Rolling a fair 6 sided die k times
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The question:
Suppose we roll a fair 6 sided die with the number [1,6] written on them. After the first die roll we roll the die $k$ times where $k$ is the number on the first die roll. The number of points you score is the sum of the face-values on all die rolls (including the first). What is the expected number of points you will score?
Attempt:
There are six different cases for the first roll
Case 1: Roll 1
$P(D_1=1) =frac{1}{6}$The expected number on the next roll is $E(D_2)=frac{1}{6}times(1+2+3+4+5+6)=3.5$
$E(S_1) = 3.5+1=4.5$ points
Case 2: Roll 2
We know what the expected value of one roll is, and since rolling the die is independent we can use the previous expected value for the next roll.
$E(S_2) = (3.5times 2)+2=9$ points
Case 3: Roll 3
$E(S_3) = (3.5times 3)+3=13.5$ points
Case 4: Roll 4
$E(S_4) = (3.5times 4)+4=18$ points
Case 5: Roll 5
$E(S_5) = (3.5times 5)+5=22.5$ points
Case 6: Roll 6
$E(S_6) = (3.5times 6)+6=27$ points
Therefore, the expected number of points scored is
$=frac{1}{6}times(4.5+9+13.5+18+22.5+27)=15.75$ points
probability combinatorics
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up vote
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The question:
Suppose we roll a fair 6 sided die with the number [1,6] written on them. After the first die roll we roll the die $k$ times where $k$ is the number on the first die roll. The number of points you score is the sum of the face-values on all die rolls (including the first). What is the expected number of points you will score?
Attempt:
There are six different cases for the first roll
Case 1: Roll 1
$P(D_1=1) =frac{1}{6}$The expected number on the next roll is $E(D_2)=frac{1}{6}times(1+2+3+4+5+6)=3.5$
$E(S_1) = 3.5+1=4.5$ points
Case 2: Roll 2
We know what the expected value of one roll is, and since rolling the die is independent we can use the previous expected value for the next roll.
$E(S_2) = (3.5times 2)+2=9$ points
Case 3: Roll 3
$E(S_3) = (3.5times 3)+3=13.5$ points
Case 4: Roll 4
$E(S_4) = (3.5times 4)+4=18$ points
Case 5: Roll 5
$E(S_5) = (3.5times 5)+5=22.5$ points
Case 6: Roll 6
$E(S_6) = (3.5times 6)+6=27$ points
Therefore, the expected number of points scored is
$=frac{1}{6}times(4.5+9+13.5+18+22.5+27)=15.75$ points
probability combinatorics
1
I think this is a law of total expectation problem, $E[X]=E[E(X|K)]$. Where $K$ is the face of the first roll, and $X$ is the sum of the $K$ rolls
– gd1035
Nov 16 at 15:49
1
I think your attempt is correct. That is how I would have tried it anyways.
– Sauhard Sharma
Nov 16 at 16:22
add a comment |
up vote
1
down vote
favorite
up vote
1
down vote
favorite
The question:
Suppose we roll a fair 6 sided die with the number [1,6] written on them. After the first die roll we roll the die $k$ times where $k$ is the number on the first die roll. The number of points you score is the sum of the face-values on all die rolls (including the first). What is the expected number of points you will score?
Attempt:
There are six different cases for the first roll
Case 1: Roll 1
$P(D_1=1) =frac{1}{6}$The expected number on the next roll is $E(D_2)=frac{1}{6}times(1+2+3+4+5+6)=3.5$
$E(S_1) = 3.5+1=4.5$ points
Case 2: Roll 2
We know what the expected value of one roll is, and since rolling the die is independent we can use the previous expected value for the next roll.
$E(S_2) = (3.5times 2)+2=9$ points
Case 3: Roll 3
$E(S_3) = (3.5times 3)+3=13.5$ points
Case 4: Roll 4
$E(S_4) = (3.5times 4)+4=18$ points
Case 5: Roll 5
$E(S_5) = (3.5times 5)+5=22.5$ points
Case 6: Roll 6
$E(S_6) = (3.5times 6)+6=27$ points
Therefore, the expected number of points scored is
$=frac{1}{6}times(4.5+9+13.5+18+22.5+27)=15.75$ points
probability combinatorics
The question:
Suppose we roll a fair 6 sided die with the number [1,6] written on them. After the first die roll we roll the die $k$ times where $k$ is the number on the first die roll. The number of points you score is the sum of the face-values on all die rolls (including the first). What is the expected number of points you will score?
Attempt:
There are six different cases for the first roll
Case 1: Roll 1
$P(D_1=1) =frac{1}{6}$The expected number on the next roll is $E(D_2)=frac{1}{6}times(1+2+3+4+5+6)=3.5$
$E(S_1) = 3.5+1=4.5$ points
Case 2: Roll 2
We know what the expected value of one roll is, and since rolling the die is independent we can use the previous expected value for the next roll.
$E(S_2) = (3.5times 2)+2=9$ points
Case 3: Roll 3
$E(S_3) = (3.5times 3)+3=13.5$ points
Case 4: Roll 4
$E(S_4) = (3.5times 4)+4=18$ points
Case 5: Roll 5
$E(S_5) = (3.5times 5)+5=22.5$ points
Case 6: Roll 6
$E(S_6) = (3.5times 6)+6=27$ points
Therefore, the expected number of points scored is
$=frac{1}{6}times(4.5+9+13.5+18+22.5+27)=15.75$ points
probability combinatorics
probability combinatorics
asked Nov 16 at 15:41
Arthur Green
656
656
1
I think this is a law of total expectation problem, $E[X]=E[E(X|K)]$. Where $K$ is the face of the first roll, and $X$ is the sum of the $K$ rolls
– gd1035
Nov 16 at 15:49
1
I think your attempt is correct. That is how I would have tried it anyways.
