Expected time to roll all 1 through 6 on a die












76












$begingroup$


What is the average number of times it would it take to roll a fair 6-sided die and get all numbers on the die? The order in which the numbers appear does not matter.



I had this questions explained to me by a professor (not math professor), but it was not clear in the explanation. We were given the answer $(1-(frac56)^n)^6 = .5$ or $n = 12.152$



Can someone please explain this to me, possibly with a link to a general topic?










share|cite|improve this question











$endgroup$








  • 21




    $begingroup$
    This is the coupon-collector's problem (en.wikipedia.org/wiki/Coupon_collector%27s_problem), but with dice. So there's your link to a general topic. :)
    $endgroup$
    – Mike Spivey
    Mar 24 '11 at 23:49








  • 13




    $begingroup$
    I'm glad that the justification for that formula which produces $12.152$ was unclear, since it's wrong, and it looks like more than one conceptual error went into it.
    $endgroup$
    – Douglas Zare
    Mar 25 '11 at 2:34






  • 1




    $begingroup$
    If anyone's curious, simulating reveals $E[$time until all values rolled$]$ for two dice is roughly $61.2$.
    $endgroup$
    – ninjagecko
    Jun 8 '11 at 20:35












  • $begingroup$
    @DouglasZare Performing a little algebromancy... (a) trying to find the point at which a probability becomes 50% and presuming that's the expectation (i.e., confusing mean for median); (b) Assuming that the probabilities each number is rolled is independent? If you assume independence, that gives the LHS: $1-left(frac56right)^n$ gives the probability that e.g. 1 has been rolled after $n$ rolls; now include one of those factors for each of the six faces...
    $endgroup$
    – Steven Stadnicki
    Jul 30 '14 at 15:36
















76












$begingroup$


What is the average number of times it would it take to roll a fair 6-sided die and get all numbers on the die? The order in which the numbers appear does not matter.



I had this questions explained to me by a professor (not math professor), but it was not clear in the explanation. We were given the answer $(1-(frac56)^n)^6 = .5$ or $n = 12.152$



Can someone please explain this to me, possibly with a link to a general topic?










share|cite|improve this question











$endgroup$








  • 21




    $begingroup$
    This is the coupon-collector's problem (en.wikipedia.org/wiki/Coupon_collector%27s_problem), but with dice. So there's your link to a general topic. :)
    $endgroup$
    – Mike Spivey
    Mar 24 '11 at 23:49








  • 13




    $begingroup$
    I'm glad that the justification for that formula which produces $12.152$ was unclear, since it's wrong, and it looks like more than one conceptual error went into it.
    $endgroup$
    – Douglas Zare
    Mar 25 '11 at 2:34






  • 1




    $begingroup$
    If anyone's curious, simulating reveals $E[$time until all values rolled$]$ for two dice is roughly $61.2$.
    $endgroup$
    – ninjagecko
    Jun 8 '11 at 20:35












  • $begingroup$
    @DouglasZare Performing a little algebromancy... (a) trying to find the point at which a probability becomes 50% and presuming that's the expectation (i.e., confusing mean for median); (b) Assuming that the probabilities each number is rolled is independent? If you assume independence, that gives the LHS: $1-left(frac56right)^n$ gives the probability that e.g. 1 has been rolled after $n$ rolls; now include one of those factors for each of the six faces...
    $endgroup$
    – Steven Stadnicki
    Jul 30 '14 at 15:36














76












76








76


44



$begingroup$


What is the average number of times it would it take to roll a fair 6-sided die and get all numbers on the die? The order in which the numbers appear does not matter.



I had this questions explained to me by a professor (not math professor), but it was not clear in the explanation. We were given the answer $(1-(frac56)^n)^6 = .5$ or $n = 12.152$



Can someone please explain this to me, possibly with a link to a general topic?










share|cite|improve this question











$endgroup$




What is the average number of times it would it take to roll a fair 6-sided die and get all numbers on the die? The order in which the numbers appear does not matter.



