Paper 2, Section I, F

Probability | Part IA, 2010

Let XX and YY be two non-constant random variables with finite variances. The correlation coefficient ρ(X,Y)\rho(X, Y) is defined by

ρ(X,Y)=E[(X−EX)(Y−EY)](Var⁡X)1/2(Var⁡Y)1/2\rho(X, Y)=\frac{\mathbb{E}[(X-\mathbb{E} X)(Y-\mathbb{E} Y)]}{(\operatorname{Var} X)^{1 / 2}(\operatorname{Var} Y)^{1 / 2}}

(a) Using the Cauchy-Schwarz inequality or otherwise, prove that

−1⩽ρ(X,Y)⩽1-1 \leqslant \rho(X, Y) \leqslant 1

(b) What can be said about the relationship between XX and YY when either (i) ρ(X,Y)=0\rho(X, Y)=0 or (ii) ∣ρ(X,Y)∣=1|\rho(X, Y)|=1. [Proofs are not required.]

(c) Take 0⩽r⩽10 \leqslant r \leqslant 1 and let X,X′X, X^{\prime} be independent random variables taking values ±1\pm 1 with probabilities 1/21 / 2. Set

Y={X, with probability rX′, with probability 1−rY= \begin{cases}X, & \text { with probability } r \\ X^{\prime}, & \text { with probability } 1-r\end{cases}

Find ρ(X,Y)\rho(X, Y).

Typos? Please submit corrections to this page on GitHub.