Property of correlation coefficient

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SUMMARY

The correlation coefficient, denoted as ρ, is proven to equal 1 if and only if the relationship between two variables X and Y can be expressed as P(Y = aX + b) = 1. This establishes that a perfect linear relationship exists between X and Y when ρ(X, Y) = 1. The proof involves demonstrating both directions of the equivalence, starting with the assumption that P(Y = aX + b) = 1 leads to ρ(X, Y) = 1, and vice versa.

PREREQUISITES
  • Understanding of correlation coefficients and their properties
  • Familiarity with covariance and standard deviation
  • Knowledge of linear equations and probability theory
  • Basic statistical concepts, including mean (μ) and variance (σ²)
NEXT STEPS
  • Study the derivation of the correlation coefficient formula: ρ(X, Y) = cov(X, Y) / (σ_x σ_y)
  • Explore proofs of properties of correlation coefficients in statistics
  • Learn about linear regression and its relationship with correlation
  • Investigate the implications of perfect correlation in data analysis
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Students in statistics, data analysts, and anyone interested in understanding the mathematical properties of correlation coefficients and their applications in data relationships.

Max.Planck
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Homework Statement


Prove the following:
Let [tex]\rho[/tex] be the correlation coefficient.
Prove:
[tex]\rho(X, Y) = 1 \iff P(Y=aX+b) = 1[/tex]

Homework Equations


[tex]\rho(X, Y) = cov(X,Y)/\sigma_x\sigma_y[/tex]

The Attempt at a Solution


I have no idea how to prove this, I know that rho must lie between 1 and -1 (inclusive) and that values close to 1 indicate that high values of X must go with high values of Y. But I don't know how to formally prove the above problem.
 
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bump. I can't believe nobody hasn't proven or seen the proof of this.
 
As this is an if and only if, you must prove both ways, I suggest starting with
[tex]P(Y=aX+b)=1 \ \ \implies \ \ \rho(X,Y) = 1[/tex]

Now as the probability is one, you can start with
[tex]Y=aX+b[/tex]

Now assume you know [itex]\mu_X, \sigma_x[/itex] and calculate [itex]\rho(X,Y)[/itex]

The other direction may be slightly trickier, but you should get some good insights from the first exercise
 

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