Regression analysis forecasting is categorized under which model type?

Regression analysis forecasting is categorized as a causal model because it seeks to identify and quantify the relationship between one or more independent variables (predictors) and a dependent variable (outcome). In regression analysis, the focus is on understanding how changes in the predictors can directly affect the predicted outcome, allowing for predictions based on the values of the independent variables. This aligns with the definition of causal models, which assume that there is a cause-and-effect relationship.

In contrast, moving average and exponential smoothing are time-series forecasting methods. These methods rely on historical data and trends to make predictions, without establishing a direct cause-and-effect relationship. They focus more on smoothing past data to forecast future values rather than analyzing the influence of specific causal factors.

Subjective models, on the other hand, rely on human judgment, intuition, or qualitative data rather than quantitative analysis or established relationships. This form of forecasting is less structured and does not typically involve the rigorous statistical techniques used in regression analysis.

Therefore, categorizing regression analysis forecasting as a causal model is appropriate because it emphasizes the importance of examining the relationships between variables to make informed predictions.