I had this questions explained to me by a professor (not math professor), but it was not clear in the explanation. We were given the answer $(1-(frac56)^n)^6 = .5$ or $n = 12.152$



Can someone please explain this to me, possibly with a link to a general topic?







probability dice coupon-collector






share|cite|improve this question















share|cite|improve this question













share|cite|improve this question




share|cite|improve this question








edited Apr 30 '17 at 4:30









Community

1




1










asked Mar 24 '11 at 23:42









eternalmatteternalmatt

525158




525158








  • 21




    $begingroup$
    This is the coupon-collector's problem (en.wikipedia.org/wiki/Coupon_collector%27s_problem), but with dice. So there's your link to a general topic. :)
    $endgroup$
    – Mike Spivey
    Mar 24 '11 at 23:49








  • 13




    $begingroup$
    I'm glad that the justification for that formula which produces $12.152$ was unclear, since it's wrong, and it looks like more than one conceptual error went into it.
    $endgroup$
    – Douglas Zare
    Mar 25 '11 at 2:34






  • 1




    $begingroup$
    If anyone's curious, simulating reveals $E[$time until all values rolled$]$ for two dice is roughly $61.2$.
    $endgroup$
    – ninjagecko
    Jun 8 '11 at 20:35












  • $begingroup$
    @DouglasZare Performing a little algebromancy... (a) trying to find the point at which a probability becomes 50% and presuming that's the expectation (i.e., confusing mean for median); (b) Assuming that the probabilities each number is rolled is independent? If you assume independence, that gives the LHS: $1-left(frac56right)^n$ gives the probability that e.g. 1 has been rolled after $n$ rolls; now include one of those factors for each of the six faces...
    $endgroup$
    – Steven Stadnicki
    Jul 30 '14 at 15:36














  • 21




    $begingroup$
    This is the coupon-collector's problem (en.wikipedia.org/wiki/Coupon_collector%27s_problem), but with dice. So there's your link to a general topic. :)
    $endgroup$
    – Mike Spivey
    Mar 24 '11 at 23:49








  • 13




    $begingroup$
    I'm glad that the justification for that formula which produces $12.152$ was unclear, since it's wrong, and it looks like more than one conceptual error went into it.
    $endgroup$
    – Douglas Zare
    Mar 25 '11 at 2:34






  • 1




    $begingroup$
    If anyone's curious, simulating reveals $E[$time until all values rolled$]$ for two dice is roughly $61.2$.
    $endgroup$
    – ninjagecko
    Jun 8 '11 at 20:35












  • $begingroup$
    @DouglasZare Performing a little algebromancy... (a) trying to find the point at which a probability becomes 50% and presuming that's the expectation (i.e., confusing mean for median); (b) Assuming that the probabilities each number is rolled is independent? If you assume independence, that gives the LHS: $1-left(frac56right)^n$ gives the probability that e.g. 1 has been rolled after $n$ rolls; now include one of those factors for each of the six faces...
    $endgroup$
    – Steven Stadnicki
    Jul 30 '14 at 15:36








21




21




$begingroup$
This is the coupon-collector's problem (en.wikipedia.org/wiki/Coupon_collector%27s_problem), but with dice. So there's your link to a general topic. :)
$endgroup$
– Mike Spivey
Mar 24 '11 at 23:49






$begingroup$
This is the coupon-collector's problem (en.wikipedia.org/wiki/Coupon_collector%27s_problem), but with dice. So there's your link to a general topic. :)
$endgroup$
– Mike Spivey
Mar 24 '11 at 23:49






13




13




$begingroup$
I'm glad that the justification for that formula which produces $12.152$ was unclear, since it's wrong, and it looks like more than one conceptual error went into it.
$endgroup$
– Douglas Zare
Mar 25 '11 at 2:34




$begingroup$
I'm glad that the justification for that formula which produces $12.152$ was unclear, since it's wrong, and it looks like more than one conceptual error went into it.
$endgroup$
– Douglas Zare
Mar 25 '11 at 2:34




1




1




$begingroup$
If anyone's curious, simulating reveals $E[$time until all values rolled$]$ for two dice is roughly $61.2$.
$endgroup$
– ninjagecko
Jun 8 '11 at 20:35






