
         "post": false,
         "se": 0.01848490029541098,
         "t": 2005
       },
       {
         "att": -0.00459460695286272,
         "e": 0,
         "g": 2006,
         "n_control": 309,
         "n_g": 40,
         "post": true,
         "se": 0.018221346748964883,
         "t": 2006
       },
       {
         "att": -0.04122447154621793,
         "e": 1,
         "g": 2006,
         "n_control": 309,
         "n_g": 40,
         "post": true,
         "se": 0.019524036066045164,
         "t": 2007
       },
       {
         "att": 0.030506655583292096,
         "e": -3,
         "g": 2007,
         "n_control": 309,
         "n_g": 131,
         "post": false,
         "se": 0.014885220499805493,
         "t": 2004
       },
       {
         "att": -0.0027258928861159572,
         "e": -2,
         "g": 2007,
         "n_control": 309,
         "n_g": 131,
         "post": false,
         "se": 0.01728908780159486,
         "t": 2005
       },
       {
         "att": -0.031087119389688136,
         "e": -1,
         "g": 2007,
         "n_control": 309,
         "n_g": 131,
         "post": false,
         "se": 0.01864182705314265,
         "t": 2006
       },
       {
         "att": -0.026054410719197237,
         "e": 0,
         "g": 2007,
         "n_control": 309,
         "n_g": 131,
         "post": true,
         "se": 0.017105063505205884,
         "t": 2007
       }
     ],
     "by_cohort": [
       {
         "att": -0.07974912657473057,
         "g": 2004,
         "n_g": 20,
         "post_periods": 4
       },
       {
         "att": -0.022909539249540326,
         "g": 2006,
         "n_g": 40,
         "post_periods": 2
       },
       {
         "att": -0.026054410719197237,
         "g": 2007,
         "n_g": 131,
         "post_periods": 1
       }
     ],
     "event_time": [
       {
         "att": 0.030506655583292096,
         "e": -3,
         "n_cohorts": 1,
         "se": 0.014885220499805493
       },
       {
         "att": -0.0005630846263852289,
         "e": -2,
         "n_cohorts": 2,
         "se": 0.013665058460471256
       },
       {
         "att": -0.024458744971168966,
         "e": -1,
         "n_cohorts": 2,
         "se": 0.014563335173162988
       },
       {
         "att": -0.019931816789259777,
         "e": 0,
         "n_cohorts": 3,
         "se": 0.01203765078223572
       },
       {
         "att": -0.050957367065194985,
         "e": 1,
         "n_cohorts": 2,
         "se": 0.016800409715261998
       },
       {
         "att": -0.13725873888940443,
         "e": 2,
         "n_cohorts": 1,
         "se": 0.037707820794609354
       },
       {
         "att": -0.10081136308540525,
         "e": 3,
         "n_cohorts": 1,
         "se": 0.03498817563354677
       }
     ]
   }

   Digests dos dados

   mpdta ede9279f5a99110cfc103b63bc6f1b9052eb1f6f5df8290bc4fd8388ae23fac3

   sha256 do resultado

   9bce7a417528b91b00e09b31f390b01459bcc289f3dce0d63e6c2434a8d7b475

   Resultado mostrado ao modelo
7. 6Turno do modelo
   call_bf29bc1fgemini-3.8-flashFim: STOP32.669 ms16484 de entrada · 935 de saída · 3294 de raciocínio · 12619 em cache

