Improving on Chebyshev's inequality with normal approximation
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This problem comes from Suhov's and Kelbert's Probability and Statistics by Example I, worked example 1.6.2. It's not homework.
Let $Y_n$ be the number of spots shown cumulatively over $n$ throws of a standard six-sided die. The task is to calculate $n$ such that the following is guaranteed:
$mathbb{P}left( left|frac{Y_n}{n}-3.5right| > 0.1 right) leq 0.1$.
The authors arrive at approximately $n geq 2920$ using Chebyshev's inequality. I understand this part.
But then they note:
"Observe that using the normal approximation (the De Moivre-Laplace Theorem) yields a better lower bound: $n geq (1.96 times 10)^2 35/12 approx 794.$"
I tried approximating $Y_n$ with a Gaussian in the following way:
$mathbb{P}left( left| frac{Y_n-3.5n}{sqrt{n}sigma} right| > 0.1 frac{sqrt{n}}{sigma} right) approx 1- frac{1}{sqrt{2pi}}int_{ frac{sqrt{n}}{10sigma}}^{ frac{sqrt{n}}{10sigma}} mathrm{d}y e^{-frac{y^2}{2}} $
but I don't know how to proceed from there and would be grateful for any help!
statistics
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add a comment |
$begingroup$
This problem comes from Suhov's and Kelbert's Probability and Statistics by Example I, worked example 1.6.2. It's not homework.
Let $Y_n$ be the number of spots shown cumulatively over $n$ throws of a standard six-sided die. The task is to calculate $n$ such that the following is guaranteed:
$mathbb{P}left( left|frac{Y_n}{n}-3.5right| > 0.1 right) leq 0.1$.
The authors arrive at approximately $n geq 2920$ using Chebyshev's inequality. I understand this part.
But then they note:
"Observe that using the normal approximation (the De Moivre-Laplace Theorem) yields a better lower bound: $n geq (1.96 times 10)^2 35/12 approx 794.$"
I tried approximating $Y_n$ with a Gaussian in the following way:
$mathbb{P}left( left| frac{Y_n-3.5n}{sqrt{n}sigma} right| > 0.1 frac{sqrt{n}}{sigma} right) approx 1- frac{1}{sqrt{2pi}}int_{ frac{sqrt{n}}{10sigma}}^{ frac{sqrt{n}}{10sigma}} mathrm{d}y e^{-frac{y^2}{2}} $
but I don't know how to proceed from there and would be grateful for any help!
statistics
$endgroup$
add a comment |
$begingroup$
This problem comes from Suhov's and Kelbert's Probability and Statistics by Example I, worked example 1.6.2. It's not homework.
Let $Y_n$ be the number of spots shown cumulatively over $n$ throws of a standard six-sided die. The task is to calculate $n$ such that the following is guaranteed:
$mathbb{P}left( left|frac{Y_n}{n}-3.5right| > 0.1 right) leq 0.1$.
The authors arrive at approximately $n geq 2920$ using Chebyshev's inequality. I understand this part.
But then they note:
"Observe that using the normal approximation (the De Moivre-Laplace Theorem) yields a better lower bound: $n geq (1.96 times 10)^2 35/12 approx 794.$"
I tried approximating $Y_n$ with a Gaussian in the following way:
$mathbb{P}left( left| frac{Y_n-3.5n}{sqrt{n}sigma} right| > 0.1 frac{sqrt{n}}{sigma} right) approx 1- frac{1}{sqrt{2pi}}int_{ frac{sqrt{n}}{10sigma}}^{ frac{sqrt{n}}{10sigma}} mathrm{d}y e^{-frac{y^2}{2}} $
but I don't know how to proceed from there and would be grateful for any help!
statistics
$endgroup$
This problem comes from Suhov's and Kelbert's Probability and Statistics by Example I, worked example 1.6.2. It's not homework.
Let $Y_n$ be the number of spots shown cumulatively over $n$ throws of a standard six-sided die. The task is to calculate $n$ such that the following is guaranteed:
$mathbb{P}left( left|frac{Y_n}{n}-3.5right| > 0.1 right) leq 0.1$.
The authors arrive at approximately $n geq 2920$ using Chebyshev's inequality. I understand this part.
But then they note:
"Observe that using the normal approximation (the De Moivre-Laplace Theorem) yields a better lower bound: $n geq (1.96 times 10)^2 35/12 approx 794.$"
I tried approximating $Y_n$ with a Gaussian in the following way:
$mathbb{P}left( left| frac{Y_n-3.5n}{sqrt{n}sigma} right| > 0.1 frac{sqrt{n}}{sigma} right) approx 1- frac{1}{sqrt{2pi}}int_{ frac{sqrt{n}}{10sigma}}^{ frac{sqrt{n}}{10sigma}} mathrm{d}y e^{-frac{y^2}{2}} $
but I don't know how to proceed from there and would be grateful for any help!
