{
  "context": "This evaluation assesses how well the engineer uses the spreg package to access AIC and BIC model selection criteria from fitted regression models. The focus is on proper usage of spreg's OLS class and accessing its model fit statistics attributes.",
  "type": "weighted_checklist",
  "checklist": [
    {
      "name": "Uses spreg.OLS",
      "description": "Imports and uses the spreg.OLS class to fit the regression models rather than using alternative regression packages",
      "max_score": 25
    },
    {
      "name": "Accesses AIC attribute",
      "description": "Correctly accesses the AIC (Akaike Information Criterion) value from the fitted OLS model object using the appropriate attribute (e.g., model.aic)",
      "max_score": 20
    },
    {
      "name": "Accesses BIC attribute",
      "description": "Correctly accesses the BIC (Bayesian Information Criterion) value from the fitted OLS model object using the appropriate attribute (e.g., model.schwarz or model.bic)",
      "max_score": 20
    },
    {
      "name": "Proper model specification",
      "description": "Correctly specifies the OLS models by passing the dependent variable (y) and independent variables (x) as separate arrays/matrices, following spreg's API conventions",
      "max_score": 15
    },
    {
      "name": "Criterion comparison logic",
      "description": "Implements correct comparison logic that selects the model with the lowest AIC/BIC value (understanding that lower values indicate better fit)",
      "max_score": 10
    },
    {
      "name": "Multiple model fitting",
      "description": "Successfully fits all three model specifications with different combinations of features as specified in the requirements",
      "max_score": 10
    }
  ]
}