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You have sold 17 vacuum cleaners. The vacuum cleaners have a 1-year warranty, and you have 2 vacuum cleaners in reserve so that you can honor the warranty and send someone a new vacuum cleaner if theirs breaks down. Vacuum cleaners have a 7% chance of breaking down in their first year of use. What distribution could be used to model the number of vacuum cleaners that break down during the year? Give all relevant parameters. What is the probability that more than 2 vacuum cleaners will break down? Show your working (this could take the form of an Excel formula or an equation). Would it be reasonable to use a normal approximation in the calculation above?

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The distribution that could be used to model the number of vacuum cleaners that break down during the year is the binomial distribution. The relevant parameters for the binomial distribution are the number of trials (n), which is the total number of vacuum cleaners sold (17), and the probability of success (p), which is the chance of a vacuum cleaner breaking down (7% or 0.07).

To calculate the probability that more than 2 vacuum cleaners will break down, we need to sum the probabilities of 3 or more successes in the binomial distribution.

Using the formula for the probability mass function (PMF) of the binomial distribution:

P(X > 2) = 1 - P(X ≤ 2) = 1 - [P(X = 0) + P(X = 1) + P(X = 2)]

where X is the random variable representing the number of vacuum cleaners that break down.

Calculating each term separately:

P(X = 0) = (17 choose 0) * (0.07^0) * (0.93^17) ≈ 0.155

P(X = 1) = (17 choose 1) * (0.07^1) * (0.93^16) ≈ 0.342

P(X = 2) = (17 choose 2) * (0.07^2) * (0.93^15) ≈ 0.339

Summing these probabilities:

P(X > 2) = 1 - (0.155 + 0.342 + 0.339) ≈ 0.164

Therefore, the probability that more than 2 vacuum cleaners will break down during the year is approximately 0.164 or 16.4%.

As for using a normal approximation, the binomial distribution can be approximated by a normal distribution when both np and n(1-p) are greater than or equal to 5. In this case, np = 17 * 0.07 = 1.19 and n(1-p) = 17 * 0.93 = 15.81. Since both values are less than 5, it would not be reasonable to use a normal approximation for this calculation.
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