An increase in which of the following indicates a trend in data analysis?

An increase in seasonality indicates a trend in data analysis because it reveals patterns that recur at specific intervals over time. When analyzing time series data, seasonality is identified by consistent fluctuations that happen regularly, such as increases in retail sales during the holiday season or changes in electric consumption during summer and winter months.

Recognizing seasonality helps analysts differentiate between normal, predictable changes and unexpected anomalies within the data. This distinction is crucial for making forecasts and informed business decisions based on anticipated cycles. If seasonality is increasing, it can suggest that certain patterns are becoming more pronounced, which businesses can capitalize on or need to plan around.

While variation, correlation, and causation are important concepts in data analysis, they serve different purposes. Variation assesses how much data differs from the average, correlation examines relationships between variables, and causation establishes a cause-and-effect link. None of these directly points to a recurring trend as effectively as seasonality does.