(a) less than 20 days (i.e., 0 < x < 20) (R (b) more than 50 days (i.e., 50 < x < ∞) / (c) between 30 and 70 days (Round your a
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 Poisson distribution gives the probability for the number of occurrences for a "rare" event. Now, let x be a random variable that represents the waiting time between rare events. Using some mathematics, it can be shown that x has an exponential distribution. Let x > 0 be a random variable and let ? > 0 be a constant. Then y = 1/B e^-x/b is a curve representing the exponential distribution. Areas under this curve give us exponential (refer to screenshot)
(a) less than 20 days (i.e., 0 ≤ x < 20) (Round your answer to four decimal places.)
(b) more than 50 days
Trending now
This is a popular solution!
Step by step
Solved in 2 steps with 2 images