Exponential Distribution

The Exponential Distribution is used to model the time between events in a process that occurs continuously and independently at a constant average rate. It is ideal for answering questions like:


Definition

The time \(X\) until the next event follows an exponential distribution if the probability of occurrence in a short interval is proportional to the length of the interval.

A random variable \(X\) has an exponential distribution with rate parameter \(\lambda > 0\), written: \[ X \sim \text{Exponential}(\lambda) \]


Probability Functions

Probability Density Function (PDF)

\[ f(x; \lambda) = \begin{cases} \lambda e^{-\lambda x}, & x \ge 0 \\ 0, & x < 0 \end{cases} \]

  • \(\lambda\) is the rate (e.g. arrivals per hour)
  • \(\mu = \frac{1}{\lambda}\) is the mean waiting time

Cumulative Distribution Function (CDF)

\[ F(x; \lambda) = \begin{cases} 1 - e^{-\lambda x}, & x \ge 0 \\ 0, & x < 0 \end{cases} \]


Key Properties

  • Mean (expected value):
    \[ E[X] = \frac{1}{\lambda} \]

  • Variance:
    \[ V[X] = \frac{1}{\lambda^2} \]

  • Memoryless Property:
    \[ P(X > s + t \mid X > s) = P(X > t) \]
    The exponential is the only continuous distribution with this property.


Lifetimes and Reliability

Exponential distributions are widely used to model lifespans of components, times between events, and reliability:

  • Time to failure of electronics
  • Duration between bus arrivals
  • Period between incoming phone calls

Let \(\mu = 5\). Then the probability that a device lasts more than 6 years is: \[ P(X \ge 6) = e^{-6 / 5} \approx 0.3012 \]


Example Calculations

Suppose \(\mu = 5\) years:

  • \(P(X \ge 6) = e^{-6/5} = 0.3012\)
  • \(P(X \le 4) = 1 - e^{-4/5} = 0.5507\)
  • \(P(5 \le X \le 6) = e^{-5/5} - e^{-6/5} = 0.3679 - 0.3012 = 0.0667\)

Review Questions

Q1. A call center receives 5 calls per hour.

  • What is the expected time between calls?
  • What is the probability that the next call comes within 6 minutes?

Q2. The time between bank customers is exponentially distributed with \(\lambda = 0.25\).

  • Find the mean wait time.
  • What is the probability the next arrival takes more than 5 minutes?
  • What is the probability it takes less than 2 minutes?

Q3. A gas station receives 3 customers/hour.

  • What is the expected wait time?
  • Compute \(E(T)\) and \(\text{Var}(T)\)
  • What’s the chance the next customer arrives in 15 minutes?
  • What’s the probability no customer arrives in the next 30 minutes?