The function, f(t) = 25.1/(1 + 2.7e -0.05t) models the population of Florida, f(t), in millions, t years after 1970. Solve, a. What was Florida’s population in 1970?b. According to this logistic growth model, what was Florida’s population, to the nearest tenth of a million, in 2010? Does this underestimate or overestimate the actual 2010 population of 18.8 million? c. What is the limiting size of the population of Florida?
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 function, f(t) = 25.1/(1 + 2.7e -0.05t) models the population of Florida, f(t), in millions, t years after 1970. Solve, a. What was Florida’s population in 1970?
b. According to this logistic growth model, what was Florida’s population, to the nearest tenth of a million, in 2010? Does this underestimate or overestimate the actual 2010 population of 18.8 million? c. What is the limiting size of the population of Florida?
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