Estimating the Mean for a Batch of 50 Items Using the Poisson Distribution

In summary, using the Poisson probability distribution, we can estimate that the probability of having 3 defective items in a batch of 50 items is approximately 0.09. The mean value for a batch of 50 items is estimated to be 4.8, which is half of the mean value for a batch of 100 items.
  • #1
naspek
181
0
A machine on average produces 4 defective items out of a batch of 100 items.
Find the probability that a batch of 50 items has 3 defective items in it using the Poisson probability distribution.

the problem is..
i just want to know the mean or average value for batch of 50 items..
i got mean = 2
because for 100 items, the mean is 4..
...for 50 items, the mean would be 2..
correct?
 
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  • #2
Here's how I would start it. Let the random variable X be the number of defective items in a batch of 50. P(X = 1) = 0.04.

Assuming that X is Poisson with mean lambda,
[tex]P(X = k)~= ~\frac{\lambda^k~e^{-k}}{k!}[/tex]
We also have P(X = 1) = 0.04, so using the equation above, I get
[tex]\frac{\lambda^1~e^{-\lambda}}{1}~=~0.04[/tex]

This isn't an equation that you can solve analytically, but you can use estimation techniques to get approximate values for lambda. In about a minute I got a value for lambda of about 4.8. The better you estimate for lambda is, the better your calculation for P(X = 3) will be.
 

1. What is Poisson probability distribution?

Poisson probability distribution is a statistical distribution that is used to model the number of times an event occurs in a given time period. It is often used to predict the likelihood of rare events, such as the number of accidents in a day, the number of customers arriving at a store, or the number of defects in a product.

2. How is Poisson distribution different from other probability distributions?

One key difference is that Poisson distribution assumes that the events occur independently and at a constant rate, whereas other distributions may have different assumptions about the relationship between events or the rate at which they occur. Poisson distribution is also only applicable for counting events, whereas other distributions may be used for continuous data.

3. How is the mean of a Poisson distribution calculated?

The mean of a Poisson distribution is equal to the rate at which events occur in a given time period. This is denoted by the parameter λ (lambda) and is also equal to the variance of the distribution.

4. What is the relationship between Poisson distribution and the binomial distribution?

The binomial distribution is often used to model the number of successes in a fixed number of trials, whereas Poisson distribution is used to model the number of events in a specific time period. However, as the number of trials in a binomial distribution increases, it approaches a Poisson distribution.

5. How is Poisson distribution applied in real-life situations?

Poisson distribution is commonly used in fields such as insurance, finance, and manufacturing to calculate the likelihood of rare events. For example, it can be used to predict the number of car accidents in a year for an insurance company, or the number of defects in a batch of products for a manufacturer.

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