1 (min) 10 15 L(t) (people per min) 150 110 40 20 2. On a certain day, 2000 students were present at Southside High School. At the end of this school day, when the bell rang at 3:00 PM, students left Southside High School the school at a rate of L(t), where t is the number of minutes since the bell rang. Selected values of L(t) are shown in the table above. Using a left Riemann sum with subintervals as indicated in the table, approximate the number of students still inside Southside High School in the 15 minutes after the bell rang. (A) 400 (B) 500 (С) 1150 (D) 1700
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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