CORRELATION

Correlation is a statistical technique that can show whether and how strongly pairs of variables are related. For example, height and weight are related; taller people tend to be heavier than shorter people.

Techniques in Determining Correlation

There are several different correlation techniques. The Survey System's optional Module includes the most common type, called the Pearson or product-moment correlation. The module also includes a variation on this type called partial correlation. The latter is useful when you want to look at the relationship between two variables while removing the effect of one or two other variables.

Like all statistical techniques, correlation is only appropriate for certain kinds of data. Correlation works for quantifiable data in which numbers are meaningful, usually quantities of some sort. It cannot be used for purely categorical data, such as gender, brands purchased, or favorite color

Correlation Coefficient

The main result of a correlation is called the correlation coefficient (or "r"). It ranges from -1.0 to +1.0. The closer r is to +1 or -1, the more closely the two variables are related.

If r is close to 0, it means there is no relationship between the variables. If r is positive, it means that as one variable gets larger the other gets larger. If r is negative it means that as one gets larger, the other gets smaller (often called an "inverse" correlation).

Covariance Vs Correlation

Covariance and Correlation are two mathematical concepts which are quite commonly used in statistics. Both of these two determine the relationship and measures the dependency between two random variables. Despite, some similarities between these two mathematical terms, they are different from each other. Correlation is when the change in one item may result in the change in another item. On the other hand, covariance is when two items vary together.

Key Differences Between Covariance and Correlation

The following points are noteworthy so far as the difference between co variance and correlation is concerned:

1.     A measure used to indicate the extent to which two random variables change in tandem is known as co variance. A measure used to represent how strongly two random variables are related known as correlation.

2.     Covariance is nothing but a measure of correlation. On the contrary, correlation refers to the scaled form of covariance.

3.     The value of correlation takes place between -1 and +1. Conversely, the value of covariance lies between -∞ and +∞.

4.     Covariance is affected by the change in scale, i.e. if all the value of one variable is multiplied by a constant and all the value of another variable are multiplied, by a similar or different constant, then the covariance is changed. As against this, correlation is not influenced by the change in scale.

5.     Correlation is dimensionless, i.e. it is a unit-free measure of the relationship between variables. Unlike covariance, where the value is obtained by the product of the units of the two variables

6.     Correlation is a special case of covariance which can be obtained when the data is standardized. Now, when it comes to making a choice, which is a better measure of the relationship between two variables, correlation is preferred over covariance, because it remains unaffected by the change in location and scale, and can also be used to make a comparison between two pairs of variables