What type of forecasting model assumes a relationship with usage and other factors like selling price?

The causal model is the correct choice because it is designed specifically to analyze the relationships between variables. In this context, a causal model recognizes that usage can be influenced by various factors, such as selling price, advertising spend, weather conditions, or economic indicators. This type of forecasting employs statistical methods to quantify the strength and nature of these relationships, enabling more accurate predictions by taking into account these influencing factors.

In contrast, moving average and exponential smoothing models primarily focus on historical data trends and patterns to predict future values, without explicitly incorporating external variables such as price. A moving average, for instance, smooths out fluctuations by averaging past data over a specified number of periods, while exponential smoothing gives more weight to recent observations but does not address other variables directly.

Qualitative models, on the other hand, rely on subjective judgment and intuition rather than empirical data, making them less suitable for forecasting when there is a clear relationship among multiple influencing factors. Therefore, a causal model is the appropriate forecasting model for scenarios where the usage is expected to relate significantly to other measurable variables like selling price.