Covariance in Equations: How to Identify and Understand It

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In summary, you can tell if an equation is covariant by looking at two criteria. Firstly, the equation must contain only quantities with the same type of greek/spacetime indices summed over, and the tensor rank of the RHS must be equal to the tensor rank of the LHS. Secondly, the sides of the equation must have the same 'tensor quality', meaning that there cannot be an equality between a tensor and a nontensor. An example of a non-covariant equation is A^{i}=F^{\mu i}B_{\mu}, where the "F" term does not behave like a tensor due to its indices not following the same transformation rules.
  • #1
tiger_striped_cat
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How can you tell if an equation is covariant just by looking at it. Please try and keep explaniation to text more than equations.
 
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  • #2
tiger_striped_cat said:
How can you tell if an equation is covariant just by looking at it. Please try and keep explaniation to text more than equations.

There are basically two criteria:
1.The equation must contain only quantities with the same type of greek/spacetime indices summed over.Wrt to indices,the equation must be 'balanced',that is the tensor rank of the RHS must be equal to the tensor rank of the LHS.
2.The sides of the equation must have the same 'tensor quality' (i made it up).You cannot have an equality between a tensor (e.g.in the LHS) and a nontensor (in the LHS).

Daniel.

PS.The equation
[tex] A^{i}=F^{\mu i}B_{\mu} [/tex]
is not covariant.
 
  • #3
Thank you for your great explaniation. But could you explain the example:

[tex] A^{i}=F^{\mu i}B_{\mu} [/tex]


I think I'm having problems due to my lack of understanding with tensors and covariance, no fault of your explaniation.
 
  • #4
It's basically the "F" 'animal'.The way it's given,it's not a tensor because:
a) one index takes 4 values and the other only 3.
b) both indices should behave the same at a general coordinate transformation,but the trouble is that one index transforms with the normal matrix (4*4),while the other with another one,which has only (3*3) components.

Daniel.
 

Related to Covariance in Equations: How to Identify and Understand It

1. What is covariance and how does it relate to scientific research?

Covariance refers to the relationship between two variables in a dataset. In scientific research, it is used to measure how changes in one variable affect changes in another variable. It is an important concept in statistics and data analysis, as it helps researchers understand the relationships between different factors in their experiments.

2. How do you calculate covariance?

Covariance is calculated by finding the average of the products of the deviations of each data point from their respective means. This can be done using a formula or through various software programs. It is important to note that covariance is a measure of association, not causation.

3. What is the difference between covariance and correlation?

Covariance and correlation are both measures of the relationship between variables, but they have some key differences. Covariance measures the direction and strength of the relationship between two variables, while correlation also takes into account the scale of the variables. Correlation is often preferred as it provides a standardized measure of the relationship.

4. How do you interpret a covariance value?

A positive covariance value indicates a positive relationship between the variables, meaning that as one variable increases, the other tends to increase as well. A negative covariance value indicates a negative relationship, meaning that as one variable increases, the other tends to decrease. However, the magnitude of the covariance value does not provide information on the strength of the relationship.

5. How can covariance be used in scientific research?

Covariance can be used in various ways in scientific research. It can help identify relationships between variables, assess the strength of these relationships, and determine the direction of the relationship. It can also be used to identify patterns and make predictions. However, it is important to use caution when interpreting covariance values and to consider other factors that may influence the relationship between variables.

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