Optimizing Bulb Replacement Frequency for Minimizing Burnouts

In summary, the manufacturing plant wants to minimize the number of bulbs that burn out during operating hours by replacing them after a given period of operation. The plant has 3000 light bulbs with a normally distributed length of life with a mean of 500 hours and a standard deviation of 50 hours. To ensure that no more than 1% of the bulbs burn out between replacement periods, the plant needs to determine how often the bulbs should be replaced. This can be solved by finding the time interval t at which the probability of a bulb being burned out is 0.01.
  • #1
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Homework Statement


A manufacturing plant utilizes 3000 electric light bulbs whose length of life is normally distributed
with mean 500 hours and standard deviation 50 hours. To minimize the number of
bulbs that burn out during operating hours, all the bulbs are replaced after a given period of
operation. How often should the bulbs be replaced if we want not more than 1% of the bulbs
to burn out between replacement periods?


Homework Equations





The Attempt at a Solution


I'm having great difficulty with this question. I'm confused as to how I can find how often the bulbs should be replaced (within what times interval?) I was wondering if this maybe is a combination of a normal distribution with an expontial ? I was thinking of finding standizing the distribution
P(Z <= x-500/50)= 0.01 and then simply solving for x. But I am not sure how that even helps me. Overall I'm just quite confused with this problem.

Thank you !
 
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  • #2
Suppose we pick one bulb out of the 3000, and replace it after time t. What is the probability that the bulb will be burned out at time t?

Now suppose that we use the same time t for all the bulbs. What percentage of the bulbs will be burned out at time t? (Hint: it's the same as the first question)

Our answer should be a function of t. Then we just find the value of t such that f(t) = 0.01.
 

What is a Normal Distribution Problem?

A Normal Distribution Problem is a statistical problem that involves a data set that follows a normal distribution, also known as a bell curve. This means that the data is evenly distributed around the mean, with most of the data falling close to the mean and fewer data points further away from the mean.

How do you identify a Normal Distribution?

You can identify a Normal Distribution by looking at a histogram of the data. A Normal Distribution will have a bell-shaped curve, with the data clustered around the mean and tapering off on either side. You can also use the mean and standard deviation of the data to determine if it follows a normal distribution.

What is the significance of Normal Distribution?

Normal Distribution is significant because it is very common in nature and in many real-world phenomena. It allows us to make predictions and draw conclusions about a population based on a sample of data that follows a normal distribution. It is also the basis for many statistical tests and methods.

How do you solve a Normal Distribution Problem?

To solve a Normal Distribution Problem, you first need to determine the mean and standard deviation of the data set. Then, you can use a z-table or a statistical software to find the probability of a certain value or range of values occurring within the data set. You can also use the mean and standard deviation to calculate z-scores and determine the percentage of data points that fall within a certain number of standard deviations from the mean.

What are some real-life examples of Normal Distribution?

Some real-life examples of Normal Distribution include heights and weights of a population, IQ scores, and test scores. It can also be seen in natural phenomena such as the size of raindrops, the length of tree branches, and the distribution of star brightness in the night sky.

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