The manufacturer of an extended-life lightbulb claims the bulb has an average life of 13,000 hours with a standard deviation of 1,000 hours. If the distribution is normal, what is the approximate percentage of these bulbs that will last d. between 11,000 and 15,000 hours? e. between 8,000 and 21,000 hours?
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!
The manufacturer of an extended-life lightbulb claims the bulb has an average life of 13,000 hours with a standard deviation of 1,000 hours. If the distribution is normal, what is the approximate percentage of these bulbs that will last
d. between 11,000 and 15,000 hours?
e. between 8,000 and 21,000 hours?
Given Information:
Mean Life (u) = 13000 hours
Standard Deviation (s) = 1000 hours
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