Using Bayes' theorem, calculate the probability that a person who has had a positive test for a disease actually has the illness. Typically the test gives a correct positive result 90.0% of the time when a person has the illness, and gives an incorrect positive result 1.0% of the time when the person does not. Assume that (0.3% of the population has the illness, and the person who is tested was selected at random without necessarily showing any symptoms. Give your answer as a percentage with one digit after the decimal point.
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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