Correlated random variables

Then I tried to do a simpler problem where the coefficients were integers.In summary, to construct two new normal random variables Y1 and Y2 with specific means, variances and correlation, you can use the equations Y1=s1X1+m1 and Y2=bX1+cX2+m2 where b=s2(1-r2)^1/2 and c=s2r. This approach is based on the knowledge that correlated normal variables can be obtained from uncorrelated standard normal variables through linear combinations. Additionally, setting one coefficient to 0 and trying a simpler problem with integer coefficients can help in finding the desired relations.
  • #1
gradnu
21
0
I have two independent standard normal random variables X1,X2. Now I want to construct two new normal random variables Y1,Y2 with mean[tex]\mu[/tex]1, [tex]\mu[/tex]2 and variance ([tex]\sigma[/tex]1)^2, ([tex]\sigma[/tex]2)^2 and correlation [tex]\rho[/tex].
How do I approach this problem?
 
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  • #2
Y1=s1X1+m1
Y2=bX1+cX2+m2
where b2+c2=s22
b=rs2, therefore c=s2(1-r2)1/2
 
  • #3
Thanks mathman.
But what was your thought process? How did you come up with these relations?
 
  • #4
gradnu said:
Thanks mathman.
But what was your thought process? How did you come up with these relations?

From long past experience I know that to get correlated normal variables from uncorrrelated standard normal, you just need a linear combination. Adding the desired means is obvious. Also since there are four free coefficients and there are only three conditions, I just set one coefficient to 0.
 

1. What are correlated random variables?

Correlated random variables are two or more random variables that have some type of relationship or association with each other. This means that the values of one variable are somehow influenced by the values of the other variable.

2. How can we measure the correlation between two random variables?

The most commonly used measure of correlation is the Pearson correlation coefficient, which ranges from -1 to 1. A positive correlation coefficient indicates a positive linear relationship between the variables, while a negative correlation coefficient indicates a negative linear relationship. A correlation coefficient of 0 means there is no linear relationship between the variables.

3. Why is understanding correlated random variables important in scientific research?

Correlated random variables can help us understand the relationships between different factors or variables in a study. This can lead to insights and findings that would not be possible if we only looked at variables in isolation. Additionally, understanding correlation can help us make predictions and inform decision making.

4. What are some examples of correlated random variables?

Some examples of correlated random variables include height and weight, temperature and ice cream sales, and education level and income. In each of these examples, the values of one variable are influenced by the values of the other variable.

5. Can two variables be correlated without having a causal relationship?

Yes, correlation does not necessarily imply causation. Just because two variables are correlated does not mean that one causes the other. Other factors or variables could be influencing the relationship between the two variables.

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