Linear Transformations of DRVs (Edexcel A-Level Further Mathematics): Flashcards

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Linear Transformations of DRVs
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Form of linear transformation of variable

Y=mX+cY = mX + c

Formula for E(kX)E(kX)

E(kX)=kE(X)E(kX) = kE(X)

Formula for Var(kX)\text{Var}(kX)

Var(kX)=k2Var(X)\text{Var}(kX) = k^2 \text{Var}(X)

Formula for E(X+k)E(X + k)

E(X+k)=E(X)+kE(X + k) = E(X) + k

Formula for Var(X+k)\text{Var}(X + k)

Var(X+k)=Var(X)\text{Var}(X + k) = \text{Var}(X)

Distribution of X+YX + Y for independent normals

(X+Y)∼N(μx+μy,σx2+σy2)(X + Y) \sim N(\mu_x + \mu_y, \sigma_x^2 + \sigma_y^2)

Distribution of X−YX - Y for independent normals

(X−Y)∼N(μx−μy,σx2+σy2)(X - Y) \sim N(\mu_x - \mu_y, \sigma_x^2 + \sigma_y^2)

Distribution of X+YX + Y for independent Poissons

(X+Y)∼Po(λx+λy)(X + Y) \sim Po(\lambda_x + \lambda_y)

Key property of Poisson distribution

Mean equals variance

Is X−YX - Y Poisson if X,YX, Y are independent Poisson?

No, because E(X−Y)≠Var(X−Y)E(X - Y) \neq \text{Var}(X - Y)

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