The annual per capita share of U.S. total expenditures for heath care (in dollars per year) for selected years from 2002 and projected to 2024 can be modeled by H(t) = 5330e0.0442t where t is the number of years after 2000.† Assuming that the model remains valid, evaluate H(t) dt. (Round your answer to the nearest integer.) What does this value represent? O The total per-capita share of U.S. total expenditures, in dollars, for health care during 2011. O The total per-capita share of U.S. total expenditures, in dollars, for health care over the period 2011 to 2025. O The total of U.S. total expenditures, in dollars, for health care over the period 2011 to 2025. O The total per-capita share of U.S. total expenditures, in dollars, for health care during 2025. O The total of U.S. total expenditures, in dollars, for health care during 2025.
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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