ABSTRACT
This chapter discusses correlation, which is a way of describing how two variables are related to one another. It discusses the nature of bivariate distributions where we look at two variables simultaneously. The chapter explains the use of z-scores as a starting point in understanding correlation. It describes about the Pearson Product-Moment Correlation (PPMC) coefficient, how to compute it, and how to interpret it. The chapter looks at correlation as a step in making predictions. The coefficient is known as the Pearson Product-Moment Correlation Coefficient. However, it is typically referred to as the Pearson Correlation coefficient; by the abbreviation PPMC Coefficient; or, at times, generically as the Correlation Coefficient. Correlation is useful in many ways. If two variables are correlated with one another, and if we know the value of one variable, then we are able to make a prediction about the value of the other variable. In fact, this is one of the most practical reasons to compute correlations.
