What type of quantitative analysis involves updating averages by dropping the oldest data point when new data is added?

The rolling average, also known as a moving average, is a specific type of quantitative analysis that updates its calculation by dropping the oldest data point in the dataset each time a new data point is added. This method is particularly useful in various statistical analyses, as it helps to smooth out fluctuations in data over time and provides a more current average that reflects recent trends.

When utilizing a rolling average, for example, if you're tracking monthly sales figures and you add the new month's data, the rolling average will discard the sales figure from the month that is now outside of the defined range (for instance, if you are calculating a three-month rolling average, the figure from four months ago will be dropped). This creates a fluid representation that adapts to new information while maintaining a manageable number of data points for analysis.

In contrast, the mean simply refers to the average calculated from all available data without any adjustments over time, the concept of central tendency encompasses measures like mean, median, and mode without specifying a method of calculation over time, and the weighted average assigns different weights to individual data points rather than updating averages by removing the oldest data. This distinction highlights why the rolling average is the correct choice in this scenario.