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Reverse Poisson Distribution problem


Number of calls following a poisson distributionPoisson distributionIs this a conditional probability or not?Exponential distribution helpLink between exponential distribution and poisson probability mass functionPoisson(Exponential) Distribution questionClarification on Poisson distributionProbability Statistics :Probability Poisson distribution customer servicePoisson Distribution question solving













0












$begingroup$


I was recently solving a quiz on Poisson distribution and I encountered this question




A call center receives an average of • 4.5 calls every 5 minutes. Each agent can handle one of these calls over the 5 minute period. If a call is received, but no agent is available to take it, then that caller will be placed on hold. Assuming that the calls follow a Poisson distribution, what is the minimum number of agents needed on duty so that calls are placed on hold at most 10% of the time?




I figured out this was a case of finding K while we are given the expected probability in the question itself.




what is the minimum number of agents needed on duty so that calls are
placed on hold at most 10% of the time?




I could not event determine the lambda and X to solve this question.
PS : The correct answer was 7 agents.A detailed explanation will be helpful.



Thanks










share|cite|improve this question







New contributor




Piyush Dixit is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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    0












    $begingroup$


    I was recently solving a quiz on Poisson distribution and I encountered this question




    A call center receives an average of • 4.5 calls every 5 minutes. Each agent can handle one of these calls over the 5 minute period. If a call is received, but no agent is available to take it, then that caller will be placed on hold. Assuming that the calls follow a Poisson distribution, what is the minimum number of agents needed on duty so that calls are placed on hold at most 10% of the time?




    I figured out this was a case of finding K while we are given the expected probability in the question itself.




    what is the minimum number of agents needed on duty so that calls are
    placed on hold at most 10% of the time?




    I could not event determine the lambda and X to solve this question.
    PS : The correct answer was 7 agents.A detailed explanation will be helpful.



    Thanks










    share|cite|improve this question







    New contributor




    Piyush Dixit is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.







    $endgroup$














      0












      0








      0





      $begingroup$


      I was recently solving a quiz on Poisson distribution and I encountered this question




      A call center receives an average of • 4.5 calls every 5 minutes. Each agent can handle one of these calls over the 5 minute period. If a call is received, but no agent is available to take it, then that caller will be placed on hold. Assuming that the calls follow a Poisson distribution, what is the minimum number of agents needed on duty so that calls are placed on hold at most 10% of the time?




      I figured out this was a case of finding K while we are given the expected probability in the question itself.




      what is the minimum number of agents needed on duty so that calls are
      placed on hold at most 10% of the time?




      I could not event determine the lambda and X to solve this question.
      PS : The correct answer was 7 agents.A detailed explanation will be helpful.



      Thanks










      share|cite|improve this question







      New contributor




      Piyush Dixit is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.







      $endgroup$




      I was recently solving a quiz on Poisson distribution and I encountered this question




      A call center receives an average of • 4.5 calls every 5 minutes. Each agent can handle one of these calls over the 5 minute period. If a call is received, but no agent is available to take it, then that caller will be placed on hold. Assuming that the calls follow a Poisson distribution, what is the minimum number of agents needed on duty so that calls are placed on hold at most 10% of the time?




      I figured out this was a case of finding K while we are given the expected probability in the question itself.




      what is the minimum number of agents needed on duty so that calls are
      placed on hold at most 10% of the time?




      I could not event determine the lambda and X to solve this question.
      PS : The correct answer was 7 agents.A detailed explanation will be helpful.



      Thanks







      statistics probability-distributions poisson-distribution






      share|cite|improve this question







      New contributor




      Piyush Dixit is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.











      share|cite|improve this question







      New contributor




      Piyush Dixit is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.









      share|cite|improve this question




      share|cite|improve this question






      New contributor




      Piyush Dixit is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.









      asked Mar 11 at 9:32









      Piyush DixitPiyush Dixit

      33




      33




      New contributor




      Piyush Dixit is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.





      New contributor





      Piyush Dixit is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.






      Piyush Dixit is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.




















