Curve to Find Min Correlation Between A and C

In summary, the correlation between A and B is 0.8 and between B and C is 0.9. To calculate the least correlation between A and C, the correlation matrix must be symmetric and positive definite. This can be achieved by finding values for x that will make the eigenvalues positive when substituted into the standard equation for eigenvalues. In order to ensure that there are no roots greater than 1, the expression on the left side of the equation must be always positive or always negative for values of u greater than 1. This can be achieved by setting u=1-\lambda and solving for x in terms of u. However, this still leaves one equation and two unknowns, u and x.
  • #1
Bazman
21
0
the correlation between A and B is 0.8 and between B and C is 0.9. I have to calculate what the least correlation A and C can be.

[tex]
\begin{pmatrix}
1 & 0.8 & x\\
0.8 & 1 & 0.9\\
x & 0.9 & 1
\end{pmatrix}
[/tex]

using sthe standard equation for eigenvalues you get:

[tex] -\lambda^3 + 3\lambda^2 + \lambda(x^2-1.55) - x^2 + 1.44x -0.45 [/tex]

2 points have a used the correct methodology so far?
Are my calculations correct?
how do I proceed from here?

Baz
 
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  • #2
I'm not sure what you are doing so far. Which, of many definitions of "correlation", are you using here? What does the x in your matrix represent? I will point out that the standard "equation" for eigenvalues that you give is not an equation at all. I presume you meant to make that equal to 0. Why did you write that equation?
 
  • #3
I mean corelation betwen two random variables so the correlation matrix shouls be symmetric and positive defintie.

So the x in the equation represents the unknown correlation between A and C.

Well this is the problem:

look at the equation circa half way down:

http://en.wikipedia.org/wiki/Eigenvalue_algorithm

(I try to reproduce it below but its probably clearer on Wikipeadia)
det[A-lambda I3]= -lambda^3+lambda^2 Trace(A) + 0.5*lambda[Trace(A^2) -Trace^2(A)] + det [A

when this is set equal to zero and solved you get the eigenvalues.

If you sub in the values of my matrix including the values x you get the original equation I gave.

Really I need to find the range of values x that give positive values of the eigenvalues of my correlation matrix (to keep it positive definite). Taking the lower bound to be the lowest possible value of x.

I think the way I am proceeding is correct? (But I'm not sure)
 
  • #4
Bazman said:
I mean corelation betwen two random variables so the correlation matrix shouls be symmetric and positive defintie.

So the x in the equation represents the unknown correlation between A and C.

Well this is the problem:

look at the equation circa half way down:

http://en.wikipedia.org/wiki/Eigenvalue_algorithm

(I try to reproduce it below but its probably clearer on Wikipeadia)
det[A-lambda I3]= -lambda^3+lambda^2 Trace(A) + 0.5*lambda[Trace(A^2) -Trace^2(A)] + det [A

when this is set equal to zero and solved you get the eigenvalues.

If you sub in the values of my matrix including the values x you get the original equation I gave.

Really I need to find the range of values x that give positive values of the eigenvalues of my correlation matrix (to keep it positive definite). Taking the lower bound to be the lowest possible value of x.
That's the crucial point, then.

I think the way I am proceeding is correct? (But I'm not sure)
Since the diagonal values are all 1, I would keep the equation in terms of [itex]1- \lambda[/itex] and then let [itex]u= 1-\lambda[/itex]. The equation then is
[itex]u^3- (1.45+ x^2)u+ 1.44x= 0[/itex]
In order that [itex]\lambda> 0[/itex], u must be less than 1.

In order that there be no roots greater than 1, the expression on the left side of the equation must be always positive or always negative for u> 1. It's easy to see that, as u goes to infinity, that goes to positive infinity so it must be always positive for u> 1.
 
  • #5
Hi There,

HallsofIvy thanks for your help with this. I got to couple of quesitons see your amended quote and below.

HallsofIvy said:
That's the crucial point, then.


Since the diagonal values are all 1, I would keep the equation in terms of [itex]1- \lambda[/itex] and then let [itex]u= 1-\lambda[/itex]. The equation then is
[itex]u^3- (1.45+ x^2)u+ 1.44x= 0[/itex]
In order that [itex]\lambda> 0[/itex], u must be less than 1.

I follow the first part of your argument no problem.

In order that there be no roots greater than 1, the expression on the left side of the equation must be always positive or always negative for u> 1.

How do you come to this conclusion? Please elaborate. Why are you concerned that there be no roots greater than 1?

It's easy to see that, as u goes to infinity, that goes to positive infinity so it must be always positive for u> 1.

even given the last couple of points surely I am still stuck with one equation and two unknowns? u AND x.
[itex]u^3- (1.45+ x^2)u+ 1.44x= 0[/itex]
 
Last edited:

What is the purpose of finding the minimum correlation between A and C?

The purpose of finding the minimum correlation between A and C is to determine the relationship between two variables, A and C. This can help us understand how changes in one variable affect the other and can provide insights into patterns and trends in data.

What is the process for finding the minimum correlation between A and C?

The process for finding the minimum correlation between A and C involves plotting the data points for both variables on a graph and calculating the correlation coefficient. This coefficient measures the strength and direction of the relationship between the two variables. The minimum correlation is the point at which the two variables have the weakest relationship.

What factors can affect the minimum correlation between A and C?

Several factors can affect the minimum correlation between A and C, including the sample size, the type of data being analyzed, and any outliers in the data. It is important to consider these factors when interpreting the results of the correlation analysis.

What are the limitations of using correlation to analyze the relationship between A and C?

Correlation does not necessarily indicate causation, meaning that just because A and C are correlated does not mean that one causes the other. Additionally, correlation only measures linear relationships and may not capture more complex relationships between variables.

How can the results of the minimum correlation analysis be used?

The results of the minimum correlation analysis can be used to make predictions and inform decision-making. It can also be used to identify potential areas for further research and exploration.

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