1 / 4

Difference between co-variance and correlation.pdf

Covariance and correlation are both measures used in statistics to assess the relationship between two variables. While both indicate the degree of dependence between variables, they have some key differences:<br><br>Covariance:<br>Definition:<br>Covariance: It measures the extent to which two variables change together. If the covariance is positive, it suggests that increases in one variable correspond to increases in the other, and vice versa for negative covariance.<br>Units:<br>Covariance: The unit of covariance is the product of the units of the two variables being measured.<br>Scale:<br>Covariance: The scale of

Himanshi17
Download Presentation

Difference between co-variance and correlation.pdf

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Difference between co-variance and correlation Covariance and correlation are both measures used in statistics to assess the relationship between two variables. While both indicate the degree of dependence between variables, they have some key differences: Covariance: 1. Definition: ● Covariance: It measures the extent to which two variables change together. If the covariance is positive, it suggests that increases in one variable correspond to increases in the other, and vice versa for negative covariance. 2. Units: ● Covariance: The unit of covariance is the product of the units of the two variables being measured. 3. Scale:

  2. ● Covariance: The scale of covariance is not standardized, making it difficult to compare covariances across different variable pairs. 4. Interpretation: ● Covariance: It provides a measure of the direction of the linear relationship between variables but does not give a clear indication of the strength or magnitude of the relationship. 5. Range: ● Covariance: It can take any value, positive or negative, and the magnitude is not bounded. Correlation: 1. Definition: ● Correlation: It is a standardized measure that assesses the strength and direction of a linear relationship between two variables. It is the covariance divided by the product of the standard deviations of the variables. 2. Units: ● Correlation: Being standardized, it is a unitless measure, making it easier to compare relationships between different pairs of variables. 3. Scale: ● Correlation: The scale is standardized, with values always falling between -1 and 1. 4. Interpretation: ● Correlation: It provides a clearer indication of the strength and direction of the linear relationship between variables compared to covariance. 5. Range: ● Correlation: It always falls between -1 and 1, where -1 indicates a perfect negative linear relationship, 1 indicates a perfect positive linear relationship, and 0 indicates no linear relationship. Summary:

  3. ● Covariance: Measures the direction of the linear relationship between two variables but does not provide a standardized measure, making it challenging to compare across different variable pairs. ● Correlation: Standardized measure indicating the strength and direction of a linear relationship between two variables. It is unitless, making it more interpretable and suitable for comparing relationships. In practical terms, correlation is often preferred over covariance because it provides a standardized measure that is easier to interpret and compare across different contexts.

  4. Checkout more at- https://uplevelway.com/

More Related