Suppose that the probability density function (p.d.f.) of the life (in weeks) of a certain part is: (view image) (a) Compute the probability the a certain part will fail in less than 200 weeks. (b) Compute the mean lifetime of a part and the standard deviation of the lifetime of a part. (c) Suppose that we select n = 50 parts at random. Approximate the probability that the average lifetime for these 50 parts will be less than 275 weeks?
Continuous Probability Distributions
Probability distributions are of two types, which are continuous probability distributions and discrete probability distributions. A continuous probability distribution contains an infinite number of values. For example, if time is infinite: you could count from 0 to a trillion seconds, billion seconds, so on indefinitely. A discrete probability distribution consists of only a countable set of possible values.
Normal Distribution
Suppose we had to design a bathroom weighing scale, how would we decide what should be the range of the weighing machine? Would we take the highest recorded human weight in history and use that as the upper limit for our weighing scale? This may not be a great idea as the sensitivity of the scale would get reduced if the range is too large. At the same time, if we keep the upper limit too low, it may not be usable for a large percentage of the population!
1. Suppose that the probability density function (
(a) Compute the probability the a certain part will fail in less than 200 weeks.
(b) Compute the mean lifetime of a part and the standard deviation of the lifetime of a part.
(c) Suppose that we select n = 50 parts at random. Approximate the probability that the average lifetime for these 50 parts will be less than 275 weeks?
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