Regression Analysis

In many cases, factors other than trend and seasonality are influencing demand; these factors are referred to as demand signals. We can sense these demand signals using analytics and measure their impact on demand to improve forecast accuracy. This chapter and Chapter 7 discuss modeling the cause-and-effect relationships of these demand signals.

Regression analysis is a quantitative method for investigating the cause-and-effect relationships between two or more variables. Usually the modeler seeks to discover the cause and effect of one variable on another: for example, the effect of price changes on demand or the effect of changes in advertising on the demand for a product. To explore such issues, the modeler collects data for the underlying variables of interest and uses regression to estimate the quantitative effect of the explanatory variables on the dependent, or target, variable that they affect, or influence. The modeler assesses the statistical significance of the estimated relationships, or the degree of confidence that the true relationship is close to the estimated relationship, by using several statistical criteria. Regression methods have long been central to the field of econometrics (study of applied economic statistics). However, over the past several decades, regression models have become popular for use in business to understand the effects of sales and marketing programming on consumer behavior. Now, with the advancements in technology, ...

Get Demand-Driven Forecasting: A Structured Approach to Forecasting, 2nd Edition now with the O’Reilly learning platform.

O’Reilly members experience live online training, plus books, videos, and digital content from nearly 200 publishers.