          1 Answer
          1






          active

          oldest

          votes


















          0












          $begingroup$

          I believe you are being asked to focus on a typical 5-minute period of time.
          Then the average number $X$ of incoming calls within 5 minutes has the distribution $X sim mathsfPois(lambda = 4.5).$ A bar plot of this distribution is shown below:



          enter image description here



          Poisson Model: Computations with the Poisson PDF $P(X = i) = e^-lambdafraclambda^ii!,$ where $lambda = 4.5,$ show that $P(X le 6) = 0.8311 < 0.9$ and
          $P(X le 7) = 0.9134.$ Thus seven agents would suffice to serve
          incoming customers at least 90% of the time.



          Trial and error with Poisson CDF: Computations using
          R statistical software (where the CDF of a Poisson distribution is denoted ppois) are as follows:



          ppois(6, 4.5)
          ## 0.8310506
          ppois(7, 4.5)
          ## 0.9134135


          Using a Poisson quantile function: If you have such software available, you can use the inverse CDF
          or quantile function qpois to get to the answer without
          exploration.



          qpois(.9, 4.5)
          ## 7


          Note: Without knowing the context of this problem in your course, I suppose something like this is the approach you are expected to take. However, there
          are some unstated assumptions involved. A complete analysis of such
          a problem would involve finding the required number of servers $k$ required in an M/M/k queue (at steady state) that are sufficient to keep the average number of customers in the
          system below $k$ 90% of the time. This would involve knowing the
          exponential arrival rate $lambda = 4.5$ per five minutes or $0.9$ per
          minute, and knowing the exponential rate (often denoted $mu)$ at
          which each server can finish serving customers.



          For starters: here are a few unstated assumptions: (a) No agents are busy at the beginning of the 5-minute period. (b) Each agent handles only one call within this period. (c) We are not concerned whether
          agents are still handling calls from this 5-minute period ends and the next begins.






          share|cite|improve this answer











          $endgroup$












          • $begingroup$
            maybe I was making too many assumptions at first and did not look it in a simplistic manner. I did try it using a cdf with hit and trial like you mentioned, but accepting lambda to be 4.5 instead of 0.9 was not clear to me. Considering the given assumptions, I believe you have provided the simplest solution to this. Thanks a lot
            $endgroup$
            – Piyush Dixit
            yesterday











          Your Answer





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          1 Answer
          1






          active

          oldest

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          1 Answer
          1






          active

          oldest

          votes









          active

          oldest

          votes






          active

          oldest

          votes









          0












          $begingroup$

          I believe you are being asked to focus on a typical 5-minute period of time.
          Then the average number $X$ of incoming calls within 5 minutes has the distribution $X sim mathsfPois(lambda = 4.5).$ A bar plot of this distribution is shown below:



          enter image description here



          Poisson Model: Computations with the Poisson PDF $P(X = i) = e^-lambdafraclambda^ii!,$ where $lambda = 4.5,$ show that $P(X le 6) = 0.8311 < 0.9$ and
          $P(X le 7) = 0.9134.$ Thus seven agents would suffice to serve
          incoming customers at least 90% of the time.



          Trial and error with Poisson CDF: Computations using
          R statistical software (where the CDF of a Poisson distribution is denoted ppois) are as follows:



          ppois(6, 4.5)
          ## 0.8310506
          ppois(7, 4.5)
          ## 0.9134135


          Using a Poisson quantile function: If you have such software available, you can use the inverse CDF
          or quantile function qpois to get to the answer without
          exploration.



          qpois(.9, 4.5)
          ## 7


          Note: Without knowing the context of this problem in your course, I suppose something like this is the approach you are expected to take. However, there
          are some unstated assumptions involved. A complete analysis of such
          a problem would involve finding the required number of servers $k$ required in an M/M/k queue (at steady state) that are sufficient to keep the average number of customers in the
          system below $k$ 90% of the time. This would involve knowing the
          exponential arrival rate $lambda = 4.5$ per five minutes or $0.9$ per
          minute, and knowing the exponential rate (often denoted $mu)$ at
          which each server can finish serving customers.



