Sports, weather forecasts, blood samples, guessing the sex of a baby in the womb, congenital disabilities, statistics, and many other areas of daily life are all heavily reliant on probabilities. The amount of things sold in a business on a certain day is an example of a discrete random variable. To calculate how likely it is for a store to sell a given amount of things per day, it can use historical sales data to generate a probability distribution. This is a continuous random variable while we are measuring tire pressure in a car's tire pressure gauge.
We have a lot of fascinating models in statistics that make use of random variables, such as
Distribution in the form of a graph, Binomial distribution that is negative, The distribution of Poisson,
discrete random variables are used in the examples above. In some circumstances, a normal distribution can be used to approximate a discrete distribution by taking a continuous random variable.
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Sports, weather forecasts, blood samples, guessing the sex of a baby in the womb, congenital disabilities, statistics, and many other areas of daily life are all heavily reliant on probabilities. The amount of things sold in a business on a certain day is an example of a discrete random variable. To calculate how likely it is for a store to sell a given amount of things per day, it can use historical sales data to generate a probability distribution. This is a continuous random variable while we are measuring tire pressure in a car's tire pressure gauge.
We have a lot of fascinating models in statistics that make use of random variables, such as
Distribution in the form of a graph, Binomial distribution that is negative, The distribution of Poisson,
discrete random variables are used in the examples above. In some circumstances, a normal distribution can be used to approximate a discrete distribution by taking a continuous random variable.
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