$begingroup$
If anyone's curious, simulating reveals $E[$time until all values rolled$]$ for two dice is roughly $61.2$.
$endgroup$
– ninjagecko
Jun 8 '11 at 20:35














$begingroup$
@DouglasZare Performing a little algebromancy... (a) trying to find the point at which a probability becomes 50% and presuming that's the expectation (i.e., confusing mean for median); (b) Assuming that the probabilities each number is rolled is independent? If you assume independence, that gives the LHS: $1-left(frac56right)^n$ gives the probability that e.g. 1 has been rolled after $n$ rolls; now include one of those factors for each of the six faces...
$endgroup$
– Steven Stadnicki
Jul 30 '14 at 15:36




$begingroup$
@DouglasZare Performing a little algebromancy... (a) trying to find the point at which a probability becomes 50% and presuming that's the expectation (i.e., confusing mean for median); (b) Assuming that the probabilities each number is rolled is independent? If you assume independence, that gives the LHS: $1-left(frac56right)^n$ gives the probability that e.g. 1 has been rolled after $n$ rolls; now include one of those factors for each of the six faces...
$endgroup$
– Steven Stadnicki
Jul 30 '14 at 15:36










3 Answers
3






active

oldest

votes


















85












$begingroup$

The time until the first result appears is $1$. After that, the random time until a second (different) result appears is geometrically distributed with parameter of success $5/6$, hence with mean $6/5$ (recall that the mean of a geometrically distributed random variable is the inverse of its parameter). After that, the random time until a third (different) result appears is geometrically distributed with parameter of success $4/6$, hence with mean $6/4$. And so on, until the random time of appearance of the last and sixth result, which is geometrically distributed with parameter of success $1/6$, hence with mean $6/1$. This shows that the mean total time to get all six results is
$$sum_{k=1}^6frac6k=frac{147}{10}=14.7.$$





Edit: This is called the coupon collector problem. For a fair $n$-sided die, the expected number of attempts needed to get all $n$ values is
$$nsum_{k=1}^nfrac1k,$$ which, for large $n$, is approximately $nlog n$. This stands in contrast with the mean time needed to complete only some proportion $cn$ of the whole collection, for some fixed $c$ in $(0,1)$, which, for large $n$, is approximately $-log(1-c)n$. One sees that most of the $nlog n$ steps needed to complete the full collection are actually spent completing the last one per cent, or the last one per thousand, or the last whatever percentage of the collection.






share|cite|improve this answer











$endgroup$













  • $begingroup$
    Follow-up question, Didier: what's the distribution? :)
    $endgroup$
    – Jérémie
    May 8 '12 at 23:20












  • $begingroup$
    @Jérémie Somehow my post gives the answer but if need be, just ask a new question.
    $endgroup$
    – Did
    May 8 '12 at 23:42










  • $begingroup$
    @Jérémie: See math.stackexchange.com/questions/379525.
    $endgroup$
    – joriki
    May 14 '16 at 18:01



















37












$begingroup$

Here's the logic:



The chance of rolling a number you haven't yet rolled when you start off is $1$, as any number would work. Once you've rolled this number, your chance of rolling a number you haven't yet rolled is $5/6$. Continuing in this manner, after you've rolled $n$ different numbers the chance of rolling one you haven't yet rolled is $(6-n)/6$.



You can figure out the mean time it takes for a result of probability $p$ to appear with a simple formula: $1/p$. Furthermore, the mean time it takes for multiple results to appear is the sum of the mean times for each individual result to occur.



This allows us to calculate the mean time required to roll every number:
$t = 1/1 + 6/5 + 6/4 + 6/3 + 6/2 + 6/1 = 1 + 12/10 + 15/10 + 2 + 3 + 6 = 12 + 27/10 = 14.7$






share|cite|improve this answer









$endgroup$





















    0












    $begingroup$

    Associate a success with each number appearing that has not appeared before. Let $X_i$ be the number of trials between the $i^{th}$ success and the $(i + 1)^{st}$ success.



    Let $X$ be the random variable representing the total number of trials required for the required event, and $E[X]$ be the required expected value.



