Tarefa paper_repro.dehejia_wahba_1999.tab2_pa_c1.design

Veredito

Pergunta feita ao analista

Did the National Supported Work job-training program raise the 1978 earnings of the men randomly assigned to it? Use LaLonde's experimental sample of male participants in the file and report the effect in 1982 dollars.

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

   Did the National Supported Work job-training program raise the 1978 earnings of the men randomly assigned to it? Use LaLonde's experimental sample of male participants in the file and report the effect in 1982 dollars.

   nsw (agent)

   LaLonde's (1986) National Supported Work experimental sample of male participants, as posted by Rajeev Dehejia (nsw.dta): one row per man (722 rows: the treated and the experimental controls). Columns: data_id, the sample label; treat, 1 for men randomly assigned to the NSW job training program and 0 for the experimental controls; age, in years; education, years of schooling; black, 1 if black; hispanic, 1 if Hispanic; married, 1 if married; nodegree, 1 for no high school degree; re75, real earnings in 1975; re78, real earnings in 1978 (both in 1982 dollars).

   file 3ee014cb0010e2e7fb990ac497f4217968733e2c9abfa4de84e83f524898c24e · frame —
2. 1Turno do modelo
   call_8b7e78cagemini-3.8-flashFim: STOP7.492 ms14295 de entrada · 63 de saída · 411 de raciocínio · 0 em cache

   Texto visível

   I will check the distribution and summary statistics of the experimental sample to verify the outcome and treatment columns.

   Resumo do raciocínio, como declarado pelo modelo

   **My Thought Process on Analyzing the National Supported Work Program's Impact** Okay, I need to figure out if the National Supported Work (NSW) job-training program actually boosted the 1978 earnings of the men who were randomly assigned to it. And critically, I need to express this effect in 1982 dollars. First, I've confirmed the request is in English, so my response will be too. No need to translate to Portuguese despite that one rule about decimal commas – the language of the *question* dictates the language of the *answer*. Now, let's look at the data I have available. The `nsw` dataset seems perfect for this. It has 722 participants, and I can see columns for `treat` (indicating who was in the training program, 1, and who wasn't, 0 – this is key for randomization) and `re78`, which is exactly what I need: real earnings in 1978, already adjusted to 1982 dollars. This saves me a conversion step. For analyzing randomized experiments like this, where I want to estimate the Average Treatment Effect (ATE), I have a couple of options. The `infer.ate` tool is designed for estimating the difference in means between randomly assigned treatment and control groups. It can optionally include baseline covariates and use robust standard errors, which is generally a good practice. Alternatively, `infer.neyman` offers a more conservative variance estimate for completely randomized experiments. Given the problem states random assignment, `infer.ate` seems like a robust choice, and I'll consider its options. I'll start by inspecting the `infer.ate` tool and how it works, and I might need `data.describe` to get a feel for the data distribution before I jump into the estimation. My plan is to use `infer.ate` to directly calculate the difference in `re78` between the `treat` group and the control group. This tool is well-suited because the problem explicitly states random assignment, and `infer.ate` is built to handle this, providing a direct estimate of the treatment effect on the outcome variable.

   Chamadas pedidas

   • data.describe

     {
       "columns": [
         "treat",
         "re78",
         "re75",
         "age",
         "education"
       ],
       "dataset": "nsw"
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
3. 2Ferramenta
   data.describev0.1.0call_09f093b4decidido pelo modeloreexecutável

   Motivo: I will check the distribution and summary statistics of the experimental sample to verify the outcome and treatment columns.

   Argumentos

   {
     "columns": [
       "treat",
       "re78",
       "re75",
       "age",
       "education"
     ],
     "dataset": "nsw"
   }

   Figuras call_09f093b4

   Chave	Valor	Valor bruto	Erro-padrão	IC
   n_rows Rows in the dataset	722 rows	722	—	—

   Tabelas (1)

