How does showing a moving average affect the data represented in a trend graph?

The concept of a moving average is primarily used in trend analysis to smooth out short-term fluctuations and highlight longer-term trends in data. When represented in a trend graph, the moving average tends to reduce the impact of random noise and fluctuations in the data, providing a clearer visualization of the underlying trend.

By calculating the average of a set number of data points over a specific period and updating it with new data, the moving average helps to balance out peaks and troughs, allowing viewers to see trends that may not be obvious when looking at the raw data. This smoothing effect can be particularly useful in scenarios where data might be erratic or subject to seasonal changes.

In contrast, options that suggest highlighting the highest values, adding additional data points, or increasing data variance do not accurately describe the primary function of a moving average. Instead, the moving average is designed to provide a more stable view of data trends rather than calling attention to outliers, inflating data sets, or increasing variability.