The Pearson correlation is a measure of how strongly two variables are related to one another. The Pearson correlation coefficient, r, ranges from -1 to 1. A value near zero indicates that the relationship between the two variables is not strong;
For example, height and shoe size have a weak positive correlation because taller people tend to wear larger shoes. A negative value means there is an inverse relationship between the two variables; for example, as age increases so does risk of death while driving at night in foggy conditions. A value of +1 indicates that there is a perfect positive linear relationship (in other words, as one variable goes up so does the other).
For instance, if you know how many hours someone sleeps each day then you can predict how many minutes they spend watching TV. A value close to -0.75 indicates a perfect negative linear relationship (in other words, as one variable goes up the other will go down).
For instance, if you know how much time someone spends in traffic then you can predict their blood pressure and risk of coronary heart disease. The __ is calculated by: R = rxy / sqrt(n) * 100% where “r” is Pearson’s correlation coefficient and “x” is correlated with “y”. The closer the score gets to +/- 0 or 100%, the stronger the connection between x and y becomes.