   {
     "columns": [
       {
         "column": "treat",
         "dtype": "int8",
         "max": 1,
         "mean": 0.4113573407202216,
         "min": 0,
         "n_missing": 0,
         "n_unique": 2,
         "sd": 0.4924208784224852
       },
       {
         "column": "re78",
         "dtype": "float32",
         "max": 60307.9296875,
         "mean": 5454.635843541153,
         "min": 0,
         "n_missing": 0,
         "n_unique": 524,
         "sd": 6252.943412506566
       },
       {
         "column": "re75",
         "dtype": "float32",
         "max": 37431.66015625,
         "mean": 3042.896584687801,
         "min": 0,
         "n_missing": 0,
         "n_unique": 424,
         "sd": 5066.14338191606
       },
       {
         "column": "age",
         "dtype": "int8",
         "max": 55,
         "mean": 24.520775623268698,
         "min": 17,
         "n_missing": 0,
         "n_unique": 35,
         "sd": 6.625947017405377
       },
       {
         "column": "education",
         "dtype": "int8",
         "max": 16,
         "mean": 10.267313019390581,
         "min": 3,
         "n_missing": 0,
         "n_unique": 14,
         "sd": 1.7047736628915846
       }
     ]
   }

   Digests dos dados

   nsw ba7f9a94cfaca73d179b6160b5517fb121115bdebfed891d90817b561831824d

   sha256 do resultado

   0a86bb13088d877e755a9bf655b9cb8564c07b06b17426a02c2ff9fd5a651b00

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_b82c31adgemini-3.8-flashFim: STOP5.075 ms14984 de entrada · 41 de saída · 55 de raciocínio · 12510 em cache

   Texto visível

   I will load the experiments-and-power skill to review recommended procedures and reporting standards for experimental evaluations.

   Resumo do raciocínio, como declarado pelo modelo

   **My Thought Process on Experimental Analysis and Real Earnings** Right, so I'm looking at this dataset, and the first thing that jumps out is the variable `treat`. I recognize this as a binary indicator, likely signifying a treatment or control group. The second key variable I'm focusing on is `re78`, which I understand to represent real earnings specifically for the year 1978. This immediately suggests a potential causal inference scenario, where I'll be examining the impact of the `treat` variable on earnings, particularly in that historical context. To delve into this further, I have a couple of immediate pathways in mind. The first is to load a pre-packaged set of experimental configurations, specifically those related to "experiments-and-power." This seems like a robust approach, as it likely encapsulates best practices and relevant methodologies for analyzing experimental data, potentially including power calculations and various estimation strategies. Alternatively, if I want to get straight to the core estimation without the overhead of a full experimental setup, I can directly invoke `infer.ate`. This function is presumably designed to compute the Average Treatment Effect, which is precisely what I'm interested in – the average impact of the treatment on the outcome variable, `re78`. I'll need to decide which of these approaches is most efficient and informative for my current analytical goals, weighing the comprehensiveness of the `load_skill` option against the directness of `infer.ate`.

   Chamadas pedidas

   • load_skill (meta-ferramenta)

     {
       "name": "experiments-and-power"
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
5. 4Habilidade
   experiments-and-powerTexto da habilidade
6. 5Turno do modelo
   call_78edd08cgemini-3.8-flashFim: STOP6.380 ms15621 de entrada · 62 de saída · 156 de raciocínio · 12613 em cache

   Texto visível

   I will estimate the average treatment effect of the job-training program on 1978 real earnings using a difference in means with robust standard errors.

   Resumo do raciocínio, como declarado pelo modelo

   **My Thought Process on Analyzing the NSW Program's Impact** Alright, the core task here is to determine if the National Supported Work (NSW) job-training program demonstrably increased the 1978 earnings of the men who participated. The prompt specifically mentions using LaLonde's experimental sample and reporting the effect in 1982 dollars, which I'll need to account for later. My immediate inclination is to leverage the `infer.ate` function, as it's designed for estimating average treatment effects. I'm also aware of `infer.neyman`, which might be relevant if I need to consider alternative estimation strategies, particularly in the presence of potential unobserved confounders or if I want to explore robust inference methods. So, first things first, I need to load and prepare the data. I'll be using LaLonde's experimental sample, and the relevant variables are clearly indicated: `dataset="nsw"`, the outcome variable `y="re78"` (earnings in 1978), and the treatment indicator `treatment="treat"`. Now, a critical decision point: should I include covariates in my analysis or not? The prompt mentions "men randomly assigned to it," which strongly suggests that the randomization itself is the primary mechanism to ensure balance between treatment and control groups. Therefore, a naive comparison of means between the treated and control groups, *without* any covariates, should provide a valid estimate of the average treatment effect on the treated (ATT) given the experimental design. However, even in a randomized experiment, including covariates can improve the precision of the estimate by explaining some of the variability in the outcome. It can also help to demonstrate that the randomization was effective, by showing that the covariate distributions are similar across groups. So, while a covariate-free estimate is theoretically sound and a good starting point, I should also consider running the analysis with covariates to see if it yields a more precise estimate and to potentially gain further confidence in the robustness of the findings. Given this, my plan is to proceed in stages: 1. **Run `infer.ate` without any covariates.** This will serve as my baseline estimate, directly leveraging the randomization. 2. **Then, I will explore running `infer.ate` *with* covariates.** This will allow me to assess the impact of controlling for observable characteristics on the precision of the treatment effect estimate. 3. **Finally, I will consider `infer.neyman`.** This function might be useful for alternative inference strategies, especially if I want to investigate different assumptions or robustness checks beyond what `infer.ate` directly offers. For now, the immediate next step is to execute the `infer.ate` function with `dataset="nsw"`, `y="re78"`, and `treatment="treat"`, and crucially, *without* specifying any covariates. This will give me the most straightforward estimate of the program's impact based solely on the randomization.

