Find $P(X > 1)$ if $X$ is standard normal

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$f(x) = int_1^infty frac2sqrt2pie^-frac12x^2 dx$



find $P(X > 1)$



This is $X$ ~ $Norm(0, 1)$.



$P(X > 1) = 1 - P(X leq 1) = 1 - 2 phi(1) = 1-2(1-phi(-1)) = 1 - 2(1-0.1587) = -0.6826$.



Yikes. Negative number. What am I doing wrong?







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  • First your $f(x)$ has an error in that there should be no $2$ in the numerator of $frac2sqrt2pi$, further, expression for $f$ is independent of $x$ and unnecessary. It's not clear why you are manipulating the expression for $P(X>1)$ here since you're going to need to resort to numerically integrating the Gaussian at some point anyway. In my opinion, the easiest way to do this problem is to depend on the Empirical rule to conclude that $P(0leq X leq 1) approx 0.6827/2$ and so $P(X>1) approx 0.5- 0.6827/2 = 0.15865$.
    – JessicaK
    Aug 11 at 4:48











  • Please concoct better titles...
    – Did
    Aug 11 at 5:51














up vote
0
down vote

favorite












$f(x) = int_1^infty frac2sqrt2pie^-frac12x^2 dx$



find $P(X > 1)$



This is $X$ ~ $Norm(0, 1)$.



$P(X > 1) = 1 - P(X leq 1) = 1 - 2 phi(1) = 1-2(1-phi(-1)) = 1 - 2(1-0.1587) = -0.6826$.



Yikes. Negative number. What am I doing wrong?







share|cite|improve this question






















  • First your $f(x)$ has an error in that there should be no $2$ in the numerator of $frac2sqrt2pi$, further, expression for $f$ is independent of $x$ and unnecessary. It's not clear why you are manipulating the expression for $P(X>1)$ here since you're going to need to resort to numerically integrating the Gaussian at some point anyway. In my opinion, the easiest way to do this problem is to depend on the Empirical rule to conclude that $P(0leq X leq 1) approx 0.6827/2$ and so $P(X>1) approx 0.5- 0.6827/2 = 0.15865$.
    – JessicaK
    Aug 11 at 4:48











  • Please concoct better titles...
    – Did
    Aug 11 at 5:51












up vote
0
down vote

favorite









up vote
0
down vote

favorite











$f(x) = int_1^infty frac2sqrt2pie^-frac12x^2 dx$



find $P(X > 1)$



This is $X$ ~ $Norm(0, 1)$.



$P(X > 1) = 1 - P(X leq 1) = 1 - 2 phi(1) = 1-2(1-phi(-1)) = 1 - 2(1-0.1587) = -0.6826$.



Yikes. Negative number. What am I doing wrong?







share|cite|improve this question














$f(x) = int_1^infty frac2sqrt2pie^-frac12x^2 dx$



find $P(X > 1)$



This is $X$ ~ $Norm(0, 1)$.



$P(X > 1) = 1 - P(X leq 1) = 1 - 2 phi(1) = 1-2(1-phi(-1)) = 1 - 2(1-0.1587) = -0.6826$.



Yikes. Negative number. What am I doing wrong?









share|cite|improve this question













share|cite|improve this question




share|cite|improve this question








edited Aug 11 at 5:51









Did

242k23208443




242k23208443










asked Aug 11 at 4:02









Bas bas

39611




39611











  • First your $f(x)$ has an error in that there should be no $2$ in the numerator of $frac2sqrt2pi$, further, expression for $f$ is independent of $x$ and unnecessary. It's not clear why you are manipulating the expression for $P(X>1)$ here since you're going to need to resort to numerically integrating the Gaussian at some point anyway. In my opinion, the easiest way to do this problem is to depend on the Empirical rule to conclude that $P(0leq X leq 1) approx 0.6827/2$ and so $P(X>1) approx 0.5- 0.6827/2 = 0.15865$.
    – JessicaK
    Aug 11 at 4:48











  • Please concoct better titles...
    – Did
    Aug 11 at 5:51
















  • First your $f(x)$ has an error in that there should be no $2$ in the numerator of $frac2sqrt2pi$, further, expression for $f$ is independent of $x$ and unnecessary. It's not clear why you are manipulating the expression for $P(X>1)$ here since you're going to need to resort to numerically integrating the Gaussian at some point anyway. In my opinion, the easiest way to do this problem is to depend on the Empirical rule to conclude that $P(0leq X leq 1) approx 0.6827/2$ and so $P(X>1) approx 0.5- 0.6827/2 = 0.15865$.
    – JessicaK
    Aug 11 at 4:48











