CSCP Practice Exam 2026 – Complete Guide for Exam Prep

The chosen forecasting method is associative forecasting, which leverages data from both internal and external sources to create predictive models. This approach correlates historical data with various predictors or independent variables to forecast future trends or outcomes. By using these predictors—such as economic indicators, consumer behavior patterns, or other relevant metrics—associative forecasting aims to capture the relationships between different factors that influence the variable being predicted.

For instance, a business might analyze how changes in economic conditions, market trends, and even competitor actions impact their sales figures. By quantifying these relationships, organizations can make more informed predictions that are grounded in a broader context of causative factors rather than relying solely on past performance.

The other methods mentioned do not utilize external or internal Predictors in the same manner. Time-series forecasting focuses mainly on historical data trends over time, qualitative forecasting relies on subjective judgment rather than structured data, and naive forecasting assumes that future values will be the same as the most recent data point, making it overly simplistic. Associative forecasting provides a richer, more comprehensive approach to understanding and predicting future scenarios.