What method is best suited for summarizing quantitative data to illustrate frequency distributions?

Descriptive statistics is the method best suited for summarizing quantitative data to illustrate frequency distributions. It provides a means of presenting and analyzing data in a way that highlights patterns and central tendencies without making inferences or predictions. Key elements of descriptive statistics include measures such as mean, median, mode, and standard deviation, as well as the use of frequency tables and graphical representations like histograms and bar charts. These tools effectively summarize and describe the main characteristics of the data set.

In contrast, regression analysis focuses on examining relationships between variables and predicting outcomes rather than summarizing data distributions. Factor analysis deals with identifying underlying relationships among variables by grouping them into factors, thus not directly relevant to basic data summarization. Correlation analysis assesses the strength and direction of relationships between two variables but, like regression, does not summarize frequency distributions. Therefore, descriptive statistics is the most appropriate choice for summarizing quantitative data to illustrate frequency distributions.