   Chamadas pedidas

   • infer.ate

     {
       "dataset": "nsw",
       "treatment": "treat",
       "y": "re78"
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
7. 6Ferramenta
   infer.atev0.1.0call_0cff21eadecidido pelo modeloreexecutável

   Motivo: I will estimate the average treatment effect of the job-training program on 1978 real earnings using a difference in means with robust standard errors.

   Argumentos

   {
     "cluster": null,
     "covariates": [],
     "dataset": "nsw",
     "level": 0.95,
     "treatment": "treat",
     "weights": null,
     "y": "re78"
   }

   Figuras call_0cff21ea

   Chave	Valor	Valor bruto	Erro-padrão	IC
   ate Average effect of treat on re78	886,304	886.3037224514105	488,139488.13853147430973	-70,4302 – 1.843,04

   Diagnósticos

   • n_treated = 297297
   • n_control = 425425
   • se_type = "HC1"
   • n_clusters = —

   Digests dos dados

   nsw ba7f9a94cfaca73d179b6160b5517fb121115bdebfed891d90817b561831824d

   sha256 do resultado

   1b8792660e4e354643c82164ffebde1bb5b9e201750c1561ad99564318c36bf9

   Resultado mostrado ao modelo
8. 7Turno do modelo
   call_353596d8gemini-3.8-flashFim: STOP4.652 ms15948 de entrada · 50 de saída · 79 de raciocínio · 12614 em cache

   Texto visível

   I will calculate the conservative Neyman design-based difference in means for the experimental sample.

   Resumo do raciocínio, como declarado pelo modelo

   **My Thought Process Regarding Inferential Analysis** Alright, so my initial inferential analysis, specifically using what I'm calling `infer.ate` – a proprietary function, I presume, given its naming – yielded an Average Treatment Effect (ATE) estimate. The raw figure here is 886.304. I'm also noting the associated standard error (SE) of 488.139, and a 95% confidence interval (CI) that spans from -70.4302 to 1843.04. However, before I commit to interpreting this ATE, a crucial nagging thought arises: the methodology. Is `infer.ate` the *only* or the *most robust* inferential framework I should be considering? My expertise immediately flags the need for cross-validation or at least an alternative perspective. Therefore, my next logical step, the thought that's now occupying my attention, is to run a comparative analysis. Specifically, I need to see what `infer.neyman` – another, presumably distinct, inferential engine – would produce under the same data conditions. This comparison will be invaluable in assessing the stability and reliability of the initial ATE estimate and for potentially uncovering different facets of the treatment effect.

   Chamadas pedidas

   • infer.neyman

     {
       "dataset": "nsw",
       "treatment": "treat",
       "y": "re78"
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
9. 8Ferramenta
   infer.neymanv0.1.0call_c66d8043decidido pelo modeloreexecutável

   Motivo: I will calculate the conservative Neyman design-based difference in means for the experimental sample.

   Argumentos

   {
     "dataset": "nsw",
     "level": 0.95,
     "treatment": "treat",
     "y": "re78"
   }

   Figuras call_c66d8043

   Chave	Valor	Valor bruto	Erro-padrão	IC
   ate Difference in mean re78, treated minus control	886,304	886.3037224514028	488,205488.2045285367423	-70,5596 – 1.843,17

   Diagnósticos

   • n_treated = 297297
   • n_control = 425425
   • z = 1,815441.8154352748587819
   • p_value = 0,0694570.06945704427508079

   Digests dos dados

   nsw ba7f9a94cfaca73d179b6160b5517fb121115bdebfed891d90817b561831824d

   sha256 do resultado

   86f24684c9054c48d9a5a68ddcf0037cb89ae256e9755e2b9d2eff9f66a86138

   Resultado mostrado ao modelo
10. 9Turno do modelo
    call_cb096770gemini-3.8-flashFim: STOP4.500 ms16206 de entrada · 82 de saída · 57 de raciocínio · 12607 em cache

    Texto visível

    I will estimate the treatment effect controlling for baseline covariates to improve precision.

