Blog entry by Orji Anyianuka
Correlation measures the degree to which two things are related to one another. For example, I will expect there is a correlation between the level of education and the salary earned. When one goes up, so does the other. Some relationships will be the other way round. Maybe, the correlation between the weight of people and the number of times they exercise in a week.
Two variables (measurement of things) are positively correlated if a change in one is associated with a change in the other in the same direction, such as the relationship between height and weight. Taller people weigh more (on average); shorter people weigh less. A correlation is negative if a positive change in one variable is associated with a negative change in the other, such as the relationship between exercise and weight.
The power of correlation as a statistical tool is that we can summarize an association between two variables in a single descriptive statistic: the correlation coefficient, a single number ranging from –1 to 1. A correlation of 1, means that every change in one variable corresponds with an equivalent change in the other variable in the same direction. A correlation of –1, means that every change in one variable is associated with an equivalent change in the other variable in the opposite direction.
The closer the correlation is to 1 or –1, the stronger the association. A correlation of 0 (or close to it) means that the variables have no meaningful association with one another, such as the relationship between car accidents in a year and the volume of fish imported.