   Resumo do raciocínio, como declarado pelo modelo

   **My Thought Process on Estimating Group-Time Average Treatment Effects of Minimum Wage Increases** I've carefully reviewed the user's request, which asks me to estimate the group-time average treatment effects (ATT(g,t)) of state minimum wage increases on log county teen employment. The core of this task involves using the Callaway and Sant'Anna (2021) method, as implemented in the `did` package, specifically employing the county panel data from 2003-2007 provided as an example. I need to consider three distinct adoption cohorts (2004, 2006, and 2007) and estimate the effects for each period from 2004 to 2007, treating the never-treated counties as the comparison group, without covariates, and using the default varying base period. The ultimate goal is to report these group-time effects. Upon executing the relevant `did` package functions, I've obtained a table detailing these group-time ATT estimates, labeled `att_gt`. I'm now going through this table systematically to extract the requested information for each cohort and time period. For the **2004 cohort**, I see the following estimates: * In 2004, the ATT is -0.0105032 (with a standard error of 0.0228824). This period is marked as `post: true` and `e: 0`, meaning it's the first period after treatment adoption. * In 2005, the ATT is -0.0704232 (SE = 0.0353493). This is `post: true` and `e: 1`, the second period post-adoption. * In 2006, the ATT is -0.137259 (SE = 0.0377078), `post: true` and `e: 2`. * In 2007, the ATT is -0.100811 (SE = 0.0349882), `post: true` and `e: 3`. Moving on to the **2006 cohort**: * In 2004, the ATT is 0.00652011 (SE = 0.0233194). This is `post: false` and `e: -2`, indicating a pre-treatment period. * In 2005, the ATT is -0.00275082 (SE = 0.0184849). This is also `post: false` and `e: -1`, another pre-treatment period. * In 2006, the ATT is -0.00459461 (SE = 0.0182213), now `post: true` and `e: 0`. * In 2007, the ATT is -0.0412245 (SE = 0.019524), `post: true` and `e: 1`. Finally, for the **2007 cohort**: * In 2004, the ATT is 0.0305067 (SE = 0.0148852), marked as `post: false` and `e: -3` (pre-treatment). * In 2005, the ATT is -0.00272589 (SE = 0.0172891), `post: false` and `e: -2` (pre-treatment). * In 2006, the ATT is -0.0310871 (SE = 0.0186418), `post: false` and `e: -1` (pre-treatment). * In 2007, the ATT is -0.0260544 (SE = 0.0171051), `post: true` and `e: 0`. I also have an overall ATT estimate, which represents the average effect across all post-adoption cohort-periods. This value is -0.0399513, with a standard error of 0.0119411 and a 95% confidence interval of [-0.0633554, -0.0165472]. This overall estimate is identified by the key `call_e841b472:att`. To ensure the validity of the method, I've reviewed the diagnostic tests. The pre-trend mean is -0.00059695767393341 (SE: 0.00682527115146348). This suggests that, on average, there were no significant trends in log county teen employment in the years prior to treatment adoption among the treated groups compared to the never-treated group, which supports the parallel trends assumption. The diagnostics also show that there are 3 cohorts included in the analysis, and the comparison group consists of 309 never-treated counties out of a total of 500 units. Regarding the output format and rules: I need to provide a concise summary in English, as the original question was in English, and adhere to Rule 7, which states the answer should be short and direct, including the estimate with its uncertainty, the identification design, and potential limitations. I must also list all quoted figures using their references and any diagnostics that limit the conclusion in caveats. Therefore, my final output will present the group-time effects as requested, along with the overall ATT, the identification strategy (staggered difference-in-differences using never-treated counties), and the pre-trend diagnostic as support for the parallel trends assumption, while also noting potential caveats. The overall ATT, identified by `call_e841b472:att`, will be central to the summary. I've confirmed all numbers are directly from the tool output or the question itself, and no calculations have been performed. I will use standard English punctuation, including decimal points, as the question was in English.

   Chamadas pedidas

   • submit_answer (meta-ferramenta)

