Linear Combination of Independent Random Variables












0














I am working on a problem and am a bit stuck



The problem: For independent random variables



X ~ N(-1,3)



Y ~ N(0,2)



Z ~ N(4,1)



Consider U:= 2X - 4Y - Z + 5



Find the Expectation, variance, and MGF for U.



What I have done so far:



Fx(t) = Ee$^{tX}$ = exp(-t + $3t^2over2$)



Fy(t) = Ee$^{tY}$ = exp(0t + t$^2$)



Fz(t) = Ee$^{tZ}$ = exp(4t + $t^2over2$)



Then MGF of U:



Fu(t) = Ee$^{t(2X-4Y-Z+5)}$ = E[e$^{(2t)X}$e$^{(-4t)Y}$e$^{(-t)Z}$e$^{(5t)}$] =



e$^{(5t)}$Ee$^{(2t)X}$Ee$^{(-4t)Y}$Ee$^{(-t)Z}$ = e$^{(5t)}$Fx(2t)Fy(-4t)Fz(-1t) =



exp(5t) exp(-(2t) + $3(2t)^2over2$) exp(0(-4t) + (-4t)$^2$) exp(4(-1t) + $(-1t)^2over2$) =



exp(5t -2t +0t -4t + 6t$^2$ +16t$^2$ + $1over2$$t^2$) = exp(-1t + 22.5t$^2$) =



U ~ N(-1, 22.5)



Then for Expectation:



F'u(t) =



But here is where I am stuck. How do we find the Expectation of U (how do we take this derivative) and how do we find the Variance of U? Also, does the first part look correct?










share|cite|improve this question



























    0














    I am working on a problem and am a bit stuck



    The problem: For independent random variables



    X ~ N(-1,3)



    Y ~ N(0,2)



    Z ~ N(4,1)



    Consider U:= 2X - 4Y - Z + 5



    Find the Expectation, variance, and MGF for U.



    What I have done so far:



    Fx(t) = Ee$^{tX}$ = exp(-t + $3t^2over2$)



    Fy(t) = Ee$^{tY}$ = exp(0t + t$^2$)



    Fz(t) = Ee$^{tZ}$ = exp(4t + $t^2over2$)



    Then MGF of U:



    Fu(t) = Ee$^{t(2X-4Y-Z+5)}$ = E[e$^{(2t)X}$e$^{(-4t)Y}$e$^{(-t)Z}$e$^{(5t)}$] =



    e$^{(5t)}$Ee$^{(2t)X}$Ee$^{(-4t)Y}$Ee$^{(-t)Z}$ = e$^{(5t)}$Fx(2t)Fy(-4t)Fz(-1t) =



    exp(5t) exp(-(2t) + $3(2t)^2over2$) exp(0(-4t) + (-4t)$^2$) exp(4(-1t) + $(-1t)^2over2$) =



    exp(5t -2t +0t -4t + 6t$^2$ +16t$^2$ + $1over2$$t^2$) = exp(-1t + 22.5t$^2$) =



    U ~ N(-1, 22.5)



    Then for Expectation:



    F'u(t) =



    But here is where I am stuck. How do we find the Expectation of U (how do we take this derivative) and how do we find the Variance of U? Also, does the first part look correct?










    share|cite|improve this question

























      0












      0








      0







      I am working on a problem and am a bit stuck



      The problem: For independent random variables



      X ~ N(-1,3)



      Y ~ N(0,2)



      Z ~ N(4,1)



      Consider U:= 2X - 4Y - Z + 5



      Find the Expectation, variance, and MGF for U.



      What I have done so far:



      Fx(t) = Ee$^{tX}$ = exp(-t + $3t^2over2$)



      Fy(t) = Ee$^{tY}$ = exp(0t + t$^2$)



      Fz(t) = Ee$^{tZ}$ = exp(4t + $t^2over2$)



      Then MGF of U:



      Fu(t) = Ee$^{t(2X-4Y-Z+5)}$ = E[e$^{(2t)X}$e$^{(-4t)Y}$e$^{(-t)Z}$e$^{(5t)}$] =



      e$^{(5t)}$Ee$^{(2t)X}$Ee$^{(-4t)Y}$Ee$^{(-t)Z}$ = e$^{(5t)}$Fx(2t)Fy(-4t)Fz(-1t) =



      exp(5t) exp(-(2t) + $3(2t)^2over2$) exp(0(-4t) + (-4t)$^2$) exp(4(-1t) + $(-1t)^2over2$) =



      exp(5t -2t +0t -4t + 6t$^2$ +16t$^2$ + $1over2$$t^2$) = exp(-1t + 22.5t$^2$) =



      U ~ N(-1, 22.5)



