You are analysing a dataset gathered at Baystate Medical Center, Springfield, Mass, in $1986,$ aiming to unravel factors tied to low birth weight. Variables encompass indicators like the mother's age, weight, race, smoking status during pregnancy, etc. Constructing a regression model to predict birth weight, you observe significant associations and goodness$-$of$-$fit metrics in the output. Constructing a regression model to predict birth weight, you observe significant associations and goodness$-$of$-$fit metrics in the output. Assess the model's ability to identify risk factors and highlight significant discoveries, focusing on independent variables impacting birth weight while explaining the lack of statistical significance for certain variables using specific values from the model output.

Questions

$1.$ Is this a good model for identifying the risk factors? Comment using the data above.

Based on the results reported, suggest whether this model qualifies to be a good model

$2.$ Identify what is surprising, interesting, or significant in this data. According to this model, which independent variables impact birth weight more than others? Why do you think the other variables are not statistically significant? Explain and be specific.

Identify the values you need to report to suggest which variables are significant