     {
       "answer": "Using Callaway and Sant'Anna's (2021) staggered difference-in-differences estimator on the county panel (500 units, of which 309 are never-treated), comparing each treated cohort to never-treated counties without covariates:\n\n### Overall Average Treatment Effect\n- **Overall ATT**: -0.0399513 (SE: 0.0119411; 95% CI: [-0.0633554, -0.0165472])\n\n---\n\n### Group-Time Average Treatment Effects ATT(g,t)\n\n#### Cohort 2004 (20 counties)\n- **2004 (t = 2004, e = 0)**: -0.0105032 (SE: 0.0228824)\n- **2005 (t = 2005, e = 1)**: -0.0704232 (SE: 0.0353493)\n- **2006 (t = 2006, e = 2)**: -0.137259 (SE: 0.0377078)\n- **2007 (t = 2007, e = 3)**: -0.100811 (SE: 0.0349882)\n\n#### Cohort 2006 (40 counties)\n- **2004 (t = 2004, e = -2, pre-treatment)**: 0.00652011 (SE: 0.0233194)\n- **2005 (t = 2005, e = -1, pre-treatment)**: -0.00275082 (SE: 0.0184849)\n- **2006 (t = 2006, e = 0)**: -0.00459461 (SE: 0.0182213)\n- **2007 (t = 2007, e = 1)**: -0.0412245 (SE: 0.019524)\n\n#### Cohort 2007 (131 counties)\n- **2004 (t = 2004, e = -3, pre-treatment)**: 0.0305067 (SE: 0.0148852)\n- **2005 (t = 2005, e = -2, pre-treatment)**: -0.00272589 (SE: 0.0172891)\n- **2006 (t = 2006, e = -1, pre-treatment)**: -0.0310871 (SE: 0.0186418)\n- **2007 (t = 2007, e = 0)**: -0.0260544 (SE: 0.0171051)\n\n---\n\n### Identification and Diagnostics\n- **Parallel trends**: Pre-treatment placebo ATT mean is -0.00059695767393341 (SE: 0.00682527115146348), which is close to zero and supports the parallel trends assumption prior to adoption.",
       "caveats": [
         "Identification relies on the parallel trends assumption holding between treated cohorts and the 309 never-treated counties in the absence of treatment.",
         "Effects exhibit substantial heterogeneity across adoption cohorts: cohort 2004 experiences larger negative employment impacts than later cohorts (2006 and 2007)."
       ],
       "figure_refs": [
         "call_e841b472:att"
       ]
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
8. 7Guarda
   resposta aceita

   Números encontrados

   2021 · 500 · 309 · -0.0399513 · 0.0119411 · 95% · -0.0633554 · -0.0165472 · 2004 · 20 · 2004 · 2004 · 0 · -0.0105032 · 0.0228824 · 2005 · 2005 · 1 · -0.0704232 · 0.0353493 · 2006 · 2006 · 2 · -0.137259 · 0.0377078 · 2007 · 2007 · 3 · -0.100811 · 0.0349882 · 2006 · 40 · 2004 · 2004 · -2 · 0.00652011 · 0.0233194 · 2005 · 2005 · -1 · -0.00275082 · 0.0184849 · 2006 · 2006 · 0 · -0.00459461 · 0.0182213 · 2007 · 2007 · 1 · -0.0412245 · 0.019524 · 2007 · 131 · 2004 · 2004 · -3 · 0.0305067 · 0.0148852 · 2005 · 2005 · -2 · -0.00272589 · 0.0172891 · 2006 · 2006 · -1 · -0.0310871 · 0.0186418 · 2007 · 2007 · 0 · -0.0260544 · 0.0171051 · -0.00059695767393341 · 0.00682527115146348 · 309 · 2004 · 2006 · 2007

   Sem lastro

   —

   Não citados

   —

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

   Using Callaway and Sant'Anna's (2021) staggered difference-in-differences estimator on the county panel (500 units, of which 309 are never-treated), comparing each treated cohort to never-treated counties without covariates: ### Overall Average Treatment Effect - **Overall ATT**: -0.0399513 (SE: 0.0119411; 95% CI: [-0.0633554, -0.0165472]) --- ### Group-Time Average Treatment Effects ATT(g,t) #### Cohort 2004 (20 counties) - **2004 (t = 2004, e = 0)**: -0.0105032 (SE: 0.0228824) - **2005 (t = 2005, e = 1)**: -0.0704232 (SE: 0.0353493) - **2006 (t = 2006, e = 2)**: -0.137259 (SE: 0.0377078) - **2007 (t = 2007, e = 3)**: -0.100811 (SE: 0.0349882) #### Cohort 2006 (40 counties) - **2004 (t = 2004, e = -2, pre-treatment)**: 0.00652011 (SE: 0.0233194) - **2005 (t = 2005, e = -1, pre-treatment)**: -0.00275082 (SE: 0.0184849) - **2006 (t = 2006, e = 0)**: -0.00459461 (SE: 0.0182213) - **2007 (t = 2007, e = 1)**: -0.0412245 (SE: 0.019524) #### Cohort 2007 (131 counties) - **2004 (t = 2004, e = -3, pre-treatment)**: 0.0305067 (SE: 0.0148852) - **2005 (t = 2005, e = -2, pre-treatment)**: -0.00272589 (SE: 0.0172891) - **2006 (t = 2006, e = -1, pre-treatment)**: -0.0310871 (SE: 0.0186418) - **2007 (t = 2007, e = 0)**: -0.0260544 (SE: 0.0171051) --- ### Identification and Diagnostics - **Parallel trends**: Pre-treatment placebo ATT mean is -0.00059695767393341 (SE: 0.00682527115146348), which is close to zero and supports the parallel trends assumption prior to adoption. Ressalvas: Identification relies on the parallel trends assumption holding between treated cohorts and the 309 never-treated counties in the absence of treatment. Effects exhibit substantial heterogeneity across adoption cohorts: cohort 2004 experiences larger negative employment impacts than later cohorts (2006 and 2007).

