Tarefa causcibench_real.q171

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Pergunta feita ao analista

How does gaining legal access to alcohol at age 21 affect property crime arrest rates?

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1. 0Início

   How does gaining legal access to alcohol at age 21 affect property crime arrest rates?

   carpenter_the_minimum_legal_drinking (agent)

   This dataset contains comprehensive arrest and criminal justice records from California covering individuals around their 21st birthday. The data captures detailed information on various types of criminal offenses, demographic characteristics, and temporal variables that enable analysis of crime patterns across different age groups. The dataset includes both raw count data and population-adjusted rates for multiple categories of criminal activity, spanning from serious felonies to minor infractions and regulatory violations. Variable Descriptions: days_to_21: Number of days until or since an individual's 21st birthday years_to_21: Years until or since turning 21 all: Total number of arrests across all offense categories felony: Count of felony-level arrests misdemeanor: Count of misdemeanor-level arrests fbi_offense_miss: FBI-classified misdemeanor offenses violent: Total violent crime arrests murder: Homicide arrests manslaughter: Manslaughter arrests rape: Sexual assault arrests robbery: Robbery arrests assault: Total assault arrests aggravated_assault: Aggravated assault arrests ot_assault: Other types of assault property: Total property crime arrests burglary: Burglary arrests larceny: Theft arrests mv_theft: Motor vehicle theft arrests stolen_prop_buy_rec_poss: Receiving stolen property arrests vandalism: Property damage arrests ill_drugs: Total drug-related arrests cocaine_opio_sale_manuf: Cocaine/opiate sales and manufacturing arrests mj_sale_manuf: Marijuana sales and manufacturing arrests dang_non_narc_sale_manuf: Other dangerous drug sales/manufacturing arrests cocaine_opio_posses: Cocaine/opiate possession arrests mj_posses: Marijuana possession arrests dang_non_narc_posses: Other dangerous drug possession arrests alcohol: Total alcohol-related arrests drunk_at_risk: Public intoxication arrests dui: Driving under the influence arrests liquor_laws: Liquor law violation arrests drunkeness_pc: Public drunkenness arrests disorderly_cond: Disorderly conduct arrests vagrancy: Vagrancy arrests arson: Arson arrests forgery_counterfit: Forgery and counterfeiting arrests fraud: Fraud arrests embezelment: Embezzlement arrests weapons: Weapons violations prostitution: Prostitution arrests sex_offenses: Sexual offense arrests bookmaking: Illegal gambling arrests all_other_gambling: Other gambling violations aga_family_child: Offenses against family and children curfew_loitering: Curfew and loitering violations runaways: Runaway arrests coded_on_offense: Arrests coded by primary offense county_ordinance: County ordinance violations outside_warrent: Out-of-jurisdiction warrant arrests traffic_violations: Traffic violation arrests hit_run_reckl_driv: Hit and run/reckless driving arrests parole_violation: Parole violation arrests dump_or_litter: Illegal dumping/littering violations trespass: Trespassing arrests violate_court_order: Court order violations failure_to_appear: Failure to appear in court cont_delinq_minor: Contributing to the delinquency of a minor not_classified: Unclassified arrests other_reported: Other reported offenses pop1, pop2, pop3: Population counts for different demographic groups pop_day1, pop_day2, pop_day3: Daily population estimates post: Binary indicator variable that is 1 if age >= 21, i.e., the individual is 21 linear, square, cubic: years_to_21 raised to the power of 1, 2, and 3 respectively linear_post, square_post, cubic_post: Interaction of linear, square, cubic with post birthday_19, birthday_20, birthday_21, birthday_22, birthday_23: Dummy variables for 19th, 20th, 21st, 22nd, and 23rd birthdays birthday_19_1, birthday_20_1, birthday_21_1, birthday_22_1, birthday_23_1: Dummy variables for a day after the 19th, 20th, 21st, 22nd, and 23rd birthdays All variables with _r suffix represent population-adjusted rates corresponding to their count variable counterparts. These include crime-specific rates such as violent_r, property_r, alcohol_r, dui_r, and all_r, among others. The rate variables normalize the raw counts by relevant population denominators.

   file 3083a3b3ad5ad5d822488e13fe0b49c98bcca2c9ab621e4fdec0abab049efd59 · frame —
2. 1Turno do modelo
   call_b60d2a03referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   causcibench_metadata_rule/1: rdd

   Chamadas pedidas

   • infer.ols

     {
       "controls": [
         "days_to_21"
       ],
       "dataset": "carpenter_the_minimum_legal_drinking",
       "x": [
         "post"
       ],
       "y": "property_r"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.olsv0.1.0call_b3eef8e2decidido pelo modeloreexecutável

   Motivo: causcibench_metadata_rule/1: rdd

   Argumentos

   {
     "cluster": null,
     "controls": [
       "days_to_21"
     ],
     "dataset": "carpenter_the_minimum_legal_drinking",
     "fe": [],
     "intercept": true,
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "weights": null,
     "x": [
       "post"
     ],
     "y": "property_r"
   }

   Figuras call_b3eef8e2

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.post Coefficient on post in the regression of property_r	1,41851	1.4185102484089052	1,632251.6322531133747815	-1,78197 – 4,61899

   Diagnósticos

   • n_obs = 2.9222922
   • k = 33
   • r2 = 0,937490.9374903369087264
   • df_resid = 2.9192919
   • se_type = "HC1"
   • n_clusters = —

   Tabelas (1)

   {
     "coefficients": [
       {
         "ci_high": 251.49805171238972,
         "ci_low": 247.83237486037103,
         "coef": 249.66521328638038,
         "p": 0,
         "se": 0.9347510763176634,
         "t": 267.0927261939143,
         "term": "Intercept"
       },
       {
         "ci_high": 4.618994637206755,
         "ci_low": -1.7819741403889442,
         "coef": 1.4185102484089052,
         "p": 0.38489100418578864,
         "se": 1.6322531133747815,
         "t": 0.8690504167433014,
         "term": "post"
       },
       {
         "ci_high": -0.08640015868468867,
         "ci_low": -0.09040607705926615,
         "coef": -0.08840311787197741,
         "p": 0,
         "se": 0.0010215129874895804,
         "t": -86.54135478906883,
         "term": "days_to_21"
       }
     ]
   }

   Digests dos dados

   carpenter_the_minimum_legal_drinking c3e530c6e7505a6b45c809a4b7110c6ff1bcf4917d532cba2bd1fc079433bde4

   sha256 do resultado

   0dff06bd4e8d26a2fbdd1e27965257e886a294cc5c1612d8321cb1b6a28763ef

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_396efd5creferenceFim: 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.post = 1,419.",
       "caveats": [],
       "figure_refs": [
         "call_b3eef8e2:coef.post"
       ]
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
5. 4Guarda
   resposta aceita

   Números encontrados

   1,419

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   1 nova(s) tentativa(s) restante(s)
6. 5Resposta

   Estimativa (infer.ols): coef.post = 1,419.

   Figuras citadas: call_b3eef8e2:coef.post · Parada: answered

   Figura da resposta: call_b3eef8e2:coef.post = 1,418511.4185102484089052 (se 1,632251.6322531133747815)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximafalhouobrigatória1.418510248 vs 4.925

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• época 1, tentativa 1falhoupontuada(exibindo)

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