Probability and Poisson Random Variable

In summary: So, in summary, the problem involves throwing two dice and determining the probability of getting a sum of 12. In 36 trials, the probability that the number of successes is greater than one can be calculated using the binomial distribution, with N = 36, p = 1/36, and q = 35/36. The probability of the event {n ≥ 2} is 0.2642.
  • #1
twoski
181
2

Homework Statement



A trial consists of throwing two dice. The result is counted as successful if the sum of
the outcomes is 12. What is the probability that the number of successes in 36 such trials
is greater than one? What is this probability if we approximate its value using the Poisson
random variable?

The Attempt at a Solution



So there is a 1/36 chance that throwing two dice results in a sum of 12. I'm not sure where to go from here though.
 
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  • #2
The trials are independent since you are doing each trial separately. What do you know about the probability of independent events?
 
  • #3
P(EF) = P(E)P(F), so i guess i'd say E represents the event that the sum of the dice is 12 and F is the event that more than one trial is a success?
 
  • #4
twoski said:

Homework Statement



A trial consists of throwing two dice. The result is counted as successful if the sum of
the outcomes is 12. What is the probability that the number of successes in 36 such trials
is greater than one? What is this probability if we approximate its value using the Poisson
random variable?

The Attempt at a Solution



So there is a 1/36 chance that throwing two dice results in a sum of 12. I'm not sure where to go from here though.

Until you understand the basics, you should work with a simpler case.

So, to start: suppose you do two independent trials. What is the probability that neither trial gives a '12'? What is the probability that both trials give a '12'? What is the probability that exactly one of the two trials gives a '12'?

Now think about what would happen if you had three independent trials, then four independent trials, etc.
 
  • #5
Bleh so apparently i need to be using a binomial distribution on this problem.

Inline8.gif


So i have

N = number of trials
p = probability of a success in a given trial
n = number of expected successful trials
q = 1 - p

N = 36,
p = 1/36,
1 = 35/36,
n >= 1

The problem is that i need an actual value for n, and n could be any number larger than 1 so it's throwing me off. How would i calculate this?
 
  • #6
twoski said:
Bleh so apparently i need to be using a binomial distribution on this problem.

Inline8.gif


So i have

N = number of trials
p = probability of a success in a given trial
n = number of expected successful trials
q = 1 - p

N = 36,
p = 1/36,
1 = 35/36,
n >= 1

The problem is that i need an actual value for n, and n could be any number larger than 1 so it's throwing me off. How would i calculate this?

The question asked for n > 1, not n >= 1. It makes a big difference. Anyway, you want the probability of the event {n ≥ 2} = {n = 2 or n = 3 or n = 4 or ... or n = 36}.
 
  • #7
By my calculations, n = 1 - [q^N] - [N*p*q^(N-1)]

which evaluates to be 0.2642.
 

FAQ: Probability and Poisson Random Variable

What is probability and how is it calculated?

Probability is the measure of the likelihood of an event occurring. It is calculated by dividing the number of favorable outcomes by the total number of outcomes.

What is a Poisson random variable?

A Poisson random variable is a discrete random variable that represents the number of times an event occurs in a given time interval or space when the events are independent and the average rate of occurrence is known.

What is the formula for calculating the probability of a Poisson random variable?

The formula for calculating the probability of a Poisson random variable is P(X=x) = (e^-λ * λ^x) / x!, where λ is the average rate of occurrence, x is the number of occurrences, and e is the mathematical constant approximately equal to 2.71828.

How is a Poisson random variable different from a binomial random variable?

A Poisson random variable is used to model the number of occurrences of an event in a continuous interval or space, while a binomial random variable is used to model the number of successes in a fixed number of independent trials. Additionally, the probability of success in a binomial random variable is constant, while the rate of occurrence in a Poisson random variable can vary.

What are the applications of Poisson random variables in real life?

Poisson random variables are commonly used in various fields such as insurance, finance, and telecommunications to model the number of claims, financial losses, or calls during a given time period. They are also used in biology to model the number of mutations in a DNA sequence and in physics to model the number of radioactive decays.

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