Binomial Probability problem.

In summary, the probability of finding the first defective engine between the 5th and 25th trial is 0.5843102 when randomly selecting engines and testing them one at a time. This can be calculated using the geometric distribution formula, summing for x from 5 to 25 of (.1)(.9)^(x-1).
  • #1
TheHamburgler1
39
0

Homework Statement


10% of engines manufactured on an assembly line are defective. If engines are randomly selected one at a time and tested, what is the probability that the first defective engine will be found between the 5th trial and the 25th trial, inclusive?


Homework Equations





The Attempt at a Solution


I believe this is just a binomial distribution with Bin(n,1) where n varies between 5 and 25.

[tex]\sum[/tex](nC1)(0.1)(.9)^(n-1)

This is way off because I am getting 6.47...

thanks ahead of time.
 
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  • #2
Hi TheHamburgler1! :smile:

No, it's just P(first is defective is the 5th) + … + P(first is defective is the 25th) :wink:
 
  • #3
tiny-tim said:
Hi TheHamburgler1! :smile:

No, it's just P(first is defective is the 5th) + … + P(first is defective is the 25th) :wink:

Would we not express each of those via Bin(n,.1) where x=1? If not, how would we express one of them?

Thanks
 
  • #4
Actually, this could be a Geometric distribution problem right? In that case we would sum x from 5 to 25 of (.1)(.9)^(x-1). This gives 0.5843102
 
  • #5
(just got up :zzz: …)
TheHamburgler1 said:
Actually, this could be a Geometric distribution problem right? In that case we would sum x from 5 to 25 of (.1)(.9)^(x-1).

Yup! :biggrin:

(and I assume you've used algebra to calculate that, and not 20 additions? :wink:)
 

What is a binomial probability problem?

A binomial probability problem is a type of probability problem that involves two possible outcomes (usually success or failure) and a fixed number of trials. It follows a specific formula and can be used to determine the likelihood of a certain number of successes occurring in a given number of trials.

How do you calculate binomial probability?

The formula for calculating binomial probability is P(x) = nCx * p^x * (1-p)^(n-x), where n is the number of trials, x is the number of successes, and p is the probability of success in each trial. This formula can also be input into a calculator or statistical software to quickly calculate the probability.

What are the conditions for a binomial experiment?

There are four conditions for a binomial experiment: 1) there are a fixed number of trials, 2) each trial has two possible outcomes, 3) the probability of success is the same for each trial, and 4) the trials are independent (the outcome of one trial does not affect the outcome of any other trial).

How is a binomial probability problem different from other types of probability problems?

Unlike other types of probability problems that involve continuous variables, binomial probability problems deal with discrete variables (success or failure). They also follow a specific formula and have specific conditions that must be met in order to use that formula.

What are some real-life applications of binomial probability problems?

Binomial probability problems are useful in many areas, including genetics, finance, and quality control. For example, in genetics, binomial probability can be used to determine the likelihood of certain traits being passed down from parents to offspring. In finance, it can be used to calculate the probability of a stock price increasing or decreasing. In quality control, it can be used to determine the probability of a product passing quality standards.

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