The marital status distribution of the U.S male population, age 15 and older, is as shown below in first column. Suppose that a random sample of 400 U.Syound adult males, 18-38 years old, yielded the following frequency distribution . We are interested in whether this age group of males fits the distribution of the U.S adult population. marital status expected frequency observed frequency never marreid 31.3 140 marreid 56.1 238 widowed 2.5 2 divorced/seperated 10.1 20 total 100 400 Choose the correct calculation
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!
The marital status distribution of the U.S male population, age 15 and older, is as shown below in first column.
Suppose that a random sample of 400 U.Syound adult males, 18-38 years old, yielded the following frequency distribution . We are interested in whether this age group of males fits the distribution of the U.S adult population.
| marital status | expected frequency | observed frequency |
| never marreid | 31.3 | 140 |
| marreid | 56.1 | 238 |
| widowed | 2.5 | 2 |
| divorced/seperated | 10.1 | 20 |
| total | 100 | 400 |
Choose the correct calculation
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