In a recent year, the average daily circulation of the Wall Street Journal was 2,276,207. Suppose the standard deviation is 70,940. Assume the paper’s daily circulation is normally distributed. (a) On what percentage of days would circulation pass 1,793,000? (b) Suppose the paper cannot support the fixed expenses of a full-production setup if the circulation drops below 1,602,000. If the probability of this even occurring is low, the production manager might try to keep the full crew in place and not disrupt operations. How often will this even happen, based on this historical information? (Round the values of z to 2 decimal places. Round your answers to 4 decimal places.) (a) P(x > 1,793,000) = enter the probability that the daily circulation would pass 1,793,000 (b) P(x < 1,602,000) = enter the probability that the daily circulation will drop below 1,602,000
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!
In a recent year, the average daily circulation of the Wall Street Journal was 2,276,207. Suppose the standard deviation is 70,940. Assume the paper’s daily circulation is
(a) On what percentage of days would circulation pass 1,793,000?
(b) Suppose the paper cannot support the fixed expenses of a full-production setup if the circulation drops below 1,602,000. If the probability of this even occurring is low, the production manager might try to keep the full crew in place and not disrupt operations. How often will this even happen, based on this historical information?
(Round the values of z to 2 decimal places. Round your answers to 4 decimal places.)
| (a) P(x > 1,793,000) = enter the probability that the daily circulation would pass 1,793,000 (b) P(x < 1,602,000) = enter the probability that the daily circulation will drop below 1,602,000 |
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