Finding correlations is a common technique in statistical analysis, and Release Point authors often use it to examine various data. If you're not sure what correlations are, here's an explanation:
Correlation shows how strongly connected things are. For example, imagine someone paid by the hour for work. There would be a perfect positive correlation between A) The number of hours they worked and B) The amount of money they earned. As the number of hours they worked increased, their pay would also always increase. Now, think of someone going up a set of stairs. There would be a perfect negative correlation between A) The number of stairs they climbed and B) The number of stairs left to climb. As the number of stairs they climbed increased, the number of stairs they had left to climb would always decrease.
A perfect positive correlation is represented by the number 1, and a perfect negative correlation is represented by the number -1. If two things are completely unrelated, that’s represented by 0. Though we can't be sure, it's likely that if we analyzed A) The number of Ford Focuses sold in the world and B) Paige Pierce's C1X putting percentage on consecutive days of a tournament, there would be almost 0 correlation (A's increases and decreases would have extremely little correlation with B).
This all means that when you see a positive number for a correlation, it means there was a positive correlation between A and B (as A increased, so did B). The closer the number is to 1, the stronger the correlation, and the closer to 0, the weaker the correlation. Negative numbers mean there was a negative correlation (as A increased, B decreased). The closer the number is to -1, the stronger the negative correlation, and the closer to 0, the weaker the correlation.
An important thing to remember is that correlation should never be seen as proving causation. For example, people wearing swimsuits and people buying ice cream cones are correlated, but buying an ice cream cone doesn’t cause you to wear a swimsuit.
