This exercise uses the population growth model. The bat population in a certain Midwestern county was 220,000 in 2012, and the observed doubling time for the population is 31 years. (a) Find an exponential model n(t) = no2/a for the population t years after 2012. n(t) = (b) Find an exponential model n(t) = noert for the population t years after 2012. (Round your r value to four decimal places.) n(t) = (c) Sketch a graph of the population at time t. n(t) n(t) 1400000 1400000 1200000 1200000 1000000 1000000 800000 800000 600000 600000 400000- 400000 200000 200000 10 20 30 40 50 60 10 20 30 40 50 n(t) n(t) 1400000 1400000 1200000 1200000 1000000 1000000 800000 800000 600000 600000 400000 400000 200000 200000 10 20 30 40 50 60 10 20 30 40 50 (d) Estimate how long it takes the population to reach 2 million. (Round your answer to two decimal places.) yr
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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