   • Identification relies on the parallel trends assumption holding between treated cohorts and the 309 never-treated counties in the absence of treatment.
   • Effects exhibit substantial heterogeneity across adoption cohorts: cohort 2004 experiences larger negative employment impacts than later cohorts (2006 and 2007).

   Figuras citadas: call_e841b472:att · Parada: answered

   Figura da resposta: call_e841b472:att = -0,0399513-0.03995127515517701 (se 0,01194110.011941084528484509)
10. 9Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
11. 10Pontuação
    #1figura da resposta escolhidapassouobrigatóriaanswer figure call_e841b472:att (infer.did_staggered)
12. 11Pontuação
    #2ferramenta chamadapassouobrigatóriaanswer call is infer.did_staggered
13. 12Pontuação
    #3argumento igualpassouobrigatóriainfer.did_staggered.y = 'lemp'
14. 13Pontuação
    #4argumento igualpassouobrigatóriainfer.did_staggered.first_treat = 'first_treat'
15. 14Pontuação
    #5argumento igualpassouobrigatóriainfer.did_staggered.unit = 'countyreal'
16. 15Pontuação
    #6argumento igualpassouobrigatóriainfer.did_staggered.control_group = 'never_treated'
17. 16Pontuação
    #7diagnósticopassouobrigatória500 vs 500
18. 17Pontuação
    #8célula de tabela próximapassouobrigatóriaatt_gt[{'g': 2004, 't': 2004}].att = -0.01050324622 vs -0.010503246220963533
19. 18Pontuação
    #9célula de tabela próximapassouregistradaatt_gt[{'g': 2004, 't': 2004}].att = -0.01050324622 vs -0.0105
20. 19Pontuação
    #10célula de tabela próximafalhouregistradaatt_gt[{'g': 2004, 't': 2004}].se = 0.02288236018 vs 0.0246
21. 20Pontuação
    #11célula de tabela próximapassouobrigatóriaatt_gt[{'g': 2004, 't': 2005}].att = -0.0704231581 vs -0.07042315810314909
22. 21Pontuação
    #12célula de tabela próximapassouregistradaatt_gt[{'g': 2004, 't': 2005}].att = -0.0704231581 vs -0.0704
23. 22Pontuação
    #13célula de tabela próximafalhouregistradaatt_gt[{'g': 2004, 't': 2005}].se = 0.0353493381 vs 0.0332
24. 23Pontuação
    #14célula de tabela próximapassouobrigatóriaatt_gt[{'g': 2004, 't': 2006}].att = -0.1372587389 vs -0.13725873888940443
25. 24Pontuação
    #15célula de tabela próximapassouregistradaatt_gt[{'g': 2004, 't': 2006}].att = -0.1372587389 vs -0.1373
26. 25Pontuação
    #16célula de tabela próximafalhouregistradaatt_gt[{'g': 2004, 't': 2006}].se = 0.03770782079 vs 0.0397
27. 26Pontuação
    #17célula de tabela próximapassouobrigatóriaatt_gt[{'g': 2004, 't': 2007}].att = -0.1008113631 vs -0.10081136308540525
28. 27Pontuação
    #18célula de tabela próximapassouregistradaatt_gt[{'g': 2004, 't': 2007}].att = -0.1008113631 vs -0.1008
29. 28Pontuação
    #19célula de tabela próximafalhouregistradaatt_gt[{'g': 2004, 't': 2007}].se = 0.03498817563 vs 0.0357
30. 29Pontuação
    #20célula de tabela próximapassouobrigatóriaatt_gt[{'g': 2006, 't': 2004}].att = 0.006520112424 vs 0.006520112424232907
31. 30Pontuação
    #21célula de tabela próximapassouregistradaatt_gt[{'g': 2006, 't': 2004}].att = 0.006520112424 vs 0.0065
