Regression analysis is a statistical method used to estimate the relationship among continuous variables. Linear regression is the simplest and most frequently used type of regression analysis. The aim of linear regression is to describe the response variable y through a linear combination of one or more explanatory variables x1, x2, x3, …, xp. In other words, the explanatory variables get weighted with constants and then summarized. For example, the simplest linear model is y = a + bx, where the two parameters a and b are the intercept and slope, respectively. The model formula for this relationship in R is y ~ x. Note that all parameters are left out. So, if our linear model was y = a + bx + cz, then our model formula will be y ~ x + z.