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Create statinf-exp-distro
Introduction of births data set for analysis of exponential distribution.
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statinf-exp-distro

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# statinf-exp-distro
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While I was searching for real examples of exponential distribution, I came across this interesting piece.
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"To see an example of a distribution that is approximately exponential, we will look at the interarrival time of babies.
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On December 18, 1997, 44 babies were born in a hospital in Brisbane, Australia. The times of birth for all 44 babies were
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reported in the local paper; you can download the data from http://thinkstats.com/babyboom.dat. "
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Later, I came across the Centers for Disease Control and Prevention web site that has data (~200 MB !) on births in the US.
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The User Guide PDF document has the columns for date and time of birth. I want to use this data in a way similar to course
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project of 'Statistical Inference' course. The project description as below:
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In this project you will investigate the exponential distribution in R and compare it with the Central Limit Theorem.
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The exponential distribution can be simulated in R with rexp(n, λ ) where λ is the rate parameter.
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The mean of exponential distribution is 1/λ and the standard deviation is also 1/λ . Set λ = 0.2 for all
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of the simulations. You will investigate the distribution of averages of 40 exponentials.
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Note that you will need to do a thousand simulations.
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Illustrate via simulation and associated explanatory text the properties of the distribution of the mean of 40 exponentials.
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You should
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1. Show the sample mean and compare it to the theoretical mean of the distribution.
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2. Show how variable the sample is (via variance) and compare it to the theoretical variance of the distribution.
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3. Show that the distribution is approximately normal.
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Thus, with the births dataset, to prove the convergence, do the following steps sound valid?
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Establish that the data has an exponential distribution. (How do I find the λ value?)
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Take a random sample (size = 10) of the birth inter-arrival time. Calculate its mean.
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Repeat this many times (n=1000). Plot it.
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Repeat for size=50, size=100, size=250 and size=500.
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Then, the plots (for sizes 50, 100 and 500) should converge around normal distribution.
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