Can Calculating Cumulative Binomial Probabilities Be Simplified?

In summary, the probability that fewer than four of the 20 weld failures are base metal failures is 0.034, and there is no significantly faster way to solve for this probability than by using the recursive method.
  • #1
Mdhiggenz
327
1

Homework Statement



Of all the weld failures in a certain assembly, 85%
of them occur in the weld metal itself, and the remaining
15% occur in the base metal. A sample of
20 weld failures is examined.

a. What is the probability that fewer than four of
them are base metal failures?

Is there a faster way to solve rather than doing p(x=0)+p(x=1)+p(x=2)+p(x=3)?

Thanks

Brandon


Homework Equations





The Attempt at a Solution

 
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  • #2
Mdhiggenz said:

Homework Statement



Of all the weld failures in a certain assembly, 85%
of them occur in the weld metal itself, and the remaining
15% occur in the base metal. A sample of
20 weld failures is examined.

a. What is the probability that fewer than four of
them are base metal failures?

Is there a faster way to solve rather than doing p(x=0)+p(x=1)+p(x=2)+p(x=3)?

Thanks

Brandon


Homework Equations





The Attempt at a Solution


No, the way you said is about as short as possible. You can sometines speed things up a bit by doing it recursively: if
[tex] P(k) = {n \choose k} p^k (1-p)^{n-k}[/tex]
we have
[tex] \frac{P(k+1)}{P(k)} = r(k) \equiv \frac{n-k}{k+1} \frac{p}{1-p},[/tex]
so if we start from ##P(0) = (1-p)^n##, we can get ##P(1) = r(0) P(0),## ##P(2) = r(1) P(1),## etc.
 

Related to Can Calculating Cumulative Binomial Probabilities Be Simplified?

What is the Binomial distribution?

The Binomial distribution is a probability distribution that describes the number of successes in a sequence of n independent trials, where each trial has a binary outcome (success or failure) with a constant probability of success p.

What are some common applications of the Binomial distribution?

The Binomial distribution is commonly used in various fields, including statistics, finance, and biology. It is often used to model the number of successes in a fixed number of trials, such as the number of heads in a series of coin tosses, the number of defective products in a sample, or the number of patients who respond to a particular treatment.

How is the Binomial distribution different from other probability distributions?

The Binomial distribution is unique in that it models a discrete random variable (the number of successes) with a fixed number of trials. Other common probability distributions, such as the Normal distribution or Poisson distribution, model continuous random variables or events that occur in a continuous interval of time or space.

What is the formula for calculating the probability of a certain number of successes in the Binomial distribution?

The formula for calculating the probability of x successes in n trials of a Binomial distribution with probability of success p is:
P(X = x) = (n choose x) * px * (1-p)n-x, where (n choose x) represents the number of ways to choose x objects from a set of n objects.

Can the Binomial distribution be approximated by other distributions?

Yes, for large values of n and p close to 0.5, the Binomial distribution can be approximated by the Normal distribution. This is known as the Normal approximation to the Binomial distribution, and it is often used to simplify calculations and make approximations in real-world applications.

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