Statistical forecasting models primarily rely on what to make predictions?

Statistical forecasting models are fundamentally based on the analysis of historical data to make predictions about future events or trends. This reliance on historical data allows these models to identify patterns, correlations, and trends that can be quantified and mathematically analyzed. By examining past performance, organizations can create models that project future outcomes with a certain level of accuracy.

Using historical data is crucial, as it provides a factual basis for forecasting rather than subjective interpretations or feelings. Proper statistical analysis of historical trends can help businesses decide on inventory levels, budget allocations, and strategic planning by understanding how variables interacted in the past and how they may behave in the future.

The other options, while useful in different contexts—such as expert opinions, market surveys, and personal intuition—do not form the core of statistical forecasting. These methods may supplement the analytical process, but they lack the rigorous, quantifiable nature that historical data offers in the context of statistical models.