а) Для независимых случайных величин \( X \) и \( Y \), ковариация \( Cov(X, Y) = 0 \). Коэффициент корреляции \( \rho_{X, Y} = 0 \).
б) \( Cov(X, X+Y) = Cov(X, X) + Cov(X, Y) = Var(X) + 0 = Var(X) \). Так как \( X \) принимает значения от 1 до 6, \( E[X] = \frac{1+2+3+4+5+6}{6} = \frac{7}{2} = 3.5 \). \( E[X^2] = \frac{1^2+2^2+3^2+4^2+5^2+6^2}{6} = \frac{91}{6} \). \( Var(X) = E[X^2] - (E[X])^2 = \frac{91}{6} - (\frac{7}{2})^2 = \frac{91}{6} - \frac{49}{4} = \frac{182 - 147}{12} = \frac{35}{12} \). \( Cov(X, X+Y) = \frac{35}{12} \). \( \sigma_X = \sqrt{\frac{35}{12}} \). \( E[Y] = E[X] = 3.5 \), \( Var(Y) = Var(X) = \frac{35}{12} \). \( Var(X+Y) = Var(X) + Var(Y) = \frac{35}{12} + \frac{35}{12} = \frac{35}{6} \). \( \sigma_{X+Y} = \sqrt{\frac{35}{6}} \). Коэффициент корреляции: \( \rho_{X, X+Y} = \frac{Cov(X, X+Y)}{\sigma_X \sigma_{X+Y}} = \frac{\frac{35}{12}}{\sqrt{\frac{35}{12}} \sqrt{\frac{35}{6}}} = \frac{\frac{35}{12}}{\sqrt{\frac{35^2}{12 \cdot 6}}} = \frac{\frac{35}{12}}{\frac{35}{\sqrt{72}}} = \frac{\sqrt{72}}{12} = \frac{\sqrt{36 \cdot 2}}{12} = \frac{6\sqrt{2}}{12} = \frac{\sqrt{2}}{2} \).
в) \( Cov(X, X-Y) = Cov(X, X) - Cov(X, Y) = Var(X) - 0 = Var(X) \). \( Cov(X, X-Y) = \frac{35}{12} \). \( Var(X-Y) = Var(X) + Var(-Y) = Var(X) + Var(Y) = \frac{35}{12} + \frac{35}{12} = \frac{35}{6} \). \( \sigma_{X-Y} = \sqrt{\frac{35}{6}} \). Коэффициент корреляции: \( \rho_{X, X-Y} = \frac{Cov(X, X-Y)}{\sigma_X \sigma_{X-Y}} = \frac{\frac{35}{12}}{\sqrt{\frac{35}{12}} \sqrt{\frac{35}{6}}} = \frac{\sqrt{2}}{2} \).
г) \( Cov(X+Y, X-Y) = Cov(X, X) - Cov(X, Y) + Cov(Y, X) - Cov(Y, Y) = Var(X) - Var(Y) = \frac{35}{12} - \frac{35}{12} = 0 \). Коэффициент корреляции \( \rho_{X+Y, X-Y} = 0 \).