f ( x , y ) x 1 2 3 1 0.05 0.05 0.10 y 2 0.05 0.10 0.35 3 0.00 0.20 0.10 \def\arraystretch{1.5}
\begin{array}{c:c}
f(x,y) & & && x \\
& & & 1 & 2 & 3 \\ \hline
& 1 & & 0.05 & 0.05 & 0.10 \\
y & 2 & & 0.05 & 0.10 & 0.35 \\
& 3 & & 0.00 & 0.20 & 0.10 \\
\end{array} f ( x , y ) y 1 2 3 1 0.05 0.05 0.00 x 2 0.05 0.10 0.20 3 0.10 0.35 0.10 a) Evaluate the marginal distribution of X
g ( x ) = ∑ y f ( x , y ) g(x)=\sum_yf(x, y) g ( x ) = y ∑ f ( x , y ) g ( 1 ) = f ( 1 , 1 ) + f ( 1 , 2 ) + f ( 1 , 3 ) = 0.05 + 0.05 + 0.00 = 0.10 g(1)=f(1, 1)+f(1,2)+f(1,3)=0.05+0.05+0.00=0.10 g ( 1 ) = f ( 1 , 1 ) + f ( 1 , 2 ) + f ( 1 , 3 ) = 0.05 + 0.05 + 0.00 = 0.10
g ( 2 ) = f ( 2 , 1 ) + f ( 2 , 2 ) + f ( 2 , 3 ) = 0.05 + 0.10 + 0.20 = 0.35 g(2)=f(2, 1)+f(2,2)+f(2,3)=0.05+0.10+0.20=0.35 g ( 2 ) = f ( 2 , 1 ) + f ( 2 , 2 ) + f ( 2 , 3 ) = 0.05 + 0.10 + 0.20 = 0.35
g ( 3 ) = f ( 3 , 1 ) + f ( 3 , 2 ) + f ( 3 , 3 ) = 0.10 + 0.35 + 0.10 = 0.55 g(3)=f(3, 1)+f(3,2)+f(3,3)=0.10+0.35+0.10=0.55 g ( 3 ) = f ( 3 , 1 ) + f ( 3 , 2 ) + f ( 3 , 3 ) = 0.10 + 0.35 + 0.10 = 0.55
b) Evaluate the marginal distribution of Y
h ( y ) = ∑ x f ( x , y ) h(y)=\sum_xf(x, y) h ( y ) = x ∑ f ( x , y ) h ( 1 ) = f ( 1 , 1 ) + f ( 2 , 1 ) + f ( 3 , 1 ) = 0.05 + 0.05 + 0.10 = 0.20 h(1)=f(1, 1)+f(2,1)+f(3,1)=0.05+0.05+0.10=0.20 h ( 1 ) = f ( 1 , 1 ) + f ( 2 , 1 ) + f ( 3 , 1 ) = 0.05 + 0.05 + 0.10 = 0.20
h ( 2 ) = f ( 1 , 2 ) + f ( 2 , 2 ) + f ( 3 , 2 ) = 0.05 + 0.10 + 0.35 = 0.50 h(2)=f(1, 2)+f(2,2)+f(3,2)=0.05+0.10+0.35=0.50 h ( 2 ) = f ( 1 , 2 ) + f ( 2 , 2 ) + f ( 3 , 2 ) = 0.05 + 0.10 + 0.35 = 0.50
h ( 3 ) = f ( 1 , 3 ) + f ( 2 , 3 ) + f ( 3 , 3 ) = 0.00 + 0.20 + 0.10 = 0.30 h(3)=f(1, 3)+f(2,3)+f(3,3)=0.00+0.20+0.10=0.30 h ( 3 ) = f ( 1 , 3 ) + f ( 2 , 3 ) + f ( 3 , 3 ) = 0.00 + 0.20 + 0.10 = 0.30
c) Find the mean of X and Y
E ( X ) = 1 ( 0.10 ) + 2 ( 0.35 ) + 3 ( 0.55 ) = 2.45 E(X)=1(0.10)+2(0.35)+3(0.55)=2.45 E ( X ) = 1 ( 0.10 ) + 2 ( 0.35 ) + 3 ( 0.55 ) = 2.45
