7231 B H 98' \\'3 \"if. 5. You are interested in understanding the maternal characteristics that are associated with low birthweight babies. Being born low birthweight is associated with greater likelihood of neonatal and infant mortality, but also has adverse later life implications for those children, such as worse educational and labor market outcomes. The variables in the data are defined in the following ways: - Iow_bw = 1 if the child was born underweight ( F n 0..." Residual I'll. 956351 2 . 595 . IS'MIMI R-squl red I I . I!!! Art] R-squared I LII" Total 115.3361 2.599 .6846?!" Root llSE I .2396! lw_bu Conuent Std. err. t Paltl [95's conf. interval] age . "2.1" . "DIS! unoa r rial . .729253 . 011353 I. smoker educ _cons Jun\" .0115\"! - .3389\" . \"232.2 .0351\"! .0331613 5a. Interpret the coefficient on age (mathematically and statistically). (8 points) 5b. Interpret the coefficient on smoker (mathematically and statistically). (8 points) 5c. Given those regression results, what is the predicted probability that a child is born low birthweight if the mother is 29 years old, unmarried, a smoker, and has completed 12 years of education? (8 points) 5d. What would happen if you added another variable to regression equation above (Equation 5) called nonsmoker that = 1 if the mother did not smoke during pregnancy and 0 if they did? Why would this be a problem? (4 points) Be. What is another approach you could use to estimate the relationship between low birthweight (a binary dependent variable) and maternal characteristics? Specify at least one alternative approach and explain what the advantages and disadvantages are relative to the linear probability model. (5 points) (t