Location of Home Downtown Area Elsewhere Outside Total In the City In the City the City Car Yes 10 15 35 60 Ownership No 60 55 25 140 Total 70 70 60 200
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!
- A local company is interested in supporting environmentally friendly initiatives such as carpooling among employees. The company surveyed all of the 200 employees at the downtown offices. Employees responded as to whether or not they own a car and to the location of the home where they live. The results are shown in the table below.
- If the person owns a car, he or she is more likely to live elsewhere in the city than to live in the downtown area in the city.
- If the person does not own a car, he or she is more likely to live outside the city than to live in the city (downtown area or elsewhere).
- The person is more likely to own a car if he or she lives in the city (downtown area or elsewhere) than if he or she lives outside the city.
- The person is more likely to live in the downtown area in the city than elsewhere in the city.
- The person is more likely to own a car than not to own a car.
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