Modeling a perfect linear association

So far, we have explored several real cases for which we have searched linear associations, and therefore we have built models of simple linear regression. Next, we tried to analyze the results to confirm the goodness of fit in the simulation of the real system. At this point, it is reasonable to wonder what results of a model perfectly fit a linear system. In this way we will know how to distinguish between a model with a good approximation to what is wrong. In this last case, clearly indicating a nonlinear relationship remains the best solution.

Previously, we said that a simple linear relationship is represented by the following formula:

Here, α and β, represent, respectively, the slope and the intercept with the y axis of the regression line. That being said, we build a dummy system by deciding a priori an intercept and a slope:

x<-seq(from = 1, to = 100, by = 0.1)
y<-2.7*x+6

In this way, we first created an integer vector (x) containing numbers...