statistics
statistics
edited Dec 26 '18 at 15:07
dom_miketa
asked Dec 25 '18 at 18:04
dom_miketadom_miketa
796
796
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1 Answer
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$begingroup$
First of all, you're missing a factor $frac1{sqrt{2pi}}$ in the integral. Anyway, call $Phi(z)$ the CDF of a $N(0,1)$ r.v., you have to find $n$ such that
$$Phileft(frac{sqrt n}{10sigma}right)-Phileft(-frac{sqrt n}{10sigma}right)=2Phileft(frac{sqrt n}{10sigma}right)-1ge 0.9,$$
that is,
$$Phileft(frac{sqrt n}{10sigma}right)ge0.95.$$
This is equivalent to
$$frac{sqrt n}{10sigma} ge Phi^{-1}(0.95)approx 1.96,$$
so the approximate answer is
$$nge 19.6^2 sigma^2,$$
and the answer is reasonable if the bound for $n$ is large enough to use the normal approximation (it is also necessary to find the value of $sigma$).
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$begingroup$
Thanks! I gave it another go today after a few more exercises which showcased this kind of approximation and arrived at a similar conclusion, except I find that $Phi^-1(0.95) approx 1.65$ (eg en.wikipedia.org/wiki/Standard_normal_table). Am I reading the table wrong?
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– dom_miketa
Dec 26 '18 at 15:07
1
$begingroup$
Actually not. I did not went to the table the other day so I made a mistake. You're right. $Phi^{-1}(0.95)approx1.65$. It is $Phi^{-1}(0.975)$ that is $1.96$.
$endgroup$
– Alejandro Nasif Salum
Dec 27 '18 at 15:30
1
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That's everything sorted then. Thanks again and have a lovely holiday if you celebrate one. :)
$endgroup$
– dom_miketa
Dec 27 '18 at 18:26
add a comment |
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$begingroup$
First of all, you're missing a factor $frac1{sqrt{2pi}}$ in the integral. Anyway, call $Phi(z)$ the CDF of a $N(0,1)$ r.v., you have to find $n$ such that
$$Phileft(frac{sqrt n}{10sigma}right)-Phileft(-frac{sqrt n}{10sigma}right)=2Phileft(frac{sqrt n}{10sigma}right)-1ge 0.9,$$
that is,
$$Phileft(frac{sqrt n}{10sigma}right)ge0.95.$$
This is equivalent to
$$frac{sqrt n}{10sigma} ge Phi^{-1}(0.95)approx 1.96,$$
so the approximate answer is
$$nge 19.6^2 sigma^2,$$
and the answer is reasonable if the bound for $n$ is large enough to use the normal approximation (it is also necessary to find the value of $sigma$).
$endgroup$
$begingroup$
Thanks! I gave it another go today after a few more exercises which showcased this kind of approximation and arrived at a similar conclusion, except I find that $Phi^-1(0.95) approx 1.65$ (eg en.wikipedia.org/wiki/Standard_normal_table). Am I reading the table wrong?
$endgroup$
– dom_miketa
Dec 26 '18 at 15:07
1
$begingroup$
Actually not. I did not went to the table the other day so I made a mistake. You're right. $Phi^{-1}(0.95)approx1.65$. It is $Phi^{-1}(0.975)$ that is $1.96$.
$endgroup$
– Alejandro Nasif Salum
Dec 27 '18 at 15:30
1
$begingroup$
That's everything sorted then. Thanks again and have a lovely holiday if you celebrate one. :)
$endgroup$
– dom_miketa
Dec 27 '18 at 18:26
add a comment |
$begingroup$
First of all, you're missing a factor $frac1{sqrt{2pi}}$ in the integral. Anyway, call $Phi(z)$ the CDF of a $N(0,1)$ r.v., you have to find $n$ such that
$$Phileft(frac{sqrt n}{10sigma}right)-Phileft(-frac{sqrt n}{10sigma}right)=2Phileft(frac{sqrt n}{10sigma}right)-1ge 0.9,$$
that is,
$$Phileft(frac{sqrt n}{10sigma}right)ge0.95.$$
This is equivalent to
$$frac{sqrt n}{10sigma} ge Phi^{-1}(0.95)approx 1.96,$$
so the approximate answer is
$$nge 19.6^2 sigma^2,$$
and the answer is reasonable if the bound for $n$ is large enough to use the normal approximation (it is also necessary to find the value of $sigma$).
$endgroup$
$begingroup$
Thanks! I gave it another go today after a few more exercises which showcased this kind of approximation and arrived at a similar conclusion, except I find that $Phi^-1(0.95) approx 1.65$ (eg en.wikipedia.org/wiki/Standard_normal_table). Am I reading the table wrong?
$endgroup$
– dom_miketa
Dec 26 '18 at 15:07
1
$begingroup$
Actually not. I did not went to the table the other day so I made a mistake. You're right. $Phi^{-1}(0.95)approx1.65$. It is $Phi^{-1}(0.975)$ that is $1.96$.
