Covariance and Correlation matrix

In summary, the conversation discusses the desire to learn more about two matrices, their calculation methods, and potential use in R Studio. The individual also expresses difficulty finding adequate explanations on Google and asks about using the Pearson coefficient to measure dispersion. However, the conversation ends with the mention of a Wikipedia article that provides sufficient information on the topic.
  • #1
theakdad
211
0
I would love to learn more about those two matrices. What do they tell us,how to calculate them? Maybe in R Studio?
I was searching for some good explanations on google,but i didnt find them.

And another question,i apologize if is not in right forum...
How do i know how much dispersion can i explain with Pearson coefficient?

Thank you for the answers.
 
Last edited:
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  • #2
theakdad said:
I was searching for some good explanations on google,but i didnt find them.
Fortunately, the internet has come a long way since 2014, and now we have a full Wikipedia article dedicated to this:
https://en.wikipedia.org/wiki/Covariance_and_correlation

Of course, now a Google search brings up plenty of hits.
 

FAQ: Covariance and Correlation matrix

What is a covariance matrix?

A covariance matrix is a square matrix that shows the relationships and variability between multiple variables. It is used to measure how much two variables change together and can help identify patterns and trends in data.

How is a covariance matrix different from a correlation matrix?

A covariance matrix measures the strength and direction of the relationship between two variables, while a correlation matrix measures the linear relationship between multiple variables. Correlation values range from -1 to 1, with 0 indicating no relationship, while covariance values have no specific range.

What is the purpose of using a correlation matrix?

A correlation matrix is used to identify and understand the relationships between multiple variables. It can help determine which variables are most strongly related and can be used to identify patterns and trends in data.

How is a correlation matrix calculated?

A correlation matrix is calculated by first standardizing the data for each variable, which involves subtracting the mean and dividing by the standard deviation. Then, the correlation coefficient (Pearson's r) is calculated for each pair of variables. The resulting values are then placed in a matrix format.

What is the difference between a positive and negative correlation in a correlation matrix?

A positive correlation indicates that as one variable increases, the other variable also tends to increase. A negative correlation indicates that as one variable increases, the other variable tends to decrease. A correlation value of 0 indicates no relationship between the variables.

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