
      library(AER)
      library(MASS)


      # generar la variable binaria `old` y agregarla al conjunto de datos
      
      # realizar la regresión y asignarla a `mod_bb`
      
      # imprimir un resumen sólido en la consola


      # generar la variable binaria `old` y agregarla al conjunto de datos
      Boston$old <- as.numeric(Boston$age >= 95)
      
      # realizar la regresión y asignarla a `mod_bb`
      mod_bb <- lm(medv ~ chas*old, data = Boston)
      
      # imprimir un resumen sólido en la consola
      coeftest(mod_bb, vcov. = vcovHC)


      ex() %>% check_object("Boston") %>% check_column("old", col_missing_msg = "Have you added the column `old` to `Boston`?") %>%
      check_equal(incorrect_msg = "Have you correctly calculated the column `old` based on `age`?")
      test_or({
        test_object("mod_bb")
      },{
        f <- ex() %>% override_solution("lm(medv ~ old*chas, data = Boston)") %>% check_function("lm")
        f %>% check_arg("formula") %>% check_equal()
      },{
        f <- ex() %>% override_solution("lm(Boston$medv ~ Boston$chas*Boston$old)") %>% check_function("lm")
        f %>% check_arg("formula") %>% check_equal()
      },{
        f <- ex() %>% override_solution("lm(Boston$medv ~ Boston$old*Boston$chas)") %>% check_function("lm")
        f %>% check_arg("formula") %>% check_equal()
      },{
        f <- ex() %>% override_solution("attach(Boston);lm(medv ~ chas*old)") %>% check_function("lm")
        f %>% check_arg("formula") %>% check_equal()
      },{
        f <- ex() %>% override_solution("attach(Boston);lm(medv ~ old*chas)") %>% check_function("lm")
        f %>% check_arg("formula") %>% check_equal()
      },{
        f <- ex() %>% override_solution("lm(medv ~ chas + old + chas:old, data = Boston)") %>% check_function("lm")
        f %>% check_arg("formula") %>% check_equal()
      },{
        f <- ex() %>% override_solution("lm(medv ~ old + chas + old:chas, data = Boston)") %>% check_function("lm")
        f %>% check_arg("formula") %>% check_equal()
      },{
        f <- ex() %>% override_solution("lm(Boston$medv ~ Boston$chas + Boston$old + Boston$chas:Boston$old)") %>% check_function("lm")
        f %>% check_arg("formula") %>% check_equal()
      },{
        f <- ex() %>% override_solution("lm(Boston$medv ~ Boston$old + Boston$chas + Boston$old:Boston$chas)") %>% check_function("lm")
        f %>% check_arg("formula") %>% check_equal()
      },{
        f <- ex() %>% override_solution("attach(Boston);lm(medv ~ chas + old + chas:old)") %>% check_function("lm")
        f %>% check_arg("formula") %>% check_equal()
      },{
        f <- ex() %>% override_solution("attach(Boston);lm(medv ~ old + chas + old:chas)") %>% check_function("lm")
        f %>% check_arg("formula") %>% check_equal()
      })
      
      ex() %>% check_function("coeftest") %>% {
          check_arg(., "x") %>% check_equal()
          check_arg(., "vcov.") %>% check_equal()
        }
      success_msg("¡Correcto! El efecto estimado de pasar de un suburbio con una baja proporción de edificios antiguos a uno con una alta proporción es -7.13 + 10.55 * chas. Entonces, dependiendo del paso del río Charles por un suburbio, el efecto es positivo o negativo.")