# 10: Correlation & Regression

Our interest in this chapter is in situations in which we can associate to each element of a population or sample two measurements \(x\) and y, particularly in the case that it is of interest to use the value of \(x\) to predict the value of y. For example, the population could be the air in automobile garages, \(x\) could be the electrical current produced by an electrochemical reaction taking place in a carbon monoxide meter, and \(y\) the concentration of carbon monoxide in the air. In this chapter we will learn statistical methods for analyzing the relationship between variables \(x\) and \(y\) in this context.