Understanding Covariance and Correlation
What is Covariance?
Covariance is a statistical measure that examines the directional relationship between two variables. It is a way to measure how much two random variables changes together. It is just like variance, but instead it shows how two variables change together instead of how one variable changes. It is an essential technique to compare the relationships between multiple variables.
In a high-quality covariance, the 2 variables move inside the same path, and in terrible covariance the variables pass in opposite guidelines. For instance, if covariance is 0, it suggests no relationship among the variables. However, interpreting Covariance can be challenging due to its dependency at the units of the variables.
What is Correlation?
Correlation is a statistical term that measures how much two variables fluctuate together. It quantitatively measures the connection between two variables. Correlation may be positive, negative, or zero.
- Positive correlation occurs when the values of one variable rise in tandem with the values of the other. In contrast, as one decreases, the other tends to decline as well.
- Negative correlation occurs when the values of one variable drop while the values of another variable rise, and vice versa.
- Zero correlation indicates that no systematic link exists between the variables.
A correlation coefficient, which ranges from -1 to +1, is often used to describe correlation. Correlation value of +1 indicates perfect positive connection; correlation coefficient of -1 indicates perfect negative correlation; and a correlation coefficient of 0 indicates no association.
Covariance vs Correlation: Understanding Differences and Applications
Understanding the relation between variables is seen as an essential component of Machine Learning. With covariance and correlation serving as two key concepts for quantifying this relationship. Despite being often used interchangeably, covariance and correlation have unique meanings and uses.
In this guide, we will understand the concepts of Covariance and Correlation, their differences, advantages, disadvantages, and real-world applications.
Table of Content
- Understanding Covariance and Correlation
- Differences Between Covariance and Correlation
- Covariance vs Correlation : Exploring the Formula and Their Calculations
- Covariance and Correlation: Understanding the Differences and Interpretation
- Unit Dependency Between Covariance and Correlation
- Choosing Between Covariance and Correlation: When to Use Each
- Advantages and Disadvantages of Covariance
- Advantages and Disadvantages of Correlation
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