Regression analysis is a technique used to understand the relationship between two variables. If there is a strong relationship between the two variables, it can be plotted on a line and the resulting formula can be used to predict values of the dependent variable. Regression analysis can reveal how closely two factors are related, as well as how certain one can be about the resulting predictions.
For example, a researcher plotted the unemployment rate against the percentage of college-educated adults in each county in Pennsylvania, which led her to discover that there is a strong negative relationship between college education and unemployment. As unemployment rates increase, the percentage of college graduates in a given area decreases.
Regression analysis is used in a business context for prediction and forecasting. It can anticipate consumer demand for a particular product, for instance, or predict how many people might see a print advertisement. It can be used to track potential profits, or measure the impact of advertising campaigns on sales. Regression analysis is also a useful tool for understanding and improving business processes. A grocery store manager might find it useful to know the relationship between store hours and the number of shoppers; a ridesharing app may wish to understand the relationship between wait time and the fares riders are willing to pay.
While a helpful model in determining relationships, it is important to note that regression analysis does not necessarily reveal causality. Just because two variables are related, it does not mean that the independent variable causes the dependent variable. In the unemployment rate vs. college education example above, the percentage of college-educated individuals in each county may also be influenced by additional factors: percentage of land that is urban or rural, population density, gender balance, and median age.
Further investigation on the part of the researcher may be required to fully determine all the intricacies of a given relationship.