If "X_1, X_2, ..., X_n" are independent random variables having normal distributions with means "\\mu_1, \\mu_2,..., \\mu_n" and variances "\\sigma_1^2, \\sigma_2^2 , ..., \\sigma_n ^2" respectively, then the random variable
"Y=a_1X_1+a_2X_2+...+a_nX_n" has a normal distribution with mean "\\mu_Y=a_1\\mu_1+a_2\\mu_2+...+a_n\\mu_n" and variance "\\sigma_Y^2=a_1^2\\sigma_1^2+a_2^2\\sigma_2^2+...+a_n^2\\sigma_n^2"
Then
"\\sigma_Z^2=\\sigma_{-2X+4Y-3}^2=(-2)^2(5)+(4)^2(3)=68"
If "X" and "Y" are not independent then
"\\sigma_Z^2=\\sigma_{-2X+4Y-3}^2=(-2)^2(5)+(4)^2(3)+2(-2)(4)(1)=52"
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