      Then for Expectation:



      F'u(t) =



      But here is where I am stuck. How do we find the Expectation of U (how do we take this derivative) and how do we find the Variance of U? Also, does the first part look correct?










      share|cite|improve this question













      I am working on a problem and am a bit stuck



      The problem: For independent random variables



      X ~ N(-1,3)



      Y ~ N(0,2)



      Z ~ N(4,1)



      Consider U:= 2X - 4Y - Z + 5



      Find the Expectation, variance, and MGF for U.



      What I have done so far:



      Fx(t) = Ee$^{tX}$ = exp(-t + $3t^2over2$)



      Fy(t) = Ee$^{tY}$ = exp(0t + t$^2$)



      Fz(t) = Ee$^{tZ}$ = exp(4t + $t^2over2$)



      Then MGF of U:



      Fu(t) = Ee$^{t(2X-4Y-Z+5)}$ = E[e$^{(2t)X}$e$^{(-4t)Y}$e$^{(-t)Z}$e$^{(5t)}$] =



      e$^{(5t)}$Ee$^{(2t)X}$Ee$^{(-4t)Y}$Ee$^{(-t)Z}$ = e$^{(5t)}$Fx(2t)Fy(-4t)Fz(-1t) =



      exp(5t) exp(-(2t) + $3(2t)^2over2$) exp(0(-4t) + (-4t)$^2$) exp(4(-1t) + $(-1t)^2over2$) =



      exp(5t -2t +0t -4t + 6t$^2$ +16t$^2$ + $1over2$$t^2$) = exp(-1t + 22.5t$^2$) =



      U ~ N(-1, 22.5)



      Then for Expectation:



      F'u(t) =



      But here is where I am stuck. How do we find the Expectation of U (how do we take this derivative) and how do we find the Variance of U? Also, does the first part look correct?







      probability variance moment-generating-functions expected-value






      share|cite|improve this question













      share|cite|improve this question











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      asked Nov 20 at 18:09









      Ethan

      10012




      10012






















          1 Answer
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          Expecation is linear.



          $$E[U]= 2E[X] - 4E[Y] - E[Z] + 5$$



          Also, since they are independent.



          $$Var[U]= 2^2Var[X] + 4^2Var[Y] + Var[Z] $$



          Remark:




          • Usually we use $F$ for CDF and $M$ for mgf.


          • $U$ is again a normal distribution and after you find out the mean and variance, figuring out the mgf should be easy.






          share|cite|improve this answer























          • To clarify though, to find these expectations we need to take the derivative of the MFGs correct? ie. M'x(t) = M'x(0) = EX and M''x(t) = M''x(0) = E(X$^2$)
            – Ethan
            Nov 20 at 18:25












          • that's just one of the way. but here we are given that $X sim N(-1,3)$, clearly we know $E[X]=-1$ and $Var(X)=3$ and we can compute the second moment from there.
            – Siong Thye Goh
            Nov 20 at 18:27










          • Ok, that clears things up. Thank you.
            – Ethan
            Nov 20 at 18:28











          Your Answer





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          1 Answer
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          1 Answer
          1






          active

          oldest

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          active

          oldest

          votes






          active

          oldest

          votes









          1














          Expecation is linear.



          $$E[U]= 2E[X] - 4E[Y] - E[Z] + 5$$



          Also, since they are independent.



          $$Var[U]= 2^2Var[X] + 4^2Var[Y] + Var[Z] $$



          Remark:




          • Usually we use $F$ for CDF and $M$ for mgf.


          • $U$ is again a normal distribution and after you find out the mean and variance, figuring out the mgf should be easy.






          share|cite|improve this answer























          • To clarify though, to find these expectations we need to take the derivative of the MFGs correct? ie. M'x(t) = M'x(0) = EX and M''x(t) = M''x(0) = E(X$^2$)
            – Ethan
            Nov 20 at 18:25












          • that's just one of the way. but here we are given that $X sim N(-1,3)$, clearly we know $E[X]=-1$ and $Var(X)=3$ and we can compute the second moment from there.
            – Siong Thye Goh
            Nov 20 at 18:27










          • Ok, that clears things up. Thank you.
            – Ethan
            Nov 20 at 18:28
















          1














          Expecation is linear.



          $$E[U]= 2E[X] - 4E[Y] - E[Z] + 5$$



          Also, since they are independent.



          $$Var[U]= 2^2Var[X] + 4^2Var[Y] + Var[Z] $$



          Remark:




          • Usually we use $F$ for CDF and $M$ for mgf.


