Find $P(X > 1)$ if $X$ is standard normal
Clash Royale CLAN TAG#URR8PPP
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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?
probability
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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?
probability
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 $P(X>1) approx 0.5- 0.6827/2 = 0.15865$.
â JessicaK
Aug 11 at 4:48
Please concoct better titles...
â Did
Aug 11 at 5:51
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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?
probability
$f(x) = int_1^infty frac2sqrt2pie^-frac12x^2 dx$
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?
probability
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 $P(X>1) approx 0.5- 0.6827/2 = 0.15865$.
â JessicaK
Aug 11 at 4:48
Please concoct better titles...
â Did
Aug 11 at 5:51
add a comment |Â
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 $P(X>1) approx 0.5- 0.6827/2 = 0.15865$.
â JessicaK
Aug 11 at 4:48
Please concoct better titles...
â 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 $P(X>1) approx 0.5- 0.6827/2 = 0.15865$.
â 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 $P(X>1) approx 0.5- 0.6827/2 = 0.15865$.
â JessicaK
Aug 11 at 4:48
Please concoct better titles...
â Did
Aug 11 at 5:51
Please concoct better titles...
â Did
Aug 11 at 5:51
add a comment |Â
3 Answers
3
active
oldest
votes
up vote
0
down vote
accepted
Letôs 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 68âÂÂ95âÂÂ99.7 rule you probably know that $1-P(|Y|>1)=P(|Y|<1)=0.6827$
Consequently $P(|Y|>1)=1-0.6827=0.3173=P(X>1)$
add a comment |Â
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:
$$P(X > 1) = 1 - P(X leq 1) = 1 - colorred2phi(1) = dots$$
add a comment |Â
up vote
0
down vote
thus $$P(Xle1)=phi(1)$$
$$your answer =1-phi(1)$$
add a comment |Â
3 Answers
3
active
oldest
votes
3 Answers
3
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
0
down vote
accepted
Letôs 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 68âÂÂ95âÂÂ99.7 rule you probably know that $1-P(|Y|>1)=P(|Y|<1)=0.6827$
Consequently $P(|Y|>1)=1-0.6827=0.3173=P(X>1)$
add a comment |Â
up vote
0
down vote
accepted
Letôs 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 68âÂÂ95âÂÂ99.7 rule you probably know that $1-P(|Y|>1)=P(|Y|<1)=0.6827$
Consequently $P(|Y|>1)=1-0.6827=0.3173=P(X>1)$
add a comment |Â
up vote
0
down vote
accepted
up vote
0
down vote
accepted
Letôs 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 68âÂÂ95âÂÂ99.7 rule you probably know that $1-P(|Y|>1)=P(|Y|<1)=0.6827$
Consequently $P(|Y|>1)=1-0.6827=0.3173=P(X>1)$
Letôs 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 68âÂÂ95âÂÂ99.7 rule you probably know that $1-P(|Y|>1)=P(|Y|<1)=0.6827$
Consequently $P(|Y|>1)=1-0.6827=0.3173=P(X>1)$
answered Aug 11 at 5:29
callculus
16.4k31427
16.4k31427
add a comment |Â
add a comment |Â
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:
$$P(X > 1) = 1 - P(X leq 1) = 1 - colorred2phi(1) = dots$$
add a comment |Â
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:
$$P(X > 1) = 1 - P(X leq 1) = 1 - colorred2phi(1) = dots$$
add a comment |Â
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:
$$P(X > 1) = 1 - P(X leq 1) = 1 - colorred2phi(1) = dots$$
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:
$$P(X > 1) = 1 - P(X leq 1) = 1 - colorred2phi(1) = dots$$
answered Aug 11 at 4:36
Aaron Montgomery
4,257423
4,257423
add a comment |Â
add a comment |Â
up vote
0
down vote
thus $$P(Xle1)=phi(1)$$
$$your answer =1-phi(1)$$
add a comment |Â
up vote
0
down vote
thus $$P(Xle1)=phi(1)$$
$$your answer =1-phi(1)$$
add a comment |Â
up vote
0
down vote
up vote
0
down vote
thus $$P(Xle1)=phi(1)$$
$$your answer =1-phi(1)$$
thus $$P(Xle1)=phi(1)$$
$$your answer =1-phi(1)$$
edited Aug 11 at 4:52
answered Aug 11 at 4:46
James
1,683418
1,683418
add a comment |Â
add a comment |Â
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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 $P(X>1) approx 0.5- 0.6827/2 = 0.15865$.
â JessicaK
Aug 11 at 4:48
Please concoct better titles...
â Did
Aug 11 at 5:51