E ( Y ) = 1 ( 0.20 ) + 2 ( 0.50 ) + 3 ( 0.30 ) = 2.1 E(Y)=1(0.20)+2(0.50)+3(0.30)=2.1 E ( Y ) = 1 ( 0.20 ) + 2 ( 0.50 ) + 3 ( 0.30 ) = 2.1
d) Find the variance of X and Y
E ( X 2 ) = 1 2 ( 0.10 ) + 2 2 ( 0.35 ) + 3 2 ( 0.55 ) = 6.45 E(X^2)=1^2(0.10)+2^2(0.35)+3^2(0.55)=6.45 E ( X 2 ) = 1 2 ( 0.10 ) + 2 2 ( 0.35 ) + 3 2 ( 0.55 ) = 6.45
V ( X ) = E ( X 2 ) − ( E ( X ) ) 2 = 6.45 − ( 2.45 ) 2 = 0.4475 V(X)=E(X^2)-(E(X))^2=6.45-(2.45)^2=0.4475 V ( X ) = E ( X 2 ) − ( E ( X ) ) 2 = 6.45 − ( 2.45 ) 2 = 0.4475
E ( Y 2 ) = 1 2 ( 0.20 ) + 2 2 ( 0.50 ) + 3 2 ( 0.30 ) = 4.90 E(Y^2)=1^2(0.20)+2^2(0.50)+3^2(0.30)=4.90 E ( Y 2 ) = 1 2 ( 0.20 ) + 2 2 ( 0.50 ) + 3 2 ( 0.30 ) = 4.90
V ( Y ) = E ( Y 2 ) − ( E ( Y ) ) 2 = 4.90 − ( 2.1 ) 2 = 0.49 V(Y)=E(Y^2)-(E(Y))^2=4.90-(2.1)^2=0.49 V ( Y ) = E ( Y 2 ) − ( E ( Y ) ) 2 = 4.90 − ( 2.1 ) 2 = 0.49
e) Find the correlation coefficient between X and Y
E ( X Y ) = 1 ( 1 ) ( 0.05 ) + 1 ( 2 ) ( 0.05 ) + 1 ( 3 ) ( 0.10 ) + E(XY)=1(1)(0.05)+1(2)(0.05)+1(3)(0.10)+ E ( X Y ) = 1 ( 1 ) ( 0.05 ) + 1 ( 2 ) ( 0.05 ) + 1 ( 3 ) ( 0.10 ) + + 2 ( 1 ) ( 0.05 ) + 2 ( 2 ) ( 0.10 ) + 2 ( 3 ) ( 0.35 ) + +2(1)(0.05)+2(2)(0.10)+2(3)(0.35)+ + 2 ( 1 ) ( 0.05 ) + 2 ( 2 ) ( 0.10 ) + 2 ( 3 ) ( 0.35 ) + + 3 ( 1 ) ( 0.00 ) + 3 ( 2 ) ( 0.20 ) + 3 ( 3 ) ( 0.10 ) = 5.15 +3(1)(0.00)+3(2)(0.20)+3(3)(0.10)=5.15 + 3 ( 1 ) ( 0.00 ) + 3 ( 2 ) ( 0.20 ) + 3 ( 3 ) ( 0.10 ) = 5.15
c o v x y = σ x y = E ( X Y ) − E ( X ) E ( Y ) = cov_{xy}=\sigma_{xy}=E(XY)-E(X)E(Y)= co v x y = σ x y = E ( X Y ) − E ( X ) E ( Y ) = = 5.15 − 2.45 ( 2.1 ) = 0.005 =5.15-2.45(2.1)=0.005 = 5.15 − 2.45 ( 2.1 ) = 0.005
ρ X Y = σ X Y σ X σ Y = 0.005 0.4475 ( 0.49 ) ≈ 0.0107 \rho_{XY}={\sigma_{XY}\over \sigma_X \sigma_Y}={0.005\over\sqrt{0.4475(0.49)}}\approx0.0107 ρ X Y = σ X σ Y σ X Y = 0.4475 ( 0.49 ) 0.005 ≈ 0.0107 f) Interpret the result find in part (e).
0 < ρ X Y < 0.3 0<\rho_{XY}<0.3 0 < ρ X Y < 0.3 Very weak positive correlation.
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