    Resumo do raciocínio, como declarado pelo modelo

    **My Thought Process Regarding Covariate Inclusion in the Analysis** My immediate inclination is to explore the impact of including baseline covariates in our analysis. Specifically, I want to assess whether incorporating variables such as age, education level, and indicators for race/ethnicity (black, hispanic), marital status (married), and prior degree attainment (nodegree), along with economic status in 1975 (re75), will lead to a more precise estimation of the Average Treatment Effect (ATE). My objective here isn't necessarily to fundamentally alter the core findings, but rather to rigorously investigate if these standard demographic and socioeconomic factors, when controlled for, enhance the statistical power and reliability of our results. I plan to run the `infer.ate` function with these covariates to determine if their inclusion is a standard practice in this type of analysis or if they offer specific, informative insights that are worth highlighting in our discussion. This will help me ascertain whether mentioning the inclusion of covariates is not only good practice but also substantively contributes to a deeper understanding of the treatment effect's heterogeneity or precision.

    Chamadas pedidas

    • infer.ate

      {
        "covariates": [
          "age",
          "education",
          "black",
          "hispanic",
          "married",
          "nodegree",
          "re75"
        ],
        "dataset": "nsw",
        "treatment": "treat",
        "y": "re78"
      }

    Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
11. 10Ferramenta
    infer.atev0.1.0call_1abbf079decidido pelo modeloreexecutável

    Motivo: I will estimate the treatment effect controlling for baseline covariates to improve precision.

    Argumentos

    {
      "cluster": null,
      "covariates": [
        "age",
        "education",
        "black",
        "hispanic",
        "married",
        "nodegree",
        "re75"
      ],
      "dataset": "nsw",
      "level": 0.95,
      "treatment": "treat",
      "weights": null,
      "y": "re78"
    }

    Figuras call_1abbf079

    Chave	Valor	Valor bruto	Erro-padrão	IC
    ate Average effect of treat on re78	806,511	806.5112869154375	485,196485.19563132970865	-144,455 – 1.757,48

    Diagnósticos

    • n_treated = 297297
    • n_control = 425425
    • se_type = "HC1"
    • n_clusters = —

    Digests dos dados

    nsw ba7f9a94cfaca73d179b6160b5517fb121115bdebfed891d90817b561831824d

    sha256 do resultado

    4d8d50dfa0cc0db9f0a3610bf8c79f4d2b2f4cca8265b40fb9fe728926c13a16

    Resultado mostrado ao modelo
12. 11Turno do modelo
    call_139abb42gemini-3.8-flashFim: STOP5.322 ms16432 de entrada · 37 de saída · 51 de raciocínio · 12578 em cache

    Texto visível

    I will load the reporting-numbers skill to review formatting and citation requirements for the final answer.

    Resumo do raciocínio, como declarado pelo modelo

    **My Thought Process on Data Analysis and Reporting Standards** Before I dive too deep into the specifics of the data, I want to ensure I'm adhering to all our established reporting guidelines. That's why my immediate instinct is to load the `reporting-numbers` skill. This is a critical step to guarantee that any analysis I perform, and the way I present it, aligns perfectly with our organizational standards. Once I've got the `reporting-numbers` skill loaded and ready, I'll then pivot to understanding the core statistical properties of the data. My next thought is to examine the raw means. This is a fundamental metric, and it will give me a baseline understanding of the central tendency for each variable. However, I anticipate that for a truly comprehensive view, I might need more than just the raw means. Therefore, I'll also be looking to see if functions like `infer.ols` (likely related to Ordinary Least Squares regression, which would offer more nuanced statistical insights like coefficients, standard errors, and R-squared values) or `data.describe` (which typically provides a more detailed statistical summary including count, mean, standard deviation, min, max, and quartiles) offer additional, more granular details if needed. This layered approach ensures I get a robust understanding of the data's characteristics before proceeding with any deeper analysis or interpretation.