    Then by linearity of Expectation, we have $E[X] = 1 + sum_{i=1}^{5}E[X_i]$.



    To calculate $E[X_i]$, consider the following,
    after receiving $i−1$ different numbers i.e after $i −1$ successes, each subsequent trial has probability $(6 − i)/6$ of getting a number that has not been appeared before.



    Therefore,the random variable $X_i$ is geometric with parameter $p_i = (6−i)/6$, therefore $E[X_i] = 1/p_i = 6/(6-i)$.



    It follows that $E[X] = 1 + 6sum_{i=1}^{5}1/i$.



    Hence $E[X]=14.7$.






    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









      85












      $begingroup$

      The time until the first result appears is $1$. After that, the random time until a second (different) result appears is geometrically distributed with parameter of success $5/6$, hence with mean $6/5$ (recall that the mean of a geometrically distributed random variable is the inverse of its parameter). After that, the random time until a third (different) result appears is geometrically distributed with parameter of success $4/6$, hence with mean $6/4$. And so on, until the random time of appearance of the last and sixth result, which is geometrically distributed with parameter of success $1/6$, hence with mean $6/1$. This shows that the mean total time to get all six results is
      $$sum_{k=1}^6frac6k=frac{147}{10}=14.7.$$





      Edit: This is called the coupon collector problem. For a fair $n$-sided die, the expected number of attempts needed to get all $n$ values is
      $$nsum_{k=1}^nfrac1k,$$ which, for large $n$, is approximately $nlog n$. This stands in contrast with the mean time needed to complete only some proportion $cn$ of the whole collection, for some fixed $c$ in $(0,1)$, which, for large $n$, is approximately $-log(1-c)n$. One sees that most of the $nlog n$ steps needed to complete the full collection are actually spent completing the last one per cent, or the last one per thousand, or the last whatever percentage of the collection.






      share|cite|improve this answer











      $endgroup$













      • $begingroup$
        Follow-up question, Didier: what's the distribution? :)
        $endgroup$
        – Jérémie
        May 8 '12 at 23:20












      • $begingroup$
        @Jérémie Somehow my post gives the answer but if need be, just ask a new question.
        $endgroup$
        – Did
        May 8 '12 at 23:42










      • $begingroup$
        @Jérémie: See math.stackexchange.com/questions/379525.
        $endgroup$
        – joriki
        May 14 '16 at 18:01
















      85












      $begingroup$

      The time until the first result appears is $1$. After that, the random time until a second (different) result appears is geometrically distributed with parameter of success $5/6$, hence with mean $6/5$ (recall that the mean of a geometrically distributed random variable is the inverse of its parameter). After that, the random time until a third (different) result appears is geometrically distributed with parameter of success $4/6$, hence with mean $6/4$. And so on, until the random time of appearance of the last and sixth result, which is geometrically distributed with parameter of success $1/6$, hence with mean $6/1$. This shows that the mean total time to get all six results is
      $$sum_{k=1}^6frac6k=frac{147}{10}=14.7.$$





      Edit: This is called the coupon collector problem. For a fair $n$-sided die, the expected number of attempts needed to get all $n$ values is
      $$nsum_{k=1}^nfrac1k,$$ which, for large $n$, is approximately $nlog n$. This stands in contrast with the mean time needed to complete only some proportion $cn$ of the whole collection, for some fixed $c$ in $(0,1)$, which, for large $n$, is approximately $-log(1-c)n$. One sees that most of the $nlog n$ steps needed to complete the full collection are actually spent completing the last one per cent, or the last one per thousand, or the last whatever percentage of the collection.






      share|cite|improve this answer











      $endgroup$













      • $begingroup$
        Follow-up question, Didier: what's the distribution? :)
        $endgroup$
        – Jérémie
        May 8 '12 at 23:20












      • $begingroup$
        @Jérémie Somehow my post gives the answer but if need be, just ask a new question.
        $endgroup$
        – Did
        May 8 '12 at 23:42










      • $begingroup$
        @Jérémie: See math.stackexchange.com/questions/379525.
        $endgroup$
        – joriki
        May 14 '16 at 18:01














