However, partial correlation and regression are the tests that allow the researcher to control the effect of confounders in the understanding of the relation between two variables (Chang 2003).
In biomedical or clinical research, the researcher often tries to understand or relate two or more independent (predictor) variables to predict an outcome or dependent variable.
For example, to know whether the likelihood of having high systolic BP (SBP) is influenced by factors such as age and weight, linear regression would be used.
The variable to be explained, i.e., SBP is called the dependent variable, or alternatively, the response variables that explain it age, weight, and sex are called independent variables. Armonk, NY: IBM Corp.) in five steps to analyze data using linear regression.
It is a modeling technique where a dependent variable is predicted based on one or more independent variables.