In a very large population, the distribution of annual income is skewed, with a long right tail. We take a simple random sample of ? people from this population and record the ? incomes. We expect a histogram of the ?n incomes in the sample A. will resemble a Uniform distribution for all values of ? B. will resemble a Normal distribution provided ? is large. C. will resemble a Normal distribution for all values of ? D. will not resemble a Normal distribution whatever the value of ? E. will resemble a Uniform distribution provided ?is large.
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!
In a very large population, the distribution of annual income is skewed, with a long right tail. We take a simple random sample of ? people from this population and record the ? incomes. We expect a histogram of the ?n incomes in the sample
A. will resemble a Uniform distribution for all values of ?
B. will resemble a
C. will resemble a Normal distribution for all values of ?
D. will not resemble a Normal distribution whatever the value of ?
E. will resemble a Uniform distribution provided ?is large.
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