32. 31Pontuação
    #22célula de tabela próximafalhouregistradaatt_gt[{'g': 2006, 't': 2004}].se = 0.02331943921 vs 0.0229
33. 32Pontuação
    #23célula de tabela próximapassouobrigatóriaatt_gt[{'g': 2006, 't': 2005}].att = -0.002750818751 vs -0.0027508187505186858
34. 33Pontuação
    #24célula de tabela próximapassouregistradaatt_gt[{'g': 2006, 't': 2005}].att = -0.002750818751 vs -0.0028
35. 34Pontuação
    #25célula de tabela próximafalhouregistradaatt_gt[{'g': 2006, 't': 2005}].se = 0.0184849003 vs 0.0197
36. 35Pontuação
    #26célula de tabela próximapassouobrigatóriaatt_gt[{'g': 2006, 't': 2006}].att = -0.004594606953 vs -0.004594606952862727
37. 36Pontuação
    #27célula de tabela próximapassouregistradaatt_gt[{'g': 2006, 't': 2006}].att = -0.004594606953 vs -0.0046
38. 37Pontuação
    #28célula de tabela próximafalhouregistradaatt_gt[{'g': 2006, 't': 2006}].se = 0.01822134675 vs 0.0179
39. 38Pontuação
    #29célula de tabela próximapassouobrigatóriaatt_gt[{'g': 2006, 't': 2007}].att = -0.04122447155 vs -0.04122447154621793
40. 39Pontuação
    #30célula de tabela próximapassouregistradaatt_gt[{'g': 2006, 't': 2007}].att = -0.04122447155 vs -0.0412
41. 40Pontuação
    #31célula de tabela próximafalhouregistradaatt_gt[{'g': 2006, 't': 2007}].se = 0.01952403607 vs 0.0192
42. 41Pontuação
    #32célula de tabela próximapassouobrigatóriaatt_gt[{'g': 2007, 't': 2004}].att = 0.03050665558 vs 0.030506655583292096
43. 42Pontuação
    #33célula de tabela próximapassouregistradaatt_gt[{'g': 2007, 't': 2004}].att = 0.03050665558 vs 0.0305
44. 43Pontuação
    #34célula de tabela próximafalhouregistradaatt_gt[{'g': 2007, 't': 2004}].se = 0.0148852205 vs 0.0154
45. 44Pontuação
    #35célula de tabela próximapassouobrigatóriaatt_gt[{'g': 2007, 't': 2005}].att = -0.002725892886 vs -0.002725892886115959
46. 45Pontuação
    #36célula de tabela próximapassouregistradaatt_gt[{'g': 2007, 't': 2005}].att = -0.002725892886 vs -0.0027
47. 46Pontuação
    #37célula de tabela próximafalhouregistradaatt_gt[{'g': 2007, 't': 2005}].se = 0.0172890878 vs 0.0164
48. 47Pontuação
    #38célula de tabela próximapassouobrigatóriaatt_gt[{'g': 2007, 't': 2006}].att = -0.03108711939 vs -0.031087119389688146
49. 48Pontuação
    #39célula de tabela próximapassouregistradaatt_gt[{'g': 2007, 't': 2006}].att = -0.03108711939 vs -0.0311
50. 49Pontuação
    #40célula de tabela próximafalhouregistradaatt_gt[{'g': 2007, 't': 2006}].se = 0.01864182705 vs 0.0184
51. 50Pontuação
    #41célula de tabela próximapassouobrigatóriaatt_gt[{'g': 2007, 't': 2007}].att = -0.02605441072 vs -0.026054410719197237
52. 51Pontuação
    #42célula de tabela próximapassouregistradaatt_gt[{'g': 2007, 't': 2007}].att = -0.02605441072 vs -0.0261
53. 52Pontuação
    #43célula de tabela próximafalhouregistradaatt_gt[{'g': 2007, 't': 2007}].se = 0.01710506351 vs 0.017

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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