  • Please concoct better titles...
    – Did
    Aug 11 at 5:51















First your $f(x)$ has an error in that there should be no $2$ in the numerator of $frac2sqrt2pi$, further, expression for $f$ is independent of $x$ and unnecessary. It's not clear why you are manipulating the expression for $P(X>1)$ here since you're going to need to resort to numerically integrating the Gaussian at some point anyway. In my opinion, the easiest way to do this problem is to depend on the Empirical rule to conclude that $P(0leq X leq 1) approx 0.6827/2$ and so $P(X>1) approx 0.5- 0.6827/2 = 0.15865$.
– JessicaK
Aug 11 at 4:48





First your $f(x)$ has an error in that there should be no $2$ in the numerator of $frac2sqrt2pi$, further, expression for $f$ is independent of $x$ and unnecessary. It's not clear why you are manipulating the expression for $P(X>1)$ here since you're going to need to resort to numerically integrating the Gaussian at some point anyway. In my opinion, the easiest way to do this problem is to depend on the Empirical rule to conclude that $P(0leq X leq 1) approx 0.6827/2$ and so $P(X>1) approx 0.5- 0.6827/2 = 0.15865$.
– JessicaK
Aug 11 at 4:48













Please concoct better titles...
– Did
Aug 11 at 5:51




Please concoct better titles...
– Did
Aug 11 at 5:51










3 Answers
3






active

oldest

votes

















up vote
0
down vote



accepted










Let´s say the pdf is $$f(x)=frac2sqrt2pie^-frac12x^2$$



(without the integral)



Now you want to calculate $P(X> 1)$ which is



$int_1^infty frac2sqrt2pie^-frac12x^2 dx$



First we can factor out $2$.



$2cdot int_1^infty frac1sqrtpie^-frac12x^2 dx=2cdot P(Y>1)$, where $Y$ is standard normal distributed as $Ysimmathcal N(0,1)$



Since $Y$ is symmetric distributed around $0$ we can say that $2cdot P(Y> 1)= P(|Y|>1)$.



From the 68–95–99.7 rule you probably know that $1-P(|Y|>1)=P(|Y|<1)=0.6827$



Consequently $P(|Y|>1)=1-0.6827=0.3173=P(X>1)$






share|cite|improve this answer



























    up vote
    1
    down vote













    I assume that by $phi(t)$ you mean the area in the left tail of a normal distribution. If so, then the two below is incorrect and should be removed:
    $$P(X > 1) = 1 - P(X leq 1) = 1 - colorred2phi(1) = dots$$






    share|cite|improve this answer



























      up vote
      0
      down vote













      enter image description here
      thus $$P(Xle1)=phi(1)$$



      $$your answer =1-phi(1)$$






      share|cite|improve this answer






















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        3 Answers
        3






        active

        oldest

        votes








        3 Answers
        3






        active

        oldest

        votes









        active

        oldest

        votes






        active

        oldest

        votes








        up vote
        0
        down vote



        accepted










        Let´s say the pdf is $$f(x)=frac2sqrt2pie^-frac12x^2$$



        (without the integral)



        Now you want to calculate $P(X> 1)$ which is



        $int_1^infty frac2sqrt2pie^-frac12x^2 dx$



        First we can factor out $2$.



        $2cdot int_1^infty frac1sqrtpie^-frac12x^2 dx=2cdot P(Y>1)$, where $Y$ is standard normal distributed as $Ysimmathcal N(0,1)$



        Since $Y$ is symmetric distributed around $0$ we can say that $2cdot P(Y> 1)= P(|Y|>1)$.



        From the 68–95–99.7 rule you probably know that $1-P(|Y|>1)=P(|Y|<1)=0.6827$



        Consequently $P(|Y|>1)=1-0.6827=0.3173=P(X>1)$






        share|cite|improve this answer
























          up vote
          0
          down vote



          accepted










          Let´s say the pdf is $$f(x)=frac2sqrt2pie^-frac12x^2$$



          (without the integral)



          Now you want to calculate $P(X> 1)$ which is



          $int_1^infty frac2sqrt2pie^-frac12x^2 dx$



          First we can factor out $2$.



          $2cdot int_1^infty frac1sqrtpie^-frac12x^2 dx=2cdot P(Y>1)$, where $Y$ is standard normal distributed as $Ysimmathcal N(0,1)$



          Since $Y$ is symmetric distributed around $0$ we can say that $2cdot P(Y> 1)= P(|Y|>1)$.



