{"call_id":"call_04ec9d95","run_id":"20261015T171223Z-suite-nist_anova-scripted-7361e5/nist_anova.smls02.agent/e1a1","step":"data","kind":"note","name":"data.transform","version":"","code_version":"2925aeab2262","inputs":{"dataset":"d","index":0,"op":"dummies","args":{"column":"treatment","prefix":"treatment","drop_first":true}},"input_digest":"","dataset_digests":{},"result":null,"outputs":{"rows_before":1809,"rows_after":1809,"frame_sha256_before":"6cb2bb68727fb7574ad811ffd09792f441edb5beb665e6471c627c3d9e84c785","frame_sha256_after":"40265bb6a4240c34fa765dd90a3fe6242ebe37ae0ae601c4865ceef0c45d0cd1"},"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T17:12:23.381979Z","duration_ms":0.8019169326871634,"error":null,"decided_by":"code","rationale":"","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{}}
{"call_id":"call_c5d6ecc5","run_id":"20261015T171223Z-suite-nist_anova-scripted-7361e5/nist_anova.smls02.agent/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.scripted","version":"","code_version":"2925aeab2262","inputs":{"tools":["calc__annuity","calc__breakeven","calc__combine","calc__discount","calc__fiscal_impact","core__atkinson","core__equivalent_variation","core__fgt","core__gini","core__indirect_incidence","core__kakwani","core__mvpf","core__palma","core__quantile_shares","core__reynolds_smolensky","core__scale_effect","core__theil","core__welfare_change","data__cite","data__describe","data__siconfi","data__sidra","evidence__pool_dl","infer__ate","infer__density_test","infer__did_2x2","infer__did_staggered","infer__event_study","infer__fe","infer__fisher_exact","infer__iv_2sls","infer__linear_hypothesis","infer__mde","infer__neyman","infer__ols","infer__power","infer__randomization_inference","infer__rdd","infer__sample_size","infer__twfe","infer__wald_late","submit_answer"],"exemplars":[]},"input_digest":"","dataset_digests":{},"result":null,"outputs":null,"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T17:12:23.395441Z","duration_ms":0.0,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"reference","prompt":"# Role\nYou are MAGI's economist. A municipal analyst in Brazil asks a question; you answer it by choosing registered tools and their arguments. The tools compute every number. Your job is to recognise how the data were generated, pick the tool whose design matches, read what it reports, and say what the result means for the analyst.\n\n# Rules\n1. You never compute a number. Every number in your answer must have been stated by a tool call in this run (a figure, its standard error or interval, a diagnostic, a table cell) or written in the question. No mental arithmetic: for a ratio, difference, sum, product, percent change or share of figures already computed, call calc.combine; to restate a log-point coefficient as a percent or an effect in standard deviations, call core.scale_effect.\n2. Before estimating, work out the design: how units came to be treated (a lottery, an offer with partial take-up, a threshold of a running variable, a policy that started on one date or on different dates, self-selection), what a row is, and which columns hold the outcome, treatment, unit, time, running variable, instrument, cluster and survey weight. Call data.describe when the columns are unclear. Then choose the tool whose \"Use when\" matches and heed its \"Avoid when\".\n3. Read the diagnostics, warnings and \"Check after\" items a result returns before you trust a figure. If a diagnostic says the design fails — a weak instrument, sorting at the cutoff, few clusters, non-parallel pre-trends — say so plainly and do not present the estimate as causal.\n4. If no tool fits the design, or the data cannot identify what is asked, decline and say what is missing. Never invent a design, a column or a number.\n5. A tool that errors or declines returns its reason; fix the arguments and call again, or choose another tool.\n6. Before each tool call, write one sentence on why this tool and these arguments fit the design. It is recorded as your rationale.\n7. Finish with submit_answer. 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.\n\n# Datasets\nPass a dataset's id as the `dataset` argument.\n{\n \"id\": \"d\",\n \"rows\": 1809,\n \"description\": \"NIST StRD SmLs02: 1809 observations; treatment = the treatment level (1 to 9), response = the response, treatment_2 … treatment_9 = 1 when the row is at that treatment level (level 1 is the reference)\",\n \"columns\": {\n  \"treatment\": \"int64\",\n  \"response\": \"float64\",\n  \"treatment_2\": \"int64\",\n  \"treatment_3\": \"int64\",\n  \"treatment_4\": \"int64\",\n  \"treatment_5\": \"int64\",\n  \"treatment_6\": \"int64\",\n  \"treatment_7\": \"int64\",\n  \"treatment_8\": \"int64\",\n  \"treatment_9\": \"int64\"\n }\n}\n\n# Question\nNIST Statistical Reference Datasets, one-way analysis of variance, problem SmLs02: generated data with 1 constant leading digits; one factor (treatment) with 9 levels and 1809 observations in a balanced design, model y_ij = mu + tau_i + e_ij. Dataset d holds the factor treatment (levels 1 to 9), the response response, and indicator columns treatment_2 to treatment_9 (1 when the row is at that level; level 1 is the reference). 