    Chamadas pedidas

    • load_skill (meta-ferramenta)

      {
        "name": "reporting-numbers"
      }

    Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
13. 12Habilidade
    reporting-numbersTexto da habilidade
14. 13Turno do modelo
    call_b7907bb3gemini-3.8-flashFim: STOP18.113 ms16956 de entrada · 357 de saída · 1240 de raciocínio · 12651 em cache

    Resumo do raciocínio, como declarado pelo modelo

    **My Thought Process Regarding the National Supported Work Program's Impact** When I first approached this question about the National Supported Work job-training program, my primary focus was to meticulously dissect the prompt's requirements. I needed to determine if the program demonstrably increased the 1978 earnings of the men randomly assigned to it, and crucially, to report this effect in 1982 dollars. My first step was to examine the raw output from the `infer.ate` function (specifically `call_0cff21ea`). This gave me the core figures: an estimated Average Treatment Effect (ATE) of 886.304, with a standard error of 488.139, and a 95% confidence interval that ranged from -70.4302 to 1843.04. I also noted the sample sizes: 297 individuals in the treated group and 425 in the control group, totaling 722 participants. Next, I turned to `infer.neyman` (`call_c66d8043`) to see how the Neyman design-based approach would handle this. The ATE it produced was identical to the `infer.ate` result (886.304), with a very similar standard error (488.205) and confidence interval ([-70.5596, 1843.17]). Importantly, this function provided a p-value of approximately 0.069457, which flagged for me that the result wasn't statistically significant at the conventional 5% level, though it was close to the 10% threshold. I also considered the impact of covariates by running `infer.ate` with them included (`call_1abbf079`). This yielded a slightly different point estimate of 806.511, with a standard error of 485.196 and a confidence interval of [-144.455, 1757.48]. Synthesizing these findings, I observed that both the unadjusted (ATE) and covariate-adjusted estimates indicated a positive impact of the program on earnings. The point estimates hovered around $886.30 or $806.51 in 1982 dollars. However, the critical takeaway was the confidence intervals. Both the unadjusted and covariate-adjusted intervals straddled zero. This meant that, statistically, I could not confidently conclude that the observed increase in earnings was truly attributable to the program at the 5% significance level. While the effect was directionally positive, it wasn't robust enough to reject the null hypothesis of no effect. The p-value of ~0.069 reinforced this – it was on the edge, but not definitively significant. Then, I had to carefully re-read the instructions regarding the output format, particularly the instruction: "Write the answer in the language of the question (Portuguese: decimal comma), short and direct: the estimate with its uncertainty, the design that identifies it, and what could undermine it. List in figure_refs every figure you quote, as '<call_id>:<key>' (each tool result carries its call_id), and put diagnostics that limit the conclusion in caveats. Write numbers as the tools stated them; do not quote rule-of-thumb thresholds as numbers." This instruction caused a moment of deliberation. The *question* itself was in English. So, "in the language of the question" meant I should respond in English. The parenthetical note "(Portuguese: decimal comma)" was a bit of a curveball. I interpreted it as a conditional instruction: *if* the language of the question were Portuguese, I would use a decimal comma. Since it was English, I would use the standard English decimal point. Finally, I compiled the necessary elements for the answer: * **The estimate:** The point estimate of the effect, which was around $886.30 in 1982 dollars for the unadjusted analysis. * **Its uncertainty:** The 95% confidence interval, which spanned zero. * **The design:** The use of a randomized experiment, as implied by the prompt and confirmed by the ATE calculations. * **What could undermine it:** The fact that the confidence interval included zero (lack of statistical significance at the 5% level), and potential issues like non-compliance, attrition, or the specific characteristics of the experimental sample in the LaLonde data. I made sure to meticulously check all the numbers against the tool outputs and to correctly format the `figure_refs` and `caveats` as instructed. I also double-checked that I was quoting numbers exactly as the tools presented them, without rounding or applying arbitrary thresholds. All necessary information seemed to be present and accounted for.

    Chamadas pedidas

    • submit_answer (meta-ferramenta)

      {
        "answer": "In the experimental sample of 722 men (297 treated and 425 controls), the estimated average treatment effect of the National Supported Work program on 1978 real earnings is an increase of 886.304 1982 dollars (robust standard error: 488.139; 95% confidence interval: [-70.4302, 1843.04]). \n\nControlling for baseline characteristics (age, education, race, marital status, degree status, and 1975 earnings) yields a similar point estimate of 806.511 1982 dollars (robust standard error: 485.196; 95% confidence interval: [-144.455, 1757.48]).\n\nWhile the point estimates suggest that the program raised 1978 earnings by roughly 800 to 886 dollars, the 95% confidence intervals include zero, meaning the effect is not statistically significant at the conventional 5% level.",
        "caveats": [
          "The 95% confidence intervals include zero in both specifications (unadjusted p-value ≈ 0.069), meaning the effect cannot be distinguished from zero at the conventional 5% significance level.",
          "High variance in post-program earnings limits the statistical power of the sample to detect moderate positive earnings impacts."
        ],
        "figure_refs": [
          "call_0cff21ea:ate",
          "call_1abbf079:ate"
        ]
      }

    Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
15. 14Guarda
    resposta aceita

    Números encontrados

    722 · 297 · 425 · 1978 · 886.304 · 1982 · 488.139 · 95% · -70.4302 · 1843.04 · 1975 · 806.511 · 1982 · 485.196 · 95% · -144.455 · 1757.48 · 1978 · 800 · 886 · 95% · 5% · 95% · 0.069 · 5%

    Sem lastro

    —

    Não citados

    —

    1 nova(s) tentativa(s) restante(s)
16. 15Resposta

    In the experimental sample of 722 men (297 treated and 425 controls), the estimated average treatment effect of the National Supported Work program on 1978 real earnings is an increase of 886.304 1982 dollars (robust standard error: 488.139; 95% confidence interval: [-70.4302, 1843.04]). Controlling for baseline characteristics (age, education, race, marital status, degree status, and 1975 earnings) yields a similar point estimate of 806.511 1982 dollars (robust standard error: 485.196; 95% confidence interval: [-144.455, 1757.48]). While the point estimates suggest that the program raised 1978 earnings by roughly 800 to 886 dollars, the 95% confidence intervals include zero, meaning the effect is not statistically significant at the conventional 5% level. Ressalvas: The 95% confidence intervals include zero in both specifications (unadjusted p-value ≈ 0.069), meaning the effect cannot be distinguished from zero at the conventional 5% significance level. High variance in post-program earnings limits the statistical power of the sample to detect moderate positive earnings impacts.

    • The 95% confidence intervals include zero in both specifications (unadjusted p-value ≈ 0.069), meaning the effect cannot be distinguished from zero at the conventional 5% significance level.
    • High variance in post-program earnings limits the statistical power of the sample to detect moderate positive earnings impacts.

    Figuras citadas: call_0cff21ea:ate, call_1abbf079:ate · Parada: answered

    Figura da resposta: call_0cff21ea:ate = 886,304886.3037224514105 (se 488,139488.13853147430973)
17. 16Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
18. 17Pontuação
    #1figura da resposta escolhidapassouobrigatóriaanswer figure call_0cff21ea:ate (infer.ate)
19. 18Pontuação
    #2uma entrepassouobrigatóriatool_called(infer.ols): answer call is infer.ate | tool_called(infer.ate): ok | tool_called(infer.neyman): answer call is infer.ate
20. 19Pontuação
    #3argumento igualpassouobrigatórianot applicable: the answer call is infer.ate, not infer.ols
21. 20Pontuação
    #4argumento igualpassouobrigatórianot applicable: the answer call is infer.ate, not infer.ols
22. 21Pontuação
    #5argumento igualpassouobrigatóriainfer.ate.y = 're78'
23. 22Pontuação
    #6argumento igualpassouobrigatóriainfer.ate.treatment = 'treat'
24. 23Pontuação
    #7argumento igualpassouobrigatórianot applicable: the answer call is infer.ate, not infer.neyman
25. 24Pontuação
    #8argumento igualpassouobrigatórianot applicable: the answer call is infer.ate, not infer.neyman
26. 25Pontuação
    #9argumento igualpassouregistradanot applicable: the answer call is infer.ate, not infer.ols
27. 26Pontuação
    #10argumento igualpassouregistradanot applicable: the answer call is infer.ate, not infer.ols
28. 27Pontuação
    #11argumento igualpassouregistradanot applicable: the answer call is infer.ate, not infer.ols
29. 28Pontuação
    #12diagnósticopassouregistradanot applicable: the answer call is infer.ate, not infer.ols
30. 29Pontuação
    #13figura da resposta próximapassouregistrada886.3037225 vs 886.3037224514105
31. 30Pontuação
    #14figura da resposta próximafalhouregistrada488.1385315 vs 472.0862575945186
32. 31Pontuação
    #15figura da resposta próximapassouregistrada886.3037225 vs 886.0
33. 32Pontuação
    #16figura da resposta próximafalhouregistrada488.1385315 vs 472.0

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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