          From the 68–95–99.7 rule you probably know that $1-P(|Y|>1)=P(|Y|<1)=0.6827$



          Consequently $P(|Y|>1)=1-0.6827=0.3173=P(X>1)$






          share|cite|improve this answer






















            up vote
            0
            down vote



            accepted







            up vote
            0
            down vote



            accepted






            Let´s say the pdf is $$f(x)=frac2sqrt2pie^-frac12x^2$$



            (without the integral)



            Now you want to calculate $P(X> 1)$ which is



            $int_1^infty frac2sqrt2pie^-frac12x^2 dx$



            First we can factor out $2$.



            $2cdot int_1^infty frac1sqrtpie^-frac12x^2 dx=2cdot P(Y>1)$, where $Y$ is standard normal distributed as $Ysimmathcal N(0,1)$



            Since $Y$ is symmetric distributed around $0$ we can say that $2cdot P(Y> 1)= P(|Y|>1)$.



            From the 68–95–99.7 rule you probably know that $1-P(|Y|>1)=P(|Y|<1)=0.6827$



            Consequently $P(|Y|>1)=1-0.6827=0.3173=P(X>1)$






            share|cite|improve this answer












            Let´s say the pdf is $$f(x)=frac2sqrt2pie^-frac12x^2$$



            (without the integral)



            Now you want to calculate $P(X> 1)$ which is



            $int_1^infty frac2sqrt2pie^-frac12x^2 dx$



            First we can factor out $2$.



            $2cdot int_1^infty frac1sqrtpie^-frac12x^2 dx=2cdot P(Y>1)$, where $Y$ is standard normal distributed as $Ysimmathcal N(0,1)$



            Since $Y$ is symmetric distributed around $0$ we can say that $2cdot P(Y> 1)= P(|Y|>1)$.



            From the 68–95–99.7 rule you probably know that $1-P(|Y|>1)=P(|Y|<1)=0.6827$



            Consequently $P(|Y|>1)=1-0.6827=0.3173=P(X>1)$







            share|cite|improve this answer












            share|cite|improve this answer



            share|cite|improve this answer










            answered Aug 11 at 5:29









            callculus

            16.4k31427




            16.4k31427




















                up vote
                1
                down vote













                I assume that by $phi(t)$ you mean the area in the left tail of a normal distribution. If so, then the two below is incorrect and should be removed:
                $$P(X > 1) = 1 - P(X leq 1) = 1 - colorred2phi(1) = dots$$






                share|cite|improve this answer
























                  up vote
                  1
                  down vote













                  I assume that by $phi(t)$ you mean the area in the left tail of a normal distribution. If so, then the two below is incorrect and should be removed:
                  $$P(X > 1) = 1 - P(X leq 1) = 1 - colorred2phi(1) = dots$$






                  share|cite|improve this answer






















                    up vote
                    1
                    down vote










                    up vote
                    1
                    down vote









                    I assume that by $phi(t)$ you mean the area in the left tail of a normal distribution. If so, then the two below is incorrect and should be removed:
                    $$P(X > 1) = 1 - P(X leq 1) = 1 - colorred2phi(1) = dots$$






                    share|cite|improve this answer












                    I assume that by $phi(t)$ you mean the area in the left tail of a normal distribution. If so, then the two below is incorrect and should be removed:
                    $$P(X > 1) = 1 - P(X leq 1) = 1 - colorred2phi(1) = dots$$







                    share|cite|improve this answer












                    share|cite|improve this answer



                    share|cite|improve this answer










                    answered Aug 11 at 4:36









                    Aaron Montgomery

                    4,257423




                    4,257423




















                        up vote
                        0
                        down vote













                        enter image description here
                        thus $$P(Xle1)=phi(1)$$



                        $$your answer =1-phi(1)$$






                        share|cite|improve this answer


























                          up vote
                          0
                          down vote













                          enter image description here
                          thus $$P(Xle1)=phi(1)$$



                          $$your answer =1-phi(1)$$






                          share|cite|improve this answer
























                            up vote
                            0
                            down vote










                            up vote
                            0
                            down vote









                            enter image description here
                            thus $$P(Xle1)=phi(1)$$



                            $$your answer =1-phi(1)$$






                            share|cite|improve this answer














                            enter image description here
                            thus $$P(Xle1)=phi(1)$$



                            $$your answer =1-phi(1)$$







                            share|cite|improve this answer














                            share|cite|improve this answer



                            share|cite|improve this answer








                            edited Aug 11 at 4:52

























                            answered Aug 11 at 4:46









                            James

                            1,683418




                            1,683418






















                                 

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