The covariance between X₁ and X₂ is calculated as Cov(X₁, X₂) = E(X₁X₂) - E(X₁)E(X₂).
Step 1: Calculate E(X₁)
The marginal distribution of X₁ is:
fX1(x1)=∫0281x1x2dx2=41x1,0≤x1≤1
E(X1)=∫01x1⋅41x1dx1=∫0141x12dx1=[121x13]01=121≈0.08333
Step 2: Calculate E(X₂)
The marginal distribution of X₂ is:
fX2(x2)=∫0181x1x2dx1=161x2,0≤x2≤2
E(X2)=∫02x2⋅161x2dx2=∫02161x22dx2=[481x23]02=488=61≈0.16667
Step 3: Calculate E(X₁X₂)
E(X1X2)=∫01∫02x1x2⋅81x1x2dx2dx1=81∫01x12dx1∫02x22dx2
=81[3x13]01[3x23]02=81⋅31⋅38=81⋅98=91≈0.11111
Step 4: Calculate Covariance
Cov(X1,X2)=E(X1X2)−E(X1)E(X2)=91−(121⋅61)=91−721=728−721=727≈0.09722
Thus, the covariance is approximately 0.0972, which corresponds to option A.