Answer:
Step-by-step explanation:
A correlation coefficient is a numerical measure of some type of correlation, meaning a statistical relationship between two variables. The variables may be two columns of a given data set of observations, often called a sample, or two components of a multivariate random variable with a known distribution. Pearson’s correlation coefficient returns a value between -1 and 1. The interpretation of the correlation coefficient is as under:
If the correlation coefficient is -1, it indicates a strong negative relationship. It implies a perfect negative relationship between the variables.
If the correlation coefficient is 0, it indicates no relationship.
If the correlation coefficient is 1, it indicates a strong positive relationship. It implies a perfect positive relationship between the variables.
A higher absolute value of the correlation coefficient indicates a stronger relationship between variables. Thus, a correlation coefficient of 0.78 indicates a stronger positive correlation as compared to a value of say 0.36. Similarly, a correlation coefficient of -0.87 indicates a stronger negative correlation as compared to a correlation coefficient of say -0.40.
Correlation Values
In other words, if the value is in the positive range, then it shows that the relationship between variables is correlated positively, and both the values decrease or increase together. On the other hand, if the value is in the negative range, then it shows that the relationship between variables is correlated negatively, and both the values will go in the opposite direction.
