Answer:
S~N(13, 63)
Distribution = normal
Expected value = mean = 14
Variance = 63
Step-by-step explanation:
The theory of Sums of Independent Normal Random Variables explains that if X₁, X₂, X₃..., Xₙ represent normal random variables that are all mutually independent and have means μ₁, μ₂, μ₃,...... μₙ and variances, σ₁², σ₂², σ₃²,...... σₙ² that are linearly combined in the form
Y = Σ kᵢXᵢ (summing from i = 1, to i = n)
Gives a normal random variable that Has a Mean of
μ = Σ kᵢμᵢ (summing from i = 1, to i = n)
And a variance
σ² = Σ kᵢ²σᵢ² (summing from i = 1, to i = n)
So, X~N(3, 4), Y~N(-2, 6), Z~N(1, 3) are linearly combined as
S = 3X-2Y+Z
The new mean of the combination
μ = Σ kᵢμᵢ (summing from i = 1, to i = n)
μ = (3)(3) + (-2)(-2) + (1)(1) = 9 + 4 + 1 = 14
And the variance is
σ² = Σ kᵢ²σᵢ² (summing from i = 1, to i = n)
σ² = (3²)(4) + (-2)²(6) + (1²)(3) = 36 + 24 + 3 = 63
S~N(13, 63)
Distribution = normal
Expected value = mean = 14
Variance = 63
Hope this Helps!!!