Regression analysis estimates the relationship between two or more variables. More specifically, regression analysis helps one understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed (PANCHOTIA, 2020).

When utilizing regression for predictive analysis and encountering a significant difference between expected and actual sales results, it is crucial to identify the situation and take the necessary action to resolve it. Before communicating the discrepancy to management, I think it is advisable to first review the data and assumptions used in the analysis to ensure relevance and accuracy. We could analyze any potential factors or events that may have influenced the sales from what was predicted. We need to communicate the findings to the manager in a clear and concise manner. One of the primary goals of predictive analysis is to assign a probability to forecast drivers (Wilson), therefore, we need to emphasize that discrepancies can occur due to unforeseen variables or market dynamics. It's crucial to draw attention to any particular events or circumstances that may have led to the discrepancy between expected and actual sales.

To minimize future discrepancies, it's critical to routinely update the predictive model with the most recent data. It's also crucial to assess the quality of the historical data used for regression, making sure it's complete, accurate, and reflective of the market dynamics. To ensure a thorough awareness of market dynamics and possible sales drivers, there is a need to foster constant cooperation between data analysts, sales teams, and subject matter experts. Regression methods that are more sophisticated and can better capture complicated correlations and nonlinear patterns can be used. Examples include machine learning algorithms.

It is crucial to stress that while regression analysis is an excellent technique for forecasting sales, exact results cannot be guaranteed. By incorporating feedback and improving the underlying assumptions, the objective is to reduce disparities and continuously increase the prediction model's accuracy.