Which analysis focuses on understanding and modeling the distribution and relationships of data points?

Descriptive analysis is mainly concerned with summarizing and interpreting the underlying patterns and distributions within a dataset. It provides insights into the characteristics of the data, such as central tendency (mean, median, mode), dispersion (range, variance, standard deviation), and visualization through charts and graphs. This type of analysis helps stakeholders gain a clear understanding of the past patterns and trends in the data.

In this context, focusing on the distribution and relationships among data points aligns well with the goals of descriptive analysis, as it lays the groundwork for identifying how variables relate to one another and how they are distributed.

On the other hand, predictive analysis is about forecasting future outcomes based on historical data, leveraging techniques like regression and time series analysis. Prescriptive analysis goes further by recommending actions based on analytical findings and potential outcomes, addressing "what should be done" rather than simply understanding relationships. Correlational analysis specifically examines the strength and direction of relationships between two or more variables, but it does not encompass the broader descriptive techniques that provide a more comprehensive overview of the data distribution.