Regression analysis is an effective mathematical modeling technique of increasing popularity in the corporate world. Regression analyses essentially fit straight lines to data, creating simplified but frequently accurate summaries of the relationships between a variable of interest and other variables. Regression analysis has many technical and practical benefits. Regression analyses can handle a large number of predictive factors simultaneously. It is easy to score a database with a regression model.
Modelers can usually read the importance of any factor directly from the model.
A wide variety of applications support regression analysis. While more common analyses based on tables and charts are limited to a small number of dimensions, a regression model can take into account a virtually unlimited number of factors. In forecasting sales, for example, a regression could easily adjust for season, pricing, promotions, sales force, competitive factors, delivery delays, regional, national, and world economic developments in a single model. Regression also provides a precise estimate of the magnitude of the effect of each factor on the variable being modeled. Developing a reliable regression model requires an expert with both analytical experience and subject matter mastery.