$endgroup$
– Alejandro Nasif Salum
Dec 27 '18 at 15:30
1
$begingroup$
That's everything sorted then. Thanks again and have a lovely holiday if you celebrate one. :)
$endgroup$
– dom_miketa
Dec 27 '18 at 18:26
add a comment |
$begingroup$
First of all, you're missing a factor $frac1{sqrt{2pi}}$ in the integral. Anyway, call $Phi(z)$ the CDF of a $N(0,1)$ r.v., you have to find $n$ such that
$$Phileft(frac{sqrt n}{10sigma}right)-Phileft(-frac{sqrt n}{10sigma}right)=2Phileft(frac{sqrt n}{10sigma}right)-1ge 0.9,$$
that is,
$$Phileft(frac{sqrt n}{10sigma}right)ge0.95.$$
This is equivalent to
$$frac{sqrt n}{10sigma} ge Phi^{-1}(0.95)approx 1.96,$$
so the approximate answer is
$$nge 19.6^2 sigma^2,$$
and the answer is reasonable if the bound for $n$ is large enough to use the normal approximation (it is also necessary to find the value of $sigma$).
$endgroup$
First of all, you're missing a factor $frac1{sqrt{2pi}}$ in the integral. Anyway, call $Phi(z)$ the CDF of a $N(0,1)$ r.v., you have to find $n$ such that
$$Phileft(frac{sqrt n}{10sigma}right)-Phileft(-frac{sqrt n}{10sigma}right)=2Phileft(frac{sqrt n}{10sigma}right)-1ge 0.9,$$
that is,
$$Phileft(frac{sqrt n}{10sigma}right)ge0.95.$$
This is equivalent to
$$frac{sqrt n}{10sigma} ge Phi^{-1}(0.95)approx 1.96,$$
so the approximate answer is
$$nge 19.6^2 sigma^2,$$
and the answer is reasonable if the bound for $n$ is large enough to use the normal approximation (it is also necessary to find the value of $sigma$).
answered Dec 25 '18 at 21:09
Alejandro Nasif SalumAlejandro Nasif Salum
4,765118
4,765118
$begingroup$
Thanks! I gave it another go today after a few more exercises which showcased this kind of approximation and arrived at a similar conclusion, except I find that $Phi^-1(0.95) approx 1.65$ (eg en.wikipedia.org/wiki/Standard_normal_table). Am I reading the table wrong?
$endgroup$
– dom_miketa
Dec 26 '18 at 15:07
1
$begingroup$
Actually not. I did not went to the table the other day so I made a mistake. You're right. $Phi^{-1}(0.95)approx1.65$. It is $Phi^{-1}(0.975)$ that is $1.96$.
$endgroup$
– Alejandro Nasif Salum
Dec 27 '18 at 15:30
1
$begingroup$
That's everything sorted then. Thanks again and have a lovely holiday if you celebrate one. :)
$endgroup$
– dom_miketa
Dec 27 '18 at 18:26
add a comment |
$begingroup$
Thanks! I gave it another go today after a few more exercises which showcased this kind of approximation and arrived at a similar conclusion, except I find that $Phi^-1(0.95) approx 1.65$ (eg en.wikipedia.org/wiki/Standard_normal_table). Am I reading the table wrong?
$endgroup$
– dom_miketa
Dec 26 '18 at 15:07
1
$begingroup$
Actually not. I did not went to the table the other day so I made a mistake. You're right. $Phi^{-1}(0.95)approx1.65$. It is $Phi^{-1}(0.975)$ that is $1.96$.
$endgroup$
– Alejandro Nasif Salum
Dec 27 '18 at 15:30
1
$begingroup$
That's everything sorted then. Thanks again and have a lovely holiday if you celebrate one. :)
$endgroup$
– dom_miketa
Dec 27 '18 at 18:26
$begingroup$
Thanks! I gave it another go today after a few more exercises which showcased this kind of approximation and arrived at a similar conclusion, except I find that $Phi^-1(0.95) approx 1.65$ (eg en.wikipedia.org/wiki/Standard_normal_table). Am I reading the table wrong?
$endgroup$
– dom_miketa
Dec 26 '18 at 15:07
$begingroup$
Thanks! I gave it another go today after a few more exercises which showcased this kind of approximation and arrived at a similar conclusion, except I find that $Phi^-1(0.95) approx 1.65$ (eg en.wikipedia.org/wiki/Standard_normal_table). Am I reading the table wrong?
$endgroup$
– dom_miketa
Dec 26 '18 at 15:07
1
1
$begingroup$
Actually not. I did not went to the table the other day so I made a mistake. You're right. $Phi^{-1}(0.95)approx1.65$. It is $Phi^{-1}(0.975)$ that is $1.96$.
$endgroup$
– Alejandro Nasif Salum
Dec 27 '18 at 15:30
$begingroup$
Actually not. I did not went to the table the other day so I made a mistake. You're right. $Phi^{-1}(0.95)approx1.65$. It is $Phi^{-1}(0.975)$ that is $1.96$.
$endgroup$
– Alejandro Nasif Salum
Dec 27 '18 at 15:30
1
1
$begingroup$
That's everything sorted then. Thanks again and have a lovely holiday if you celebrate one. :)
$endgroup$
– dom_miketa
Dec 27 '18 at 18:26
$begingroup$
That's everything sorted then. Thanks again and have a lovely holiday if you celebrate one. :)
$endgroup$
– dom_miketa
Dec 27 '18 at 18:26
add a comment |
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