Correlation between random variables

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SUMMARY

This discussion focuses on calculating the correlation between two random variables, x and y, given their joint probability density function (PDF) defined as $$P_{x,y}(x,y)=A \ xy \ e^{-(x^2)}e^{-\frac{y^2}{2}}u(x)u(y)$$. The covariance is calculated using the formula ##\sigma_{xy}=\overline{(x-\bar{x})(y-\bar{y})}##, and the correlation coefficient is derived from ##\rho_{xy}=\frac{\sigma_{xy}}{\sigma{x}\sigma{y}}##. The user expresses confusion regarding the calculation of variance and the incorporation of averages, indicating a need for clarity on these statistical concepts.

PREREQUISITES
  • Understanding of joint probability density functions (PDFs)
  • Knowledge of covariance and correlation coefficients
  • Familiarity with variance calculations
  • Ability to compute expected values (averages) for random variables
NEXT STEPS
  • Study the derivation of joint probability density functions for multiple variables
  • Learn how to compute covariance and correlation coefficients in detail
  • Explore variance calculations for random variables, including the use of averages
  • Review the properties of expected values in the context of joint distributions
USEFUL FOR

Students in statistics or probability theory, data analysts, and anyone seeking to understand the relationship between random variables through correlation and covariance calculations.

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Homework Statement



Find correlation between random variables x and y in the following:
$$P_{x,y}(x,y)=A \ xy \ e^{-(x^2)}e^{-\frac{y^2}{2}}u(x)u(y)$$

Homework Equations



The co-variance ##\sigma_{xy}=\overline{(x-\bar{x})(y-\bar{y})}## or ##\sigma_{xy}=\overline{xy}-\bar{x}\bar{y}##

The concept of co variance is a natural extension of the concept of variance. Definition -> ##\sigma_{x}^{2}=\overline{(x-\bar{x})(x-\bar{x})}##

Variables x and y are uncorrelated ##(\sigma_{xy}=0)## if ##\overline{xy}=\bar{x}\bar{y}##

Correlation coefficient ##\rho_{xy}=\frac{\sigma_{xy}}{\sigma{x}\sigma{y}}## if x and y are uncorrelated then ##\rho_{xy}=0##


The Attempt at a Solution



Ive solved for A in a previous problem with the same values and came up with ##A=\frac{2}{3}##

Im a little confused as to how I go about finding the "correlation" between x and y in this problem. I've posted a few points from my text that seem like they are relevant or helpful. Am I basically trying to find the correlation coefficient ##\rho_{xy}## and that will give me the correlation? From the definition above I need to know ##\sigma{xy} \ and \ \sigma{x} \ and \ sigma{y}##

I don't really under stand how to find the variance i guess? Even with the definition above. How do I incorporate the bars? Averages?

Any help is appreciated!
 
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yeah. If you have the explicit equation for P(x,y) and you know what are the possible values x and y can take, then you can calculate the quantities you have listed in your relevant section. As you say, the main thing is to use the correct equation for the average of a quantity.

hint: it is slightly more complicated than the 1 variable case, because you have a pdf which depends on two random variables x and y. But hopefully you remember the equation for the average in this case? (Or have it written down somewhere?)
 

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