      85












      85








      85





      $begingroup$

      The time until the first result appears is $1$. After that, the random time until a second (different) result appears is geometrically distributed with parameter of success $5/6$, hence with mean $6/5$ (recall that the mean of a geometrically distributed random variable is the inverse of its parameter). After that, the random time until a third (different) result appears is geometrically distributed with parameter of success $4/6$, hence with mean $6/4$. And so on, until the random time of appearance of the last and sixth result, which is geometrically distributed with parameter of success $1/6$, hence with mean $6/1$. This shows that the mean total time to get all six results is
      $$sum_{k=1}^6frac6k=frac{147}{10}=14.7.$$





      Edit: This is called the coupon collector problem. For a fair $n$-sided die, the expected number of attempts needed to get all $n$ values is
      $$nsum_{k=1}^nfrac1k,$$ which, for large $n$, is approximately $nlog n$. This stands in contrast with the mean time needed to complete only some proportion $cn$ of the whole collection, for some fixed $c$ in $(0,1)$, which, for large $n$, is approximately $-log(1-c)n$. One sees that most of the $nlog n$ steps needed to complete the full collection are actually spent completing the last one per cent, or the last one per thousand, or the last whatever percentage of the collection.






      share|cite|improve this answer











      $endgroup$



      The time until the first result appears is $1$. After that, the random time until a second (different) result appears is geometrically distributed with parameter of success $5/6$, hence with mean $6/5$ (recall that the mean of a geometrically distributed random variable is the inverse of its parameter). After that, the random time until a third (different) result appears is geometrically distributed with parameter of success $4/6$, hence with mean $6/4$. And so on, until the random time of appearance of the last and sixth result, which is geometrically distributed with parameter of success $1/6$, hence with mean $6/1$. This shows that the mean total time to get all six results is
      $$sum_{k=1}^6frac6k=frac{147}{10}=14.7.$$





      Edit: This is called the coupon collector problem. For a fair $n$-sided die, the expected number of attempts needed to get all $n$ values is
      $$nsum_{k=1}^nfrac1k,$$ which, for large $n$, is approximately $nlog n$. This stands in contrast with the mean time needed to complete only some proportion $cn$ of the whole collection, for some fixed $c$ in $(0,1)$, which, for large $n$, is approximately $-log(1-c)n$. One sees that most of the $nlog n$ steps needed to complete the full collection are actually spent completing the last one per cent, or the last one per thousand, or the last whatever percentage of the collection.







      share|cite|improve this answer














      share|cite|improve this answer



      share|cite|improve this answer








      edited Jun 11 '16 at 16:11

























      answered Mar 24 '11 at 23:47









      DidDid

      248k23225463




      248k23225463












      • $begingroup$
        Follow-up question, Didier: what's the distribution? :)
        $endgroup$
        – Jérémie
        May 8 '12 at 23:20












      • $begingroup$
        @Jérémie Somehow my post gives the answer but if need be, just ask a new question.
        $endgroup$
        – Did
        May 8 '12 at 23:42










      • $begingroup$
        @Jérémie: See math.stackexchange.com/questions/379525.
        $endgroup$
        – joriki
        May 14 '16 at 18:01


















      • $begingroup$
        Follow-up question, Didier: what's the distribution? :)
        $endgroup$
        – Jérémie
        May 8 '12 at 23:20












      • $begingroup$
        @Jérémie Somehow my post gives the answer but if need be, just ask a new question.
        $endgroup$
        – Did
        May 8 '12 at 23:42










      • $begingroup$
        @Jérémie: See math.stackexchange.com/questions/379525.
        $endgroup$
        – joriki
        May 14 '16 at 18:01
















      $begingroup$
      Follow-up question, Didier: what's the distribution? :)
      $endgroup$
      – Jérémie
      May 8 '12 at 23:20






      $begingroup$
      Follow-up question, Didier: what's the distribution? :)
      $endgroup$
      – Jérémie
      May 8 '12 at 23:20














      $begingroup$
      @Jérémie Somehow my post gives the answer but if need be, just ask a new question.
      $endgroup$
      – Did
      May 8 '12 at 23:42




