How is an example of bucketing a measure field demonstrated?

In the context of bucketing a measure field, options typically involve dividing continuous data into distinct categories or ranges. This technique aids in simplifying the analysis and visualization of numerical data.

Choosing to change zip code ranges into corresponding territories is a strong example of bucketing, as it involves taking a continuous variable (zip codes) and categorizing it into defined groups (territories). This approach allows analysts to view data distribution across specific geographical regions instead of looking at individual zip codes, making it much easier to derive insights regarding demographics, marketing strategies, and service coverage.

The other options do not fit the concept of bucketing a measure field in the same way. For instance, converting income into tax brackets is more about classification rather than grouping numerical values into a range. Similarly, grouping ages into under 18 and over 18 is another form of categorization, but it focuses on binary classification rather than the creation of a bucketed range that encompasses multiple potential values. Finally, transforming sales figures into percentage growth is a calculation rather than a grouping of the data. Thus, the second option distinctly illustrates the concept of bucketing by organizing continuous data into manageable and analyzable segments.