In data analysis, what term describes the use of numerical values to summarize data patterns?

The term that describes the use of numerical values to summarize data patterns is Quantitative Analysis. This approach involves the measurement and representation of data through numbers, allowing for the identification of trends, relationships, and patterns within the data set. Quantitative analysis often employs statistical methods to evaluate and interpret the numerical data, making it an essential tool in data analysis.

This choice is particularly relevant because it emphasizes the objective measurement and quantitative aspects of data, which helps in creating models, generating insights, and making data-driven decisions based on empirical evidence. This aspect of analysis is foundational for anyone involved in data science or analytics, as it forms the basis of sound statistical reasoning and support for hypotheses or conclusions.

In contrast, Qualitative Analysis focuses on non-numerical data, such as descriptive categories or themes, and does not summarize data patterns through numerical values. Statistical Inference relates to making predictions or generalizations about a population based on sample data but is not specifically about summarizing data patterns with numerical values. Statistical Modeling involves creating mathematical representations of data to understand relationships but is a broader concept that includes more than just summarizing data patterns.

This understanding of Quantitative Analysis provides a strong foundation for further study and application in data science practices.