Positive Correlation vs. Inverse Correlation: An Overview
In the field of statistics, correlation describes the relationship between two variables. Variables are correlated if the change in one is followed by a change in the other. Correlation shows if the relationship is positive or negative and how strong the relationship is. Positive correlation describes the relationship between two variables which change together, while an inverse correlation describes the relationship between two variables which change in opposing directions. Inverse correlation is sometimes known as a negative correlation, which describes the same type of relationship between variables.
- A positive correlation exists when two related variables move in the same direction.
- An inverse correlation exists when two related variables move in the opposite direction.
- Correlation does not necessarily imply causation as other factors may influence direction.
When two related variables move in the same direction, their relationship is positive. This correlation is measured by the coefficient of correlation (r). When r is greater than 0, it is positive. When r is +1.0, there is a perfect positive correlation. Examples of positive correlations occur in most people's daily lives. The more money spent on advertising, the more customers buy from the company. Because this is often difficult to measure, the coefficient of correlation would likely be less than +1.0. A stronger correlation would exist with the more hours an employee works, the larger that employee's paycheck will be.
Correlation is suitable when analyzing the relationship between significant, quantifiable data.
When two related variables move in opposite directions, their relationship is negative. When the coefficient of correlation (r) is less than 0, it is negative. When r is -1.0, there is a perfect negative correlation. Inverse correlations describe two factors that seesaw relative to each other. Examples include a declining bank balance relative to increased spending habits and reduced gas mileage relative to increased average driving speed. One example of an inverse correlation in the world of investments is the relationship between stocks and bonds. As stock prices rise, the bond market tends to decline, just as the bond market does well when stocks underperform.
It is important to understand that correlation does not necessarily imply causation. Variables A and B might rise and fall together, or A might rise as B falls. However, it is not always true that the rise of one factor directly influences the rise or fall of the other. Both may be caused by an underlying third factor, such as commodity prices, or the apparent relationship between the variables might be a coincidence.
The number of people connected to the Internet, for example, has been increasing since its inception, and the price of oil, until 2015, had generally trended upward over the same period. This is a positive correlation, but the two factors almost certainly have no meaningful relationship. That both the population of Internet users and the price of oil have increased is likely to be a coincidence.