          For starters: here are a few unstated assumptions: (a) No agents are busy at the beginning of the 5-minute period. (b) Each agent handles only one call within this period. (c) We are not concerned whether
          agents are still handling calls from this 5-minute period ends and the next begins.






          share|cite|improve this answer











          $endgroup$












          • $begingroup$
            maybe I was making too many assumptions at first and did not look it in a simplistic manner. I did try it using a cdf with hit and trial like you mentioned, but accepting lambda to be 4.5 instead of 0.9 was not clear to me. Considering the given assumptions, I believe you have provided the simplest solution to this. Thanks a lot
            $endgroup$
            – Piyush Dixit
            yesterday
















          0












          $begingroup$

          I believe you are being asked to focus on a typical 5-minute period of time.
          Then the average number $X$ of incoming calls within 5 minutes has the distribution $X sim mathsfPois(lambda = 4.5).$ A bar plot of this distribution is shown below:



          enter image description here



          Poisson Model: Computations with the Poisson PDF $P(X = i) = e^-lambdafraclambda^ii!,$ where $lambda = 4.5,$ show that $P(X le 6) = 0.8311 < 0.9$ and
          $P(X le 7) = 0.9134.$ Thus seven agents would suffice to serve
          incoming customers at least 90% of the time.



          Trial and error with Poisson CDF: Computations using
          R statistical software (where the CDF of a Poisson distribution is denoted ppois) are as follows:



          ppois(6, 4.5)
          ## 0.8310506
          ppois(7, 4.5)
          ## 0.9134135


          Using a Poisson quantile function: If you have such software available, you can use the inverse CDF
          or quantile function qpois to get to the answer without
          exploration.



          qpois(.9, 4.5)
          ## 7


          Note: Without knowing the context of this problem in your course, I suppose something like this is the approach you are expected to take. However, there
          are some unstated assumptions involved. A complete analysis of such
          a problem would involve finding the required number of servers $k$ required in an M/M/k queue (at steady state) that are sufficient to keep the average number of customers in the
          system below $k$ 90% of the time. This would involve knowing the
          exponential arrival rate $lambda = 4.5$ per five minutes or $0.9$ per
          minute, and knowing the exponential rate (often denoted $mu)$ at
          which each server can finish serving customers.



          For starters: here are a few unstated assumptions: (a) No agents are busy at the beginning of the 5-minute period. (b) Each agent handles only one call within this period. (c) We are not concerned whether
          agents are still handling calls from this 5-minute period ends and the next begins.






          share|cite|improve this answer











          $endgroup$












          • $begingroup$
            maybe I was making too many assumptions at first and did not look it in a simplistic manner. I did try it using a cdf with hit and trial like you mentioned, but accepting lambda to be 4.5 instead of 0.9 was not clear to me. Considering the given assumptions, I believe you have provided the simplest solution to this. Thanks a lot
            $endgroup$
            – Piyush Dixit
            yesterday














          0












          0








          0





          $begingroup$

          I believe you are being asked to focus on a typical 5-minute period of time.
          Then the average number $X$ of incoming calls within 5 minutes has the distribution $X sim mathsfPois(lambda = 4.5).$ A bar plot of this distribution is shown below:



          enter image description here



          Poisson Model: Computations with the Poisson PDF $P(X = i) = e^-lambdafraclambda^ii!,$ where $lambda = 4.5,$ show that $P(X le 6) = 0.8311 < 0.9$ and
          $P(X le 7) = 0.9134.$ Thus seven agents would suffice to serve
          incoming customers at least 90% of the time.



          Trial and error with Poisson CDF: Computations using
          R statistical software (where the CDF of a Poisson distribution is denoted ppois) are as follows:



          ppois(6, 4.5)
          ## 0.8310506
          ppois(7, 4.5)
          ## 0.9134135


          Using a Poisson quantile function: If you have such software available, you can use the inverse CDF
          or quantile function qpois to get to the answer without
          exploration.



          qpois(.9, 4.5)
          ## 7


          Note: Without knowing the context of this problem in your course, I suppose something like this is the approach you are expected to take. However, there
          are some unstated assumptions involved. A complete analysis of such
          a problem would involve finding the required number of servers $k$ required in an M/M/k queue (at steady state) that are sufficient to keep the average number of customers in the
          system below $k$ 90% of the time. This would involve knowing the
          exponential arrival rate $lambda = 4.5$ per five minutes or $0.9$ per
          minute, and knowing the exponential rate (often denoted $mu)$ at
          which each server can finish serving customers.