      $begingroup$
      @Jérémie Somehow my post gives the answer but if need be, just ask a new question.
      $endgroup$
      – Did
      May 8 '12 at 23:42












      $begingroup$
      @Jérémie: See math.stackexchange.com/questions/379525.
      $endgroup$
      – joriki
      May 14 '16 at 18:01




      $begingroup$
      @Jérémie: See math.stackexchange.com/questions/379525.
      $endgroup$
      – joriki
      May 14 '16 at 18:01











      37












      $begingroup$

      Here's the logic:



      The chance of rolling a number you haven't yet rolled when you start off is $1$, as any number would work. Once you've rolled this number, your chance of rolling a number you haven't yet rolled is $5/6$. Continuing in this manner, after you've rolled $n$ different numbers the chance of rolling one you haven't yet rolled is $(6-n)/6$.



      You can figure out the mean time it takes for a result of probability $p$ to appear with a simple formula: $1/p$. Furthermore, the mean time it takes for multiple results to appear is the sum of the mean times for each individual result to occur.



      This allows us to calculate the mean time required to roll every number:
      $t = 1/1 + 6/5 + 6/4 + 6/3 + 6/2 + 6/1 = 1 + 12/10 + 15/10 + 2 + 3 + 6 = 12 + 27/10 = 14.7$






      share|cite|improve this answer









      $endgroup$


















        37












        $begingroup$

        Here's the logic:



        The chance of rolling a number you haven't yet rolled when you start off is $1$, as any number would work. Once you've rolled this number, your chance of rolling a number you haven't yet rolled is $5/6$. Continuing in this manner, after you've rolled $n$ different numbers the chance of rolling one you haven't yet rolled is $(6-n)/6$.



        You can figure out the mean time it takes for a result of probability $p$ to appear with a simple formula: $1/p$. Furthermore, the mean time it takes for multiple results to appear is the sum of the mean times for each individual result to occur.



        This allows us to calculate the mean time required to roll every number:
        $t = 1/1 + 6/5 + 6/4 + 6/3 + 6/2 + 6/1 = 1 + 12/10 + 15/10 + 2 + 3 + 6 = 12 + 27/10 = 14.7$






        share|cite|improve this answer









        $endgroup$
















          37












          37








          37





          $begingroup$

          Here's the logic:



          The chance of rolling a number you haven't yet rolled when you start off is $1$, as any number would work. Once you've rolled this number, your chance of rolling a number you haven't yet rolled is $5/6$. Continuing in this manner, after you've rolled $n$ different numbers the chance of rolling one you haven't yet rolled is $(6-n)/6$.



          You can figure out the mean time it takes for a result of probability $p$ to appear with a simple formula: $1/p$. Furthermore, the mean time it takes for multiple results to appear is the sum of the mean times for each individual result to occur.



          This allows us to calculate the mean time required to roll every number:
          $t = 1/1 + 6/5 + 6/4 + 6/3 + 6/2 + 6/1 = 1 + 12/10 + 15/10 + 2 + 3 + 6 = 12 + 27/10 = 14.7$






          share|cite|improve this answer









          $endgroup$



          Here's the logic:



          The chance of rolling a number you haven't yet rolled when you start off is $1$, as any number would work. Once you've rolled this number, your chance of rolling a number you haven't yet rolled is $5/6$. Continuing in this manner, after you've rolled $n$ different numbers the chance of rolling one you haven't yet rolled is $(6-n)/6$.



          You can figure out the mean time it takes for a result of probability $p$ to appear with a simple formula: $1/p$. Furthermore, the mean time it takes for multiple results to appear is the sum of the mean times for each individual result to occur.



          This allows us to calculate the mean time required to roll every number:
          $t = 1/1 + 6/5 + 6/4 + 6/3 + 6/2 + 6/1 = 1 + 12/10 + 15/10 + 2 + 3 + 6 = 12 + 27/10 = 14.7$







          share|cite|improve this answer












          share|cite|improve this answer



          share|cite|improve this answer










          answered Mar 24 '11 at 23:52









          Alex BeckerAlex Becker

          49.1k698161




          49.1k698161























              0












              $begingroup$

              Associate a success with each number appearing that has not appeared before. Let $X_i$ be the number of trials between the $i^{th}$ success and the $(i + 1)^{st}$ success.



