Difference between Covariance and Correlation
We can discuss some of the main difference between them as below:
Covariance | Correlation |
| Covariance quantifies the interdependence of two variables. It measures the strength of the correlation between changes in one variable and changes in another. | Dividing the covariance by the sum of the standard deviations of the variables, it standardises the covariance. |
| Covariance: Covariance is not scaled, and the units used to quantify the variables affect how much it is worth. Comparing covariances across many datasets or variables is therefore challenging. | Correlation is a standardised measurement that has a range of -1 to 1. It enables meaningful comparisons between several datasets or variables and is independent of the magnitude of the variables. |
| The scales of the variables have an impact on covariance, which is not standardised. As a result, comparing the size of covariances across several datasets or variables is challenging. | By dividing the covariance by the sum of the standard deviations of the variables, correlation standardises the covariance. This enables for meaningful interpretation of the strength and direction of the association and makes correlation values comparable. |
| Understanding the combined variability of two variables and their potential link is helped by covariance. It is frequently used in statistical models, risk analysis, and portfolio analysis. | Correlation is frequently used to assess how strongly two variables are linearly related. It frequently appears in data analysis, regression models, prediction models, and multicollinearity assessments. |
Correlation describes the intensity and direction of the linear link between two variables, whereas covariance shows how much two variables vary together.
Covariance and Correlation in R Programming
Covariance and Correlation are terms used in statistics to measure relationships between two random variables. Both of these terms measure linear dependency between a pair of random variables or bivariate data. They both capture a different component of the relationship, despite the fact that they both provide information about the link between variables. Let’s investigate the theory underlying correlation and covariance:
We can discuss some of the main difference between them as below:In this article, we are going to discuss cov(), cor() and cov2cor() functions in R which use covariance and correlation methods of statistics and probability theory.