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Published byGianna Fick Modified about 1 year ago

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Recall your experience when you take an elevator. Think about usually how long it takes for the elevator to arrive. Most likely, the experience may be it frequently comes in a short while and once in a while, it may come pretty late.

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In another word, if we want to use a random variable to measure the waiting time for elevator to come, we can say that: ◦ 1. It must be continuous. ◦ 2. Smaller values have larger probability and larger values have smaller probability. ◦ Think about Geometric distribution, is there any similarities?

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Usually, exponential distribution is used to describe the time or distance until some event happens. It is in the form of: ◦ where x ≥ 0 and μ>0. μ is the mean or expected value.

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In this case, Then the mean or expected value is

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We also use CDF to find probabilities under exponential distribution. Or

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On average, it takes about 5 minutes to get an elevator at Math building. Let X be the waiting time until the elevator arrives. (Let’s use the form with μ here) ◦ Find the pdf of X.

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2. What is the probability that you will wait less than 3 minutes?

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3. What is the probability that you will wait for more than 10 minutes?

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What is the probability that you will wait for more than 7 minutes?

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Given that you already wait for more than 3 minutes, what is the probability that you will wait for more than 10 minutes?

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That is a very interesting and useful property for exponential distribution. It is called “Memorylessness” or simply “Lack of memory”. In mathematical form: Therefore, P(wait more than 10 minutes| wait more than 3 minutes)=P(wait more than 7+3 minutes| wait more than 3 minutes)=P(wait more than 7 minutes)

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E(X)= μ or Var(X)= μ 2 or

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Poisson is a discrete random variable that measures the number of occurrence of some given event over a specific interval (time, distance) Exponential describes the length of the interval between occurrence.

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Example II: A storekeeper estimated that on average, there are 10 customers visiting his store between 10am and 12pm everyday. However, it has been more than 30 minutes since the last customer visited. What is the probability for that?

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If we know that there are on average 10 customers visiting a store within 2-hour interval, then the average time between customers’ arrival is: 120/10=12 minutes. Therefore, the time interval between customer visits follows an exponential distribution with mean=12 minutes.

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Given that the storekeeper has not got any customers for more than 30 minutes, what is the probability that the storekeeper will still have no customer for another 15 minutes or more?

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1. Given a random variable X, a percentile means a specific value of X, say x Usually, when we say p-th percentile, we mean there is a value x 0 such that p% of the values of X fall below x A special case is the median, which is 50-th percentile. That means 50% of the values of X fall below it.

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We have learned that given some data points, how to find the percentiles. In those cases, the number of data points is finite or limited. Now we turn to a different question, that is, to look for a percentile for a continuous random variable X, with NO data points given but there are infinitely many possible values. For example, if X~Uniform(0, 20), what is the median, 25%, 75% percentile?

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X~Uniform(0, 20), what is the median, 25%, 75% percentile? We can work out this kind of problem in the form of solving an equation. Let x 0 be the median, then P(X

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Also, how do we find the 25 th and 75 th percentile of X?

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What is the mean of X? Compare the mean and median of X, what can we find? Can we tell that from the shape of the pdf of X?

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Another example: Y~Exp(5), find the median, 25 th and 75 th percentile.

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Compare the mean and median of Y, are they the same? Why?

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