# What is the Difference Between Covariance and Correlation?

The difference between covariance and correlation is that correlation assesses the strength and direction of the linear link between two variables, while covariance just shows the direction of the relationship. So, the covariance determines the correlation.

## What is Covariance?

Covariance is the measure of changes between two random variables in statistics. This table provides an overview of covariance, covering its definition, calculation, importance, relationship with risk, types, and differences from correlation.

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## What is Correlation?

A correlation is a statistical measure that indicates the extent to which two or more variables fluctuate concerning each other. This table provides an overview of correlation and its application, including its calculation methods and types.

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## What is the Difference Between Covariance and Correlation

Despite the similarities between these mathematical terms, they are different from each other. Covariance is when two variables vary with each other, whereas correlation is when a change in one variable results in a change in another variable. Here we have differentiated both terms based on the following parameters :

## Covariance and Correlation Formulas

Covariance explains the joint variability of the variables. This table provides an overview of covariance calculation, interpretation, and its formula:

Correlation measures the association between the variables. Here we have stated the formula showing its relationship with covariance.

## Application of  Covariance and Correlation in Real Life

Covariance and correlation are often used in real life. From finding patterns to diagnosing issues covariance and correlation have numerous advantages to offer:

• Finding patterns: Correlation helps identify relationships between data points. A “correlation matrix” reveals if multiple variables move together (positive correlation) or oppositely (negative correlation). This uncovers hidden trends in large datasets.
• Enabling further analysis: Correlation matrices act as a springboard for other techniques. They feed into methods like factor analysis and regression, which explore deeper connections and make predictions based on the correlations found.
• Diagnosing issues: A correlation matrix can also act as a check. In linear regression, extensive correlations between variables flag potential problems. This helps identify situations where the analysis might be unreliable.

## FAQ’s

What is the main difference between covariance and correlation?

Covariance measures the directional relationship and magnitude between variables. Correlation standardises covariance, indicating the strength and direction of the relationship.

What is the difference between correlation and variance?

Correlation measures the degree and direction of association between two variables. Variance measures the spread or dispersion of data points around the mean.

What are the 3 types of correlation?

The three types of correlation are positive correlation (direct relationship), negative correlation (inverse relationship), and zero correlation (no relationship).

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