Suppose the age distribution in a city is as follows. Under 18 26% 18–25 16% 26–50 34% 51–65 10% Over 65 14% A researcher is conducting proportionate stratified random sampling with a sample size of 400. Approximately how many people should he sample from each stratum? Number of people Under 18 enter a number of people 18–25 enter a number of people 26–50 enter a number of people 51–65 enter a number of people Over 65 enter a number of people
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!
Suppose the age distribution in a city is as follows.
|
Under 18
|
26% |
|
18–25
|
16% |
|
26–50
|
34% |
|
51–65
|
10% |
|
Over 65
|
14% |
A researcher is conducting proportionate stratified random sampling with a sample size of 400. Approximately how many people should he sample from each stratum?
|
Number of people
|
|
|---|---|
|
Under 18
|
enter a number of people |
|
18–25
|
enter a number of people |
|
26–50
|
enter a number of people |
|
51–65
|
enter a number of people |
|
Over 65
|
enter a number of people |
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