Let X be the total medical expenses (in 1000s of dollars) incurred by a particular individual during a given year. Although X is a discrete random variable, suppose its distribution is quite well approximated by a continuous distribution with pdf fx) = 1+ for x 2 0. (a) What is the value of k? 1.8 (b) Graph the pdf of X. f(x) f(x) fox) fox) 1.0 1.0 1.0 1.0 0.8 08 0.8 0.8 0.6 0.6 0.6 0.6 0.4 04 0.4 04 02 0.2 0.2 0.2 (c) What is the expected value of total medical expenses? (Round your answer to the nearest cent.) s(0.833 Ox What is the standard deviation of total medical expenses? (Round your answer to the nearest cent.) $208 (d) This individual is covered by an insurance plan that entails a $500 deductible provision (so the first $500 worth of expenses are paid by the individual). Then the plan will pay 80% of any additional expenses exceeding $500, and the maximum payment by the individual (incle deductible amount) is $2500. Let Y denote the amount of this individual's medical expenses paid by the insurance company. What is the expected value of Y? [Hint: First figure out what value of X corresponds to the maximum out-of-pocket expense of $2500. Then write an expression for Yas a function of X (which involves several different pieces) and calculate the expected value of this function.] (Round your answer to the nearest cent.) $ 10.500
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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