          • $U$ is again a normal distribution and after you find out the mean and variance, figuring out the mgf should be easy.






          share|cite|improve this answer























          • To clarify though, to find these expectations we need to take the derivative of the MFGs correct? ie. M'x(t) = M'x(0) = EX and M''x(t) = M''x(0) = E(X$^2$)
            – Ethan
            Nov 20 at 18:25












          • that's just one of the way. but here we are given that $X sim N(-1,3)$, clearly we know $E[X]=-1$ and $Var(X)=3$ and we can compute the second moment from there.
            – Siong Thye Goh
            Nov 20 at 18:27










          • Ok, that clears things up. Thank you.
            – Ethan
            Nov 20 at 18:28














          1












          1








          1






          Expecation is linear.



          $$E[U]= 2E[X] - 4E[Y] - E[Z] + 5$$



          Also, since they are independent.



          $$Var[U]= 2^2Var[X] + 4^2Var[Y] + Var[Z] $$



          Remark:




          • Usually we use $F$ for CDF and $M$ for mgf.


          • $U$ is again a normal distribution and after you find out the mean and variance, figuring out the mgf should be easy.






          share|cite|improve this answer














          Expecation is linear.



          $$E[U]= 2E[X] - 4E[Y] - E[Z] + 5$$



          Also, since they are independent.



          $$Var[U]= 2^2Var[X] + 4^2Var[Y] + Var[Z] $$



          Remark:




          • Usually we use $F$ for CDF and $M$ for mgf.


          • $U$ is again a normal distribution and after you find out the mean and variance, figuring out the mgf should be easy.







          share|cite|improve this answer














          share|cite|improve this answer



          share|cite|improve this answer








          edited Nov 20 at 18:17

























          answered Nov 20 at 18:12









          Siong Thye Goh

          99.1k1464117




          99.1k1464117












          • To clarify though, to find these expectations we need to take the derivative of the MFGs correct? ie. M'x(t) = M'x(0) = EX and M''x(t) = M''x(0) = E(X$^2$)
            – Ethan
            Nov 20 at 18:25












          • that's just one of the way. but here we are given that $X sim N(-1,3)$, clearly we know $E[X]=-1$ and $Var(X)=3$ and we can compute the second moment from there.
            – Siong Thye Goh
            Nov 20 at 18:27










          • Ok, that clears things up. Thank you.
            – Ethan
            Nov 20 at 18:28


















          • To clarify though, to find these expectations we need to take the derivative of the MFGs correct? ie. M'x(t) = M'x(0) = EX and M''x(t) = M''x(0) = E(X$^2$)
            – Ethan
            Nov 20 at 18:25












          • that's just one of the way. but here we are given that $X sim N(-1,3)$, clearly we know $E[X]=-1$ and $Var(X)=3$ and we can compute the second moment from there.
            – Siong Thye Goh
            Nov 20 at 18:27










          • Ok, that clears things up. Thank you.
            – Ethan
            Nov 20 at 18:28
















          To clarify though, to find these expectations we need to take the derivative of the MFGs correct? ie. M'x(t) = M'x(0) = EX and M''x(t) = M''x(0) = E(X$^2$)
          – Ethan
          Nov 20 at 18:25






          To clarify though, to find these expectations we need to take the derivative of the MFGs correct? ie. M'x(t) = M'x(0) = EX and M''x(t) = M''x(0) = E(X$^2$)
          – Ethan
          Nov 20 at 18:25














          that's just one of the way. but here we are given that $X sim N(-1,3)$, clearly we know $E[X]=-1$ and $Var(X)=3$ and we can compute the second moment from there.
          – Siong Thye Goh
          Nov 20 at 18:27




          that's just one of the way. but here we are given that $X sim N(-1,3)$, clearly we know $E[X]=-1$ and $Var(X)=3$ and we can compute the second moment from there.
          – Siong Thye Goh
          Nov 20 at 18:27












          Ok, that clears things up. Thank you.
          – Ethan
          Nov 20 at 18:28




          Ok, that clears things up. Thank you.
          – Ethan
          Nov 20 at 18:28


















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