Regression is an incredibly common form of analysis used by both amateurs and professionals alike. Why is that? Because it is one of the most robust tools for understanding relationships between variables. Additionally, it also allows us the ability to make predictions on previously unseen data. Most people have taken a statistics course and have run a simple linear regression model. I’d guess that most people, given some model output, could pick out the y-intercept and the variable coefficients. While these pieces of information are incredibly important, what about all the other data that is returned when we run a model?

Are there other things we should consider? What do the other values tell us?

#linear-regression #r #r-programming #developer