– Sauhard Sharma
Nov 16 at 16:22
add a comment |
1
I think this is a law of total expectation problem, $E[X]=E[E(X|K)]$. Where $K$ is the face of the first roll, and $X$ is the sum of the $K$ rolls
– gd1035
Nov 16 at 15:49
1
I think your attempt is correct. That is how I would have tried it anyways.
– Sauhard Sharma
Nov 16 at 16:22
1
1
I think this is a law of total expectation problem, $E[X]=E[E(X|K)]$. Where $K$ is the face of the first roll, and $X$ is the sum of the $K$ rolls
– gd1035
Nov 16 at 15:49
I think this is a law of total expectation problem, $E[X]=E[E(X|K)]$. Where $K$ is the face of the first roll, and $X$ is the sum of the $K$ rolls
– gd1035
Nov 16 at 15:49
1
1
I think your attempt is correct. That is how I would have tried it anyways.
– Sauhard Sharma
Nov 16 at 16:22
I think your attempt is correct. That is how I would have tried it anyways.
– Sauhard Sharma
Nov 16 at 16:22
add a comment |
1 Answer
1
active
oldest
votes
up vote
1
down vote
accepted
The expected value of a sum of random number $N$ of iid random variables $X_i$ is
$$Eleft[sum_{i=1}^N X_iright]=E[N]E[X_i]$$
In your case you add $E[N]$, so the answer is
$$E[N]E[X_i]+E[N]=3.5cdot 3.5+3.5 =4.5cdot 3.5 = 15.75$$
Why do we add $E[N]$ to the summation?
– Arthur Green
Nov 16 at 19:13
1
Because you include the 1st (actually zeroth) roll which determines $N$ into the sum, and because of linearity of expectations it adds $E[N]$.
– kludg
Nov 16 at 20:05
add a comment |
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
1
down vote
accepted
The expected value of a sum of random number $N$ of iid random variables $X_i$ is
$$Eleft[sum_{i=1}^N X_iright]=E[N]E[X_i]$$
In your case you add $E[N]$, so the answer is
$$E[N]E[X_i]+E[N]=3.5cdot 3.5+3.5 =4.5cdot 3.5 = 15.75$$
Why do we add $E[N]$ to the summation?
– Arthur Green
Nov 16 at 19:13
1
Because you include the 1st (actually zeroth) roll which determines $N$ into the sum, and because of linearity of expectations it adds $E[N]$.
– kludg
Nov 16 at 20:05
add a comment |
up vote
1
down vote
accepted
The expected value of a sum of random number $N$ of iid random variables $X_i$ is
$$Eleft[sum_{i=1}^N X_iright]=E[N]E[X_i]$$
In your case you add $E[N]$, so the answer is
$$E[N]E[X_i]+E[N]=3.5cdot 3.5+3.5 =4.5cdot 3.5 = 15.75$$
Why do we add $E[N]$ to the summation?
– Arthur Green
Nov 16 at 19:13
1
Because you include the 1st (actually zeroth) roll which determines $N$ into the sum, and because of linearity of expectations it adds $E[N]$.
– kludg
Nov 16 at 20:05
add a comment |
up vote
1
down vote
accepted
up vote
1
down vote
accepted
The expected value of a sum of random number $N$ of iid random variables $X_i$ is
$$Eleft[sum_{i=1}^N X_iright]=E[N]E[X_i]$$
In your case you add $E[N]$, so the answer is
$$E[N]E[X_i]+E[N]=3.5cdot 3.5+3.5 =4.5cdot 3.5 = 15.75$$
The expected value of a sum of random number $N$ of iid random variables $X_i$ is
$$Eleft[sum_{i=1}^N X_iright]=E[N]E[X_i]$$
In your case you add $E[N]$, so the answer is
$$E[N]E[X_i]+E[N]=3.5cdot 3.5+3.5 =4.5cdot 3.5 = 15.75$$
answered Nov 16 at 17:20
kludg
1,159611
1,159611
Why do we add $E[N]$ to the summation?
– Arthur Green
Nov 16 at 19:13
1
Because you include the 1st (actually zeroth) roll which determines $N$ into the sum, and because of linearity of expectations it adds $E[N]$.
– kludg
Nov 16 at 20:05
add a comment |
Why do we add $E[N]$ to the summation?
– Arthur Green
Nov 16 at 19:13
1
Because you include the 1st (actually zeroth) roll which determines $N$ into the sum, and because of linearity of expectations it adds $E[N]$.
– kludg
Nov 16 at 20:05
Why do we add $E[N]$ to the summation?
– Arthur Green
Nov 16 at 19:13
Why do we add $E[N]$ to the summation?
– Arthur Green
Nov 16 at 19:13
1
1
Because you include the 1st (actually zeroth) roll which determines $N$ into the sum, and because of linearity of expectations it adds $E[N]$.
– kludg
Nov 16 at 20:05
Because you include the 1st (actually zeroth) roll which determines $N$ into the sum, and because of linearity of expectations it adds $E[N]$.
– kludg
Nov 16 at 20:05
add a comment |
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1
I think this is a law of total expectation problem, $E[X]=E[E(X|K)]$. Where $K$ is the face of the first roll, and $X$ is the sum of the $K$ rolls
– gd1035
Nov 16 at 15:49
1
I think your attempt is correct. That is how I would have tried it anyways.
– Sauhard Sharma
Nov 16 at 16:22