13.11 Random numbers. If you ask a computer to generate “random numbers" between 0 and 5, you will get observations from a uniform distribution. Figure 13.12 shows the density curve for a uniform distribution. This curve takes the constant value 0.2 between 0 and 5 and is zero outside that range. Use this density curve to answer these questions. a. Why is the total area under the curve equal to 1? b. The curve is symmetric. What is the value of the mean and median? c. What percentage of the observations lie between 4 and 5? d. What percentage of the observations lie between 1.5 and 3? -height = 0.20
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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