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treatment_5 = 0; treatment_6 = 0; treatment_7 = 0; treatment_8 = 0; treatment_9 = 0","se":null,"ci_low":null,"ci_high":null,"ci_level":null,"derivation":{"steps":[{"text_pt":"Estatística de Wald das 8 restrições, com a covariância classical (homoskedastic) standard errors.","formula":"","terms":[{"symbol":"m","value":8.0,"unit":"restrições","note":""}],"result":1607.999999999996,"unit":""},{"text_pt":"Estatística F: a de Wald dividida pelo número de restrições.","formula":"F = W / m","terms":[{"symbol":"W","value":1607.999999999996,"unit":"","note":""},{"symbol":"m","value":8.0,"unit":"restrições","note":""}],"result":200.9999999999995,"unit":""}],"note_pt":""}},{"key":"p_value","value":4.037141885758291e-243,"unit":"probability","label":"p-value of treatment_2 = 0; treatment_3 = 0; treatment_4 = 0; treatment_5 = 0; treatment_6 = 0; treatment_7 = 0; treatment_8 = 0; treatment_9 = 0","se":null,"ci_low":null,"ci_high":null,"ci_level":null,"derivation":{"steps":[{"text_pt":"Probabilidade de uma F(8, 1800) exceder a estatística observada.","formula":"","terms":[],"result":4.037141885758291e-243,"unit":"probabilidade"}],"note_pt":""}}],"diagnostics":[{"name":"statistic","value":"F","flag":false,"note":"F = W/m against F(m, n − k)"},{"name":"df_numerator","value":8,"flag":false,"note":"number of restrictions"},{"name":"df_denominator","value":1800,"flag":false,"note":"residual degrees of freedom n − k"},{"name":"se_type","value":"classical","flag":false,"note":"classical (homoskedastic) standard errors"},{"name":"n_obs","value":1809,"flag":false,"note":""},{"name":"n_clusters","value":null,"flag":false,"note":"errors not clustered"}],"tables":{"restrictions":[{"restriction":"treatment_2 = 0","estimate":-0.10000000000000009,"hypothesized":0.0},{"restriction":"treatment_3 = 0","estimate":0.09999999999999998,"hypothesized":0.0},{"restriction":"treatment_4 = 0","estimate":-0.10000000000000009,"hypothesized":0.0},{"restriction":"treatment_5 = 0","estimate":0.09999999999999998,"hypothesized":0.0},{"restriction":"treatment_6 = 0","estimate":-0.10000000000000009,"hypothesized":0.0},{"restriction":"treatment_7 = 0","estimate":0.09999999999999998,"hypothesized":0.0},{"restriction":"treatment_8 = 0","estimate":-0.10000000000000009,"hypothesized":0.0},{"restriction":"treatment_9 = 0","estimate":0.09999999999999998,"hypothesized":0.0}],"coefficients":[{"term":"Intercept","coef":1.4,"se":0.007053456158586019,"t":198.48425630260851,"p":0.0,"ci_low":1.3861661778643664,"ci_high":1.4138338221356335},{"term":"treatment_2","coef":-0.10000000000000009,"se":0.009975093361076447,"t":-10.024968827881601,"p":4.7048753115723204e-23,"ci_low":-0.1195639788836703,"ci_high":-0.08043602111632987},{"term":"treatment_3","coef":0.09999999999999998,"se":0.009975093361076453,"t":10.024968827881585,"p":4.704875311573031e-23,"ci_low":0.08043602111632975,"ci_high":0.11956397888367021},{"term":"treatment_4","coef":-0.10000000000000009,"se":0.009975093361076319,"t":-10.024968827881729,"p":4.704875311566536e-23,"ci_low":-0.11956397888367006,"ci_high":-0.08043602111633012},{"term":"treatment_5","coef":0.09999999999999998,"se":0.009975093361076279,"t":10.024968827881759,"p":4.7048753115651787e-23,"ci_low":0.0804360211163301,"ci_high":0.11956397888366986},{"term":"treatment_6","coef":-0.10000000000000009,"se":0.00997509336107639,"t":-10.024968827881658,"p":4.7048753115697243e-23,"ci_low":-0.1195639788836702,"ci_high":-0.08043602111632998},{"term":"treatment_7","coef":0.09999999999999998,"se":0.00997509336107638,"t":10.024968827881658,"p":4.7048753115697243e-23,"ci_low":0.08043602111632989,"ci_high":0.11956397888367007},{"term":"treatment_8","coef":-0.10000000000000009,"se":0.009975093361076343,"t":-10.024968827881706,"p":4.704875311567541e-23,"ci_low":-0.1195639788836701,"ci_high":-0.08043602111633008},{"term":"treatment_9","coef":0.09999999999999998,"se":0.00997509336107632,"t":10.024968827881716,"p":4.704875311567068e-23,"ci_low":0.08043602111633001,"ci_high":0.11956397888366994}]},"datasets":{},"warnings":[],"assumptions":["errors are independent and have the same variance for every observation"],"notes":""},"outputs":null,"output_digest":"5e57dc970d65449a9f30a4a04beb6faa44eac37137fe51d05eea48e2b27776e8","params_versions":[],"sources":[],"started_at":"2026-10-15T17:12:23.397931Z","duration_ms":5.626500118523836,"error":null,"decided_by":"model","rationale":"With classical errors, the joint test that every indicator's coefficient is zero is the one-way ANOVA F.","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_c5d6ecc5","llm_name":"infer__linear_hypothesis","vendor_id":"script_1","result_for_model_ref":"86844b2933123c67b2469f4d2aff664e4c120645ca2872a6a042fa409ccd95aa"}}
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