Expected value and variance of a transformed random variable












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$begingroup$


X is a binomial random variable with $n = 10000$ and $p = 60%$ (hence $E(X) = 10000 * 60 = 6000$). Now Y is a transformed random variable with $Y = 100000 - 7X$.



I have to calculate the expected value $E(Y)$ and variance $V(Y)$ of Y.



For $E(Y)$ I thought of this: $E(Y) = E(100000 - 7X) = E(-7X) + 100000 = 100000 - E(7X) = 100000 - 7E(X) = 100000 - 7 * 6000 = 58000$



But how do I calculate $V(Y)$?










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    0












    $begingroup$


    X is a binomial random variable with $n = 10000$ and $p = 60%$ (hence $E(X) = 10000 * 60 = 6000$). Now Y is a transformed random variable with $Y = 100000 - 7X$.



    I have to calculate the expected value $E(Y)$ and variance $V(Y)$ of Y.



    For $E(Y)$ I thought of this: $E(Y) = E(100000 - 7X) = E(-7X) + 100000 = 100000 - E(7X) = 100000 - 7E(X) = 100000 - 7 * 6000 = 58000$



    But how do I calculate $V(Y)$?










    share|cite|improve this question









    $endgroup$















      0












      0








      0





      $begingroup$


      X is a binomial random variable with $n = 10000$ and $p = 60%$ (hence $E(X) = 10000 * 60 = 6000$). Now Y is a transformed random variable with $Y = 100000 - 7X$.



      I have to calculate the expected value $E(Y)$ and variance $V(Y)$ of Y.



      For $E(Y)$ I thought of this: $E(Y) = E(100000 - 7X) = E(-7X) + 100000 = 100000 - E(7X) = 100000 - 7E(X) = 100000 - 7 * 6000 = 58000$



      But how do I calculate $V(Y)$?










      share|cite|improve this question









      $endgroup$




      X is a binomial random variable with $n = 10000$ and $p = 60%$ (hence $E(X) = 10000 * 60 = 6000$). Now Y is a transformed random variable with $Y = 100000 - 7X$.



      I have to calculate the expected value $E(Y)$ and variance $V(Y)$ of Y.



      For $E(Y)$ I thought of this: $E(Y) = E(100000 - 7X) = E(-7X) + 100000 = 100000 - E(7X) = 100000 - 7E(X) = 100000 - 7 * 6000 = 58000$



      But how do I calculate $V(Y)$?







      statistics random-variables variance expected-value






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      asked Dec 3 '18 at 8:38









      JoeyJoey

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          $begingroup$

          $$V(Y)=V(100000-7X)=7^2cdot V(X)$$



          I believe you can finish the exercise from here.






          share|cite|improve this answer









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            $begingroup$

            In general if $a,b$ are constants then: $$mathsf{Var}(aX+b)=a^2mathsf{Var}(X)$$
            Verification: $$mathbb Eleft(aX+b-mathbb E(aX+b)right)^2=mathbb Eleft(aX+b-amathbb EX-b)right)^2=mathbb Ea^2(X-mathbb EX)^2=a^2mathbb E(X-mathbb EX)^2$$






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              0












              $begingroup$

              $$V(Y)=V(100000-7X)=7^2cdot V(X)$$



              I believe you can finish the exercise from here.






              share|cite|improve this answer









              $endgroup$


















                0












                $begingroup$

                $$V(Y)=V(100000-7X)=7^2cdot V(X)$$



                I believe you can finish the exercise from here.






                share|cite|improve this answer









                $endgroup$
















                  0












                  0








                  0





                  $begingroup$

                  $$V(Y)=V(100000-7X)=7^2cdot V(X)$$



                  I believe you can finish the exercise from here.






                  share|cite|improve this answer









                  $endgroup$



                  $$V(Y)=V(100000-7X)=7^2cdot V(X)$$



                  I believe you can finish the exercise from here.







                  share|cite|improve this answer












                  share|cite|improve this answer



                  share|cite|improve this answer










                  answered Dec 3 '18 at 8:41









                  Siong Thye GohSiong Thye Goh

                  100k1466117




                  100k1466117























                      0












                      $begingroup$

                      In general if $a,b$ are constants then: $$mathsf{Var}(aX+b)=a^2mathsf{Var}(X)$$
                      Verification: $$mathbb Eleft(aX+b-mathbb E(aX+b)right)^2=mathbb Eleft(aX+b-amathbb EX-b)right)^2=mathbb Ea^2(X-mathbb EX)^2=a^2mathbb E(X-mathbb EX)^2$$






                      share|cite|improve this answer









                      $endgroup$


















                        0












                        $begingroup$

                        In general if $a,b$ are constants then: $$mathsf{Var}(aX+b)=a^2mathsf{Var}(X)$$
                        Verification: $$mathbb Eleft(aX+b-mathbb E(aX+b)right)^2=mathbb Eleft(aX+b-amathbb EX-b)right)^2=mathbb Ea^2(X-mathbb EX)^2=a^2mathbb E(X-mathbb EX)^2$$






                        share|cite|improve this answer









                        $endgroup$
















                          0












                          0








                          0





                          $begingroup$

                          In general if $a,b$ are constants then: $$mathsf{Var}(aX+b)=a^2mathsf{Var}(X)$$
                          Verification: $$mathbb Eleft(aX+b-mathbb E(aX+b)right)^2=mathbb Eleft(aX+b-amathbb EX-b)right)^2=mathbb Ea^2(X-mathbb EX)^2=a^2mathbb E(X-mathbb EX)^2$$






                          share|cite|improve this answer









                          $endgroup$



                          In general if $a,b$ are constants then: $$mathsf{Var}(aX+b)=a^2mathsf{Var}(X)$$
                          Verification: $$mathbb Eleft(aX+b-mathbb E(aX+b)right)^2=mathbb Eleft(aX+b-amathbb EX-b)right)^2=mathbb Ea^2(X-mathbb EX)^2=a^2mathbb E(X-mathbb EX)^2$$







                          share|cite|improve this answer












                          share|cite|improve this answer



                          share|cite|improve this answer










                          answered Dec 3 '18 at 9:00









                          drhabdrhab

                          100k544130




                          100k544130






























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