              Let $X$ be the random variable representing the total number of trials required for the required event, and $E[X]$ be the required expected value.



              Then by linearity of Expectation, we have $E[X] = 1 + sum_{i=1}^{5}E[X_i]$.



              To calculate $E[X_i]$, consider the following,
              after receiving $i−1$ different numbers i.e after $i −1$ successes, each subsequent trial has probability $(6 − i)/6$ of getting a number that has not been appeared before.



              Therefore,the random variable $X_i$ is geometric with parameter $p_i = (6−i)/6$, therefore $E[X_i] = 1/p_i = 6/(6-i)$.



              It follows that $E[X] = 1 + 6sum_{i=1}^{5}1/i$.



              Hence $E[X]=14.7$.






              share|cite|improve this answer











              $endgroup$


















                0












                $begingroup$

                Associate a success with each number appearing that has not appeared before. Let $X_i$ be the number of trials between the $i^{th}$ success and the $(i + 1)^{st}$ success.



                Let $X$ be the random variable representing the total number of trials required for the required event, and $E[X]$ be the required expected value.



                Then by linearity of Expectation, we have $E[X] = 1 + sum_{i=1}^{5}E[X_i]$.



                To calculate $E[X_i]$, consider the following,
                after receiving $i−1$ different numbers i.e after $i −1$ successes, each subsequent trial has probability $(6 − i)/6$ of getting a number that has not been appeared before.



                Therefore,the random variable $X_i$ is geometric with parameter $p_i = (6−i)/6$, therefore $E[X_i] = 1/p_i = 6/(6-i)$.



                It follows that $E[X] = 1 + 6sum_{i=1}^{5}1/i$.



                Hence $E[X]=14.7$.






                share|cite|improve this answer











                $endgroup$
















                  0












                  0








                  0





                  $begingroup$

                  Associate a success with each number appearing that has not appeared before. Let $X_i$ be the number of trials between the $i^{th}$ success and the $(i + 1)^{st}$ success.



                  Let $X$ be the random variable representing the total number of trials required for the required event, and $E[X]$ be the required expected value.



                  Then by linearity of Expectation, we have $E[X] = 1 + sum_{i=1}^{5}E[X_i]$.



                  To calculate $E[X_i]$, consider the following,
                  after receiving $i−1$ different numbers i.e after $i −1$ successes, each subsequent trial has probability $(6 − i)/6$ of getting a number that has not been appeared before.



                  Therefore,the random variable $X_i$ is geometric with parameter $p_i = (6−i)/6$, therefore $E[X_i] = 1/p_i = 6/(6-i)$.



                  It follows that $E[X] = 1 + 6sum_{i=1}^{5}1/i$.



                  Hence $E[X]=14.7$.






                  share|cite|improve this answer











                  $endgroup$



                  Associate a success with each number appearing that has not appeared before. Let $X_i$ be the number of trials between the $i^{th}$ success and the $(i + 1)^{st}$ success.



                  Let $X$ be the random variable representing the total number of trials required for the required event, and $E[X]$ be the required expected value.



                  Then by linearity of Expectation, we have $E[X] = 1 + sum_{i=1}^{5}E[X_i]$.



                  To calculate $E[X_i]$, consider the following,
                  after receiving $i−1$ different numbers i.e after $i −1$ successes, each subsequent trial has probability $(6 − i)/6$ of getting a number that has not been appeared before.



                  Therefore,the random variable $X_i$ is geometric with parameter $p_i = (6−i)/6$, therefore $E[X_i] = 1/p_i = 6/(6-i)$.



                  It follows that $E[X] = 1 + 6sum_{i=1}^{5}1/i$.



                  Hence $E[X]=14.7$.







                  share|cite|improve this answer














                  share|cite|improve this answer



                  share|cite|improve this answer








                  edited Dec 24 '17 at 13:07

























                  answered Dec 24 '17 at 7:10









                  souravsourav

                  284




                  284






























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