The following data are derived from the Monthly Vital Statistics Report (October 1999) issued by the National Center for Health Statistics [10]. These data are pertinent to livebirths only. Suppose that infants are classified as low birthweight if they have a bírthweight <2500 g and as normal bírthweight if they have a birthweight 22500 g. Suppose that infants are also classified by length of gestation in the following five categories: <28 weeks, 28-31 weeks, 32–35 weeks, 36 weeks, and 237 weeks. Assume the probabilities of the dif- ferent periods of gestation are as given in Table 3.8. Also assume that the probability of low birthweight is .949 gíven a gestatíon of <28 weeks, .702 given a gestatíon of 28-31 weeks, .434 given a gestation of 32-35 weeks, .201 given a gestation of 36 weeks, and .029 given a gesta- tion of 237 weeks.
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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