Simple regression revealed that taken alone length of stay is a significant predictor of total costs. However, the linear model underestimates the total costs associated with longer lengths of stay. It makes sense that the longer a newborn stays in the hospital, the higher the total costs, but this does not tell the whole story. Additional costs are likely incurred that are related to the various diagnoses and there will be different costs associated with the type and quantity of treatments.

Birthweight was shown not to be a significant predictor of total costs. This contradicts our expectations as low birthweight is usually associated with premature birth and the resulting complications require additional therapies and hence increased costs. However, there is no information given in the data set that indicates if the births were premature. The de-identified data is inherently restricted in the details provided to maintain patient anonymity. This may limit the ability to create an adequate predictive model. The full SPARCS data contains additional information that may result in a better predictive model. The limitations of the data set used should always be considered when determining if the statistical model adequately addresses the problem posed. Reviewing the services offered at the Champlain Valley Physicians Hospital (New York State Department of Health website) shows that the hospital is designated as a Level 1 Perinatal Center which only provides care for normal and low-risk deliveries and does not have a neonatal intensive care unit. Since premature infants are often low birth weight, it seems reasonable that such infants born at CVPH would be transferred to another hospital having more neonatal care services. Conducting such additional research can often help explain the reasonableness of statistical results.

A good strategy for attacking a statistical problem is to begin simply and proceed to more complicated models. Descriptive, univariate analysis is a crucial first step to become familiar with the data. This is followed by bivariate and then multivariate analysis. At each stage, a better understanding of the data and the relationships between variables is obtained which guides subsequent, more complicated analyses. As a next step, a multiple regression analysis would create a predictive equation for total costs with multiple independent variables and may have improved explanatory power.