The exploratory analysis presented in this case illustrates the use of visualization to become familiar with the variability of each data element, examine outliers, identify variables that are not candidates for further analysis (at least initially), and view relationships between variables. Such exploratory analysis is indispensable at the beginning of an inquiry and forms the foundation for subsequent analyses.

In data sets with more than a few variables, the number of possible combinations of variables is very large and considerable time and resources may be needed to examine a large number of combinations. An understanding of the problem domain in conjunction with insight gained from basic descriptive analysis can guide the development of an analysis strategy that will meet the needs of stakeholders. Gaining deeper understanding through consulting subject matter experts and pertinent references helps in selecting and interpreting meaningful analyses.

With large data sets, such as are available from SPARCS. significance testing is often of little value, as very small differences will be statistically significant, but not of practical significance. In the next two cases, we will continue our analysis of the Adirondack newborns data by developing predictive models through regression analysis.