Giant pandas are a vulnerable species famous for their consumption of large amounts of bamboo. Suppose we track the female population of a group of pandas year-to-year, and classify the pandas into three categories: cubs, subadults and adults. Say that u1 (t) denotes the amount of cubs in year t, ug(t) denotes the amount of subadults in year t and uz(t) denotes the amount of adults in year t. Assume the following about the population of pandas: • Cubs remain cubs for only one year - they have a mortality rate of 15% and the rest become subadults; • Approximately 25% of subadults mature to adults each year, approximately 20% of subadults die year year, and the rest remain in this classification; • On average, adults give birth to 0.5 new cubs each year; • 95% of adults survive from one year to the next. We can model the change in class structure each year using the model, u(t + 1) = Aü(t) where u(t) u2 (t) | and A is u3 (t) Number Number Number Number Number Number Number Number Number
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!
Can someone please help with question 12a. Will give thumbs up
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