What is a descriptive model?

A descriptive model focuses on summarizing and interpreting the characteristics and patterns within a dataset rather than predicting future outcomes. This type of model helps analysts understand the underlying structure of the data by providing insights into various attributes such as averages, distributions, relationships, or trends present in the dataset. It essentially enables researchers and business professionals to get a clearer picture of what the data looks like, which can inform decision-making or further analyses.

In contrast, other options touch on aspects outside the realm of descriptive modeling. For instance, a model that forecasts future events would be associated with predictive analytics, as it aims to estimate unknown outcomes based on historical data. Similarly, a detailed simulation of a business process implies an emphasis on how processes operate or are expected to behave, which goes beyond merely describing data. Meanwhile, the focus solely on financial metrics indicates a narrow perspective that does not encompass the broader attributes that a descriptive model would typically examine across various dataset characteristics.