In most practical settings, more than one explanatory variable is likely to be associated with a response. Multiple linear regression is an extension of simple linear regression that allows for more than one predictor variable in a linear model. As with simple linear regression, the response variable must be numerical, but the predictor variables can be either numerical or categorical. The statistical model estimating the linear relationship between a response variable $y$ and predictors $x_1, x_2, \dots, x_p$ is based on \[y = b_0 b_1x_1 b_2x_2 \cdots b_px_p. \] There are several applications of multiple regression. One of the most common applications in a clinical setting is estimating an association between a response variable and primary predictor of interest while adjusting for possible confounding variables