The Correlation Coefficient R, also known as the Pearson product-moment, Pearson' r, or simply R, is used in mathematics, science and significantly statistics, as a measure of the strength of the linear relationship between two variables.
Basically this means that a coefficient correlation is a measure of how much one number can be expected to be influenced by changes in another.
A correlation coefficient of 1 would mean that two numbers are perfectly correlated, that is if one grows so does the other, and the change in one is a multiple of the change in the other.
Conversely a correlation coefficient of -1 means that the numbers are inversely correlated, thus if one grows the other will fall. The growth in one is a negative multiple of the growth in the other.
If a correlation coefficient is zero however, that means that the two numbers are not related.
In a non-zero correlation coefficient the numbers are related, but if the coefficient is not either 1 or -1 there are other influences, and therefore the relationship between the two numbers will not be fixed.
This means if you know one number you can only estimate the other. The closer the correlation coefficient is to zero the greater the uncertainty, and low correlation coefficients mean that the relationship is not certain enough to be useful.
Though the aforementioned description is a relationship between two variables, it is also possible to calculate correlations between any number of variables.
Basically this means that a coefficient correlation is a measure of how much one number can be expected to be influenced by changes in another.
A correlation coefficient of 1 would mean that two numbers are perfectly correlated, that is if one grows so does the other, and the change in one is a multiple of the change in the other.
Conversely a correlation coefficient of -1 means that the numbers are inversely correlated, thus if one grows the other will fall. The growth in one is a negative multiple of the growth in the other.
If a correlation coefficient is zero however, that means that the two numbers are not related.
In a non-zero correlation coefficient the numbers are related, but if the coefficient is not either 1 or -1 there are other influences, and therefore the relationship between the two numbers will not be fixed.
This means if you know one number you can only estimate the other. The closer the correlation coefficient is to zero the greater the uncertainty, and low correlation coefficients mean that the relationship is not certain enough to be useful.
Though the aforementioned description is a relationship between two variables, it is also possible to calculate correlations between any number of variables.