Regression is a powerful tool to examine whether and how factors are associated with an outcome of interest.

For instance, we may be interested in why our IQ (Intelligence Quotient) varies. It may be due to factors like age, gender, educational level, etc. Regression analysis can be used to examine if these factors are associated with IQ and if yes, by what extent.

In statistical terms, the outcome, e.g., IQ, is called the dependent variable, and the factors, e.g., age and gender, are called independent variables.

Example 1:

Obesity is a common health problem. One of the several measures of obesity is waist circumference. For the purpose of formulating prevention strategy, one may be interested in exploring factors associated with obesity.

In this scenario, waist circumference should be taken as the dependent variable in a regression analysis. The risk factors are called independent variables.