In my last post, I used Mathematica to show the connection between events that follow a Poisson process (i.e. occur independently at a constant rate per unit time/space) and the waiting time between successive events, which is exponentially distributed. By the memoryless property, the waiting time between successive events also happens to be the time until the *first *event occurs – making the exponential distribution a reasonable model of the time until failure of batteries, electronic components, machines, and so forth. Using the exponential model, I showed that the probability that a component lasts longer than average (i.e. exceeds its expected lifetime of 15 months in the example) is only 36.79%, which tells us the distribution is skewed (i.e. the average and the median aren’t equal). Below is the equation to compute this probability and below that are plots of the theoretical probability density function and cumulative density functions.

In this post I’m going to simulate the previous result using R. I provided the R code to generate the results but I’m a pretty bad programmer so I’d encourage you to look elsewhere for programming methodology etc. but nonetheless my code does produce the intended result. Before starting I loaded the MASS library because it’s a quick way to make plots look less shitty if you’re not an R pro.

First, I simulated 1000 random draws from an exponential distribution with parameter 1/15 and assign the result to a variable called sample:

*n <- 1000*

*sample <- rexp(n, rate=1/15)*

Then I made a histogram of ‘sample’ using the truehist command (which you’ll need the MASS library to use:

Now I want to somehow count how many of the random waiting times are greater than the average, 15. The graph gives us an idea – it doesn’t look more than a third – but it’s not precise. To know for sure I create a list of only the values that exceeded 15 in the sample called upper_sample with the help of a logical vector that returns true if the corresponding value in ‘sample’ is greater than 15 and false otherwise:

*above_avg <- sample > 15*

*upper_sample <- sample[above_avg]*

truehist(upper_sample, main=”Sample Waiting Times > 15″)

The number of elements in the upper_sample vector divided by the number of trials gives me the probability that a component lasts more than 15 hours, which I named psuccess:

*psuccess <- length(upper_sample)/n*

*psuccess*

There were 377 observations in my sample, so the simulated probability that a component lasts more than 15 hours is 37.7%, about 1% off from the true value.