The probability function for the number of insurance policies John will sell to a customer is given by (2) f(x) = 0.5 – for X = 0, 1, or 2. (a) Is this a valid probability function? Explain your answer. Yes, f(x) > 0 and Ef(x) # 1 O Yes, f(x) > 0 and Ef(x) = 1 O No, f(x) > 0 and Ef(x) # 1 O No, f(x) > 0 and Ef(x) = 1 (b) What is the probability that John will sell exactly 2 policies to a customer? (Round your answer to three decimal places.) (c) What is the probability that John will sell at least 2 policies to a customer? (Round your answer to three decimal places.) (d) What is the expected number of policies John will sell? (Round your answer to three decimal places.) (e) What is the variance of the number of policies John will sell? (Round your answer to three decimal places.)
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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