Scores for a common standardized college aptitude test are normally distributed with a mean of 514 and a standard deviation of 97. Randomly selected men are given a Test Prepartion Course before taking this test. Assume, for sake of argument, that the test has no effect. If 1 of the men is randomly selected, find the probability that his score is at least 572.2. P(X> 572.2) = Enter your answer as a number accurate to 4 decimal places. NOTE: Answers obtained using exact z- scores or z-scores rounded to 3 decimal places are accepted. If 16 of the men are randomly selected, find the probability that their mean score is at least 572.2. P(M> 572.2) = Enter your answer as a number accurate to 4 decimal places. NOTE: Answers obtained using exact z- scores or z-scores rounded to 3 decimal places are accepted. If the random sample of 16 men does result in a mean score of 572.2, is there strong evidence to

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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!