Find the probability distribution

In summary, the problem involves a box of 10 flashbulbs with 3 defective bulbs. A sample of 2 is randomly selected and tested, with X being the random variable representing the number of defective bulbs in the sample. The probability distribution of X is found, and the expected number of defective bulbs in a sample is calculated. However, there is an error in the calculation, as the probability of only one defective bulb is incorrect due to not considering both possible orders of the defective bulbs in the sample.
  • #1
Sucks@Physics
76
0

Homework Statement



A box of 10 flashbublbs contains 3 defective bublbs. A random sample of 2 is selcted and tested. Let X be the radom variable associated with the number of defective bulbs in the sample.

A) Find the probability distribution of X.

B) Find the expected number of defective bulbs in a sample.

I did this problem, I got 7/15 probability of there being no defective light bulbs, 1/15 of there being 2, but I got 7/30 of there being 1. The 7/30 is wrong but I don't know where I'm going wrong, please give me some help. Thanks in advance.

Homework Equations






The Attempt at a Solution



7/10*6/9 = 7/15
7/10*3/9 = 7/30
3/10*7/9 = 1/15
 
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  • #2
Sucks@Physics said:
7/10*6/9 = 7/15
7/10*3/9 = 7/30
3/10*7/9 = 1/15

Hi Sucks@Physics ! :smile:

(in the last line, you wrote a 7 instead of a 2 :wink:)

As you've probably noticed, the three probabilities should add up to 1.

Your 7/30 is the probability of the first one being defective, and the second one good … but you also need the probability of the first one being good, and the second one defective … that doubles it! :smile:
 
  • #3


Firstly, it is important to note that the probability distribution of X in this scenario follows a binomial distribution, since there are only two possible outcomes (defective or non-defective) for each tested bulb and the sample size is fixed at 2.

A) To find the probability distribution of X, we can use the binomial probability formula: P(X=k) = nCk * p^k * (1-p)^(n-k), where n is the sample size, p is the probability of success (in this case, finding a defective bulb), and k is the number of successes.

In this problem, n=2, p=3/10 (since there are 3 defective bulbs out of 10 total bulbs), and k can take on values of 0, 1, or 2. Therefore, the probability distribution is:

P(X=0) = 2C0 * (3/10)^0 * (7/10)^2 = 49/100

P(X=1) = 2C1 * (3/10)^1 * (7/10)^1 = 42/100

P(X=2) = 2C2 * (3/10)^2 * (7/10)^0 = 9/100

B) To find the expected number of defective bulbs in a sample, we can use the formula: E(X) = n * p, where n is the sample size and p is the probability of success.

In this problem, n=2 and p=3/10. Therefore, the expected number of defective bulbs in a sample is 2 * 3/10 = 0.6. This means that, on average, we would expect to find 0.6 defective bulbs in a sample of 2 bulbs.

In your attempt at the solution, it seems that you have mixed up the probabilities for finding 0 or 1 defective bulbs. The correct probabilities for P(X=0) and P(X=1) are 49/100 and 42/100, respectively. The probability for finding 2 defective bulbs is correct at 9/100.
 

What is a probability distribution?

A probability distribution is a mathematical function that describes the likelihood of different outcomes in a random experiment. It assigns probabilities to each possible outcome, often represented in a graph or table.

Why is it important to find the probability distribution?

Finding the probability distribution allows us to understand the likelihood of different outcomes in a random experiment, which is crucial for making informed decisions and predictions. It also helps us to identify any patterns or trends in the data.

How do you calculate the probability distribution?

The method for calculating the probability distribution depends on the type of experiment and the data available. In general, it involves determining all possible outcomes, assigning a probability to each outcome, and then summing the probabilities to ensure they add up to 1.

What is the difference between discrete and continuous probability distributions?

A discrete probability distribution is used for experiments with a finite or countable number of outcomes, while a continuous probability distribution is used for experiments with an infinite number of possible outcomes. Discrete distributions are represented by probability mass functions, while continuous distributions are represented by probability density functions.

How can I use the probability distribution in real life?

The probability distribution is used in various fields, including statistics, economics, and engineering. In real life, we can use it to make informed decisions, such as predicting the likelihood of success for a business venture or the probability of winning a game of chance. It can also be used to analyze data and identify patterns or trends.

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