A small manufacturing company has rated 80% of its employees as satisfactory (S) and 20% as unsatisfactory (). Personnel records show that 90% of the satisfactory workers had previous work experience (E) in the job they are now doing, while 20% of the unsatisfactory workers had no work experience ()in the job they are now doing. If a person who has had previous work experience is hired, what is the approximate empirical probability that this person will be an unsatisfactory employee?
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!
A small manufacturing company has rated 80% of its employees as satisfactory (S) and 20% as unsatisfactory (). Personnel records show that 90% of the satisfactory workers had previous work experience (E) in the job they are now doing, while 20% of the unsatisfactory workers had no work experience ()in the job they are now doing. If a person who has had previous work experience is hired, what is the approximate empirical probability that this person will be an unsatisfactory employee?
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