The quality control at a refrigerator factory has shown that approximately 3% of the refriger- ators that come off the assembly line are defective. If we make 1000 refrigerators, what is the approximate probability that more than 30 refrigerators are defective?

MATLAB: An Introduction with Applications
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**Question 5:** The quality control at a refrigerator factory has shown that approximately 3% of the refrigerators that come off the assembly line are defective. If we make 1000 refrigerators, what is the approximate probability that more than 30 refrigerators are defective?

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Explanation for Website:

In this problem, we are dealing with a probability scenario involving defective units in a manufacturing process. Given that 3% of refrigerators are typically defective, we want to calculate the likelihood that more than 30 out of 1000 refrigerators produced are defective.

This type of probability problem can be approached using the binomial distribution, which is suitable for scenarios where there are two possible outcomes, such as defective or non-defective. However, given the large sample size (1000), one might also use a normal approximation for a simpler computation. 

This question requires knowledge of probability distributions and is typical in quality control analysis.
Transcribed Image Text:**Question 5:** The quality control at a refrigerator factory has shown that approximately 3% of the refrigerators that come off the assembly line are defective. If we make 1000 refrigerators, what is the approximate probability that more than 30 refrigerators are defective? --- Explanation for Website: In this problem, we are dealing with a probability scenario involving defective units in a manufacturing process. Given that 3% of refrigerators are typically defective, we want to calculate the likelihood that more than 30 out of 1000 refrigerators produced are defective. This type of probability problem can be approached using the binomial distribution, which is suitable for scenarios where there are two possible outcomes, such as defective or non-defective. However, given the large sample size (1000), one might also use a normal approximation for a simpler computation. This question requires knowledge of probability distributions and is typical in quality control analysis.
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