          For starters: here are a few unstated assumptions: (a) No agents are busy at the beginning of the 5-minute period. (b) Each agent handles only one call within this period. (c) We are not concerned whether
          agents are still handling calls from this 5-minute period ends and the next begins.






          share|cite|improve this answer











          $endgroup$



          I believe you are being asked to focus on a typical 5-minute period of time.
          Then the average number $X$ of incoming calls within 5 minutes has the distribution $X sim mathsfPois(lambda = 4.5).$ A bar plot of this distribution is shown below:



          enter image description here



          Poisson Model: Computations with the Poisson PDF $P(X = i) = e^-lambdafraclambda^ii!,$ where $lambda = 4.5,$ show that $P(X le 6) = 0.8311 < 0.9$ and
          $P(X le 7) = 0.9134.$ Thus seven agents would suffice to serve
          incoming customers at least 90% of the time.



          Trial and error with Poisson CDF: Computations using
          R statistical software (where the CDF of a Poisson distribution is denoted ppois) are as follows:



          ppois(6, 4.5)
          ## 0.8310506
          ppois(7, 4.5)
          ## 0.9134135


          Using a Poisson quantile function: If you have such software available, you can use the inverse CDF
          or quantile function qpois to get to the answer without
          exploration.



          qpois(.9, 4.5)
          ## 7


          Note: Without knowing the context of this problem in your course, I suppose something like this is the approach you are expected to take. However, there
          are some unstated assumptions involved. A complete analysis of such
          a problem would involve finding the required number of servers $k$ required in an M/M/k queue (at steady state) that are sufficient to keep the average number of customers in the
          system below $k$ 90% of the time. This would involve knowing the
          exponential arrival rate $lambda = 4.5$ per five minutes or $0.9$ per
          minute, and knowing the exponential rate (often denoted $mu)$ at
          which each server can finish serving customers.



          For starters: here are a few unstated assumptions: (a) No agents are busy at the beginning of the 5-minute period. (b) Each agent handles only one call within this period. (c) We are not concerned whether
          agents are still handling calls from this 5-minute period ends and the next begins.







          share|cite|improve this answer














          share|cite|improve this answer



          share|cite|improve this answer








          edited 2 days ago

























          answered 2 days ago









          BruceETBruceET

          35.9k71540




          35.9k71540











          • $begingroup$
            maybe I was making too many assumptions at first and did not look it in a simplistic manner. I did try it using a cdf with hit and trial like you mentioned, but accepting lambda to be 4.5 instead of 0.9 was not clear to me. Considering the given assumptions, I believe you have provided the simplest solution to this. Thanks a lot
            $endgroup$
            – Piyush Dixit
            yesterday

















          • $begingroup$
            maybe I was making too many assumptions at first and did not look it in a simplistic manner. I did try it using a cdf with hit and trial like you mentioned, but accepting lambda to be 4.5 instead of 0.9 was not clear to me. Considering the given assumptions, I believe you have provided the simplest solution to this. Thanks a lot
            $endgroup$
            – Piyush Dixit
            yesterday
















          $begingroup$
          maybe I was making too many assumptions at first and did not look it in a simplistic manner. I did try it using a cdf with hit and trial like you mentioned, but accepting lambda to be 4.5 instead of 0.9 was not clear to me. Considering the given assumptions, I believe you have provided the simplest solution to this. Thanks a lot
          $endgroup$
          – Piyush Dixit
          yesterday





          $begingroup$
          maybe I was making too many assumptions at first and did not look it in a simplistic manner. I did try it using a cdf with hit and trial like you mentioned, but accepting lambda to be 4.5 instead of 0.9 was not clear to me. Considering the given assumptions, I believe you have provided the simplest solution to this. Thanks a lot
          $endgroup$
          – Piyush Dixit
          yesterday











          Piyush Dixit is a new contributor. Be nice, and check out our Code of Conduct.









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          Piyush Dixit is a new contributor. Be nice, and check out our Code of Conduct.














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