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Unformatted text preview: 1 IE 372 Simulation  Handout 4 SELECTING INPUT PROBABILITY DISTRIBUTIONS (Law & Kelton) For simulating a real life system, we need to estimate the probability distribution of an input random variable using past data, if any. We have two options: 1. Fit a theoretical distribution, such as exponential, normal, beta and so on. 2. Define an empirical distribution. In general, we first try to fit the data at hand to a theoretical distribution. If that fails then we define an empirical distribution. We prefer a theoretical distribution because, with an empirical distribution, the values our random variable can take (the random variates that will be generated during the simulation) are restricted by the range of the data at hand. Fitting a Theoretical Distribution There are three steps involved. 1. Choose the family of distributions, such as exponential, normal or beta. To do this: Plot the histogram of data and compare its shape with known pdf shapes. Compare point (or descriptive) statistic values, such as coefficient of variation or skewness, with known values of theoretical distributions. For example, 2 2 = for exponential or skewness = 0 for normal. 2. Estimate distribution parameters from data, e.g. mean for exponential, mean and variance for normal. 3. Test how well the data fits the distribution by using goodnessoffit tests. That is, could this data be a sample from the hypothesized distribution? We will discuss: Chisquare goodnessoffit test KolmogorovSmirnov goodnessoffit test 2 Some Theoretical Distributions Available in ARENA 3 ChiSquare GoodnessofFit Test Given an iid sample 1 , , n X X K , we want to test: H : The sample 1 , , n X X K comes from the hypothesized theoretical distribution function F , against the alternative H 1 : It does not. We start by preparing the observed and expected frequency distributions. j Interval, 1 [ , ) j j a a Observed frequency, j N Expected frequency, j np 1 1 [ , ) a a 1 N 1 np 2 1 2 [ , ) a a 2 N 2 np k 1 [ , ) k k a a k N k np Total = n j p needed to calculate the expected frequencies are found as follows. 1 ( ) j j a j a p f x dx = , 1, , j k = K , if X is continuous 1 ( ) j i j j i a x a p p x < = , 1, , j k = K , if X is discrete where f or p represents the fitted distribution. The test statistic is 2 2 1 ( ) k j j j j N np np = = . Reject H if 2 2 , 1 df  where 1 df k = if distribution parameters are known, 1 df k p =  if p distribution parameters are estimated from the same sample. 2 , 1 df  4 Notes: Chisquare goodnessoffit test can be used for both continuous and discrete distributions....
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This note was uploaded on 05/23/2010 for the course IE 372 taught by Professor T during the Spring '10 term at Middle East Technical University.
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