Let x be a random variable that represents white blood cell count per cubic milliliter of whole blood. Assume that x has a distribution that is approximately normal, with mean u = 6000 and estimated standard deviation o = 3000. A test result of x < 3500 is an indication of leukopenia. This Indicates bone marrow depresslon that may be the result of a viral infection. (a) What is the probability that, on a single test, x is less than 3500? (Round your answer to four decimal places.) (b) Suppose a doctor uses the average x for two tests taken about a week apart. What can we say about the probability distribution of x? O The probability distribution of x is approximately normal with u, = 6000 and o, = 3000. O The probability distribution of x is approximately normal with u, = 6000 and o, = 2121.32. O The probability distribution of x is not normal. O The probability distribution of x is approximately normal with u, = 6000 and o, = 1500.00. What is the probability of x < 3500? (Round your answer to four decimal places.) (c) Repeat part (b) for n = 3 tests taken a week apart. (Round your answer to four decimal places.) (d) Compare your answers to parts (a), (b), and (c). How did the probabilities change as n increased? O The probabilities decreased as n increased. O The probabilities increased as n increased. O The probabilities stayed the same as n increased. If a person had x < 3500 based on three tests, what conclusion would you draw as a doctor or a nurse? O It would be a common event for a person to have two or three tests below 3,500 purely by chance. The person probably does not have leukopenia. O It would be a common event for a person to have two or three tests below 3,500 purely by chance. The person probably has leukopenia.
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!
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