What is the relationship between the Exponential and Poisson distributions?

They are two sides of the same coin. If the number of events occurring in a time interval follows a Poisson distribution with a rate λ, then the time *between* those events follows an Exponential distribution with the same rate parameter λ. For example, if the number of customers arriving at a store per hour is Poisson, the time between each customer's arrival is Exponential.

What is the 'memoryless' property of the exponential distribution?

The memoryless property is a key feature of the exponential distribution. It means that the probability of an event occurring in the future is independent of how much time has already passed. Formally, P(X > s+t | X > s) = P(X > t). For example, if a light bulb's lifetime is exponentially distributed and it has already worked for 100 hours, the probability that it will last another 50 hours is the same as the probability that a brand new bulb will last for 50 hours.

What is a real-world example of the exponential distribution?

A common example is modeling the time a customer service representative spends on a phone call. If the average call length is 4 minutes, the rate parameter λ would be 1/4 = 0.25 customers per minute. You can then use the exponential distribution to calculate the probability that a call will last for more than 5 minutes, or between 2 and 3 minutes.

Exponential Distribution Calculator

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Rate (λ):

Interval Start (m):

Interval End (n):

How to Use This Calculator

Step 1: Enter Your Parameters

Rate (λ): The rate parameter representing the average number of events per unit of time (e.g., 0.5 means an event occurs on average every 2 time units, since mean = 1/λ)
Interval Start (m): The beginning of the time interval you want to analyze (must be ≥ 0)
Interval End (n): The end of the time interval you want to analyze (must be ≥ m)

Step 2: Click "Calculate"

The calculator will instantly show you:

Complete PDF and CDF formulas with your specific λ value
P(m ≤ X ≤ n): The probability that the waiting time falls within your specified interval, shown with full integral derivation
PDF values f(m) and f(n): The probability density at your interval endpoints
CDF values F(m) and F(n): The cumulative probabilities at your interval endpoints
Statistical measures: mean (1/λ), variance (1/λ²), and standard deviation
Visual charts: PDF showing the exponential decay curve with your probability area shaded, and CDF with reference lines at your interval points

Step 3: Export Your Results

Use the buttons below the results to:

Save as PNG: Download charts and summary as an image for presentations or reports
Save as PDF: Generate a complete report with all formulas, calculations, and visualizations
More Calculators: Explore other probability distribution calculators on our site

💡 Quick Tip: The exponential distribution has the unique "memoryless" property. If you're modeling waiting times or component lifetimes, this means past waiting time doesn't affect future probability - a key characteristic that makes this distribution perfect for modeling random arrival processes!

Exponential Distribution Online Calculator

Understanding the Exponential Distribution

The Exponential Distribution is a continuous probability distribution used to model the time until an event of interest occurs. It is often used to describe the "waiting time" between events in a Poisson process, where events happen independently and at a constant average rate.

Key Concepts and Conditions

Relationship to Poisson Process: If events occur according to a Poisson process at an average rate of $\lambda$ events per unit of time, then the time between consecutive events follows an exponential distribution with rate parameter $\lambda$.
Memoryless Property: This is a unique and crucial feature. It states that the probability of an event occurring in the future is independent of how much time has already elapsed. For example, if a component's lifespan is exponentially distributed and it has already lasted for 100 hours, its probability of lasting another 50 hours is the same as that of a new component lasting 50 hours.

Formulas for the Exponential Distribution

The distribution is defined by a single parameter, the rate $\lambda$.

Probability Density Function (PDF): Describes the relative likelihood for the random variable to take on a given value.

$$f(x; \lambda) = \lambda e^{-\lambda x} \quad \text{for } x \ge 0$$

Cumulative Distribution Function (CDF): Gives the probability that the event has occurred by time $x$, i.e., $P(X \le x)$.

$$F(x; \lambda) = 1 - e^{-\lambda x} \quad \text{for } x \ge 0$$

Mean / Expected Value: $E[X] = \frac{1}{\lambda}$
Variance: $Var(X) = \frac{1}{\lambda^2}$

Examples with Step-by-Step Answers

Example 1: Customer Arrivals

Problem: Customers arrive at a checkout counter according to a Poisson process at an average rate of 2 customers per minute. What is the probability that the time until the next customer arrives is between 0.5 and 1.5 minutes?

1. Identify the parameter:

The rate parameter, $\lambda = 2$ customers per minute.
We need to find $P(0.5 \le X \le 1.5)$.

2. Use the CDF formula: The probability $P(m \le X \le n)$ is calculated as $F(n) - F(m)$.

$F(1.5) = 1 - e^{-2 \times 1.5} = 1 - e^{-3} \approx 1 - 0.0498 = 0.9502$

$F(0.5) = 1 - e^{-2 \times 0.5} = 1 - e^{-1} \approx 1 - 0.3679 = 0.6321$

3. Calculate the final probability:

$P(0.5 \le X \le 1.5) = F(1.5) - F(0.5) \approx 0.9502 - 0.6321 = 0.3181$

Answer: There is approximately a 31.81% chance that the next customer will arrive between 30 seconds and 1.5 minutes from now.

Example 2: Component Lifetime

Problem: The lifetime of a specific electronic component is exponentially distributed with a mean lifetime of 500 hours. What is the probability that a new component will last for more than 600 hours?

1. Identify the parameter:

We are given the mean, $E[X] = 500$. We know $E[X] = 1/\lambda$.
Therefore, the rate parameter $\lambda = 1/500 = 0.002$ failures per hour.
We need to find $P(X > 600)$.

2. Use the CDF formula: The probability $P(X > x)$ is the complement of $P(X \le x)$, so $P(X > x) = 1 - F(x)$.

$P(X > 600) = 1 - F(600) = 1 - (1 - e^{-0.002 \times 600})$

$= 1 - (1 - e^{-1.2}) = e^{-1.2}$

3. Calculate the final probability:

$e^{-1.2} \approx 0.3012$

Answer: The probability that the component will last for more than 600 hours is approximately 30.12%.

How to Do This in Statistical Software

The calculations above can also be reproduced directly in the most common statistical and data-analysis environments. The examples below use Example 1's parameters (λ = 2, interval 0.5 to 1.5).

How to Do This in RStudio

R's built-in pexp() function gives the exponential CDF directly:

lambda <- 2

# P(0.5 <= X <= 1.5)
p <- pexp(1.5, rate = lambda) - pexp(0.5, rate = lambda)
p
# 0.3180924

# Mean and variance
mean_x <- 1 / lambda
var_x <- 1 / lambda^2

How to Do This in Python

Python's scipy.stats.expon module handles the exponential distribution (note: SciPy parameterizes it by scale = 1/λ, not λ directly):

from scipy.stats import expon

lam = 2
scale = 1 / lam  # SciPy uses scale = 1/lambda

p = expon.cdf(1.5, scale=scale) - expon.cdf(0.5, scale=scale)
print(p)
# 0.3180924

mean = expon.mean(scale=scale)
var = expon.var(scale=scale)

How to Do This in SPSS

SPSS doesn't offer a direct "exponential probability" dialog for a single value the way it does for normal or binomial. The common workaround uses the COMPUTE command with SPSS's built-in CDF function in the Syntax editor:

COMPUTE p = CDF.EXP(1.5, 2) - CDF.EXP(0.5, 2).
EXECUTE.

How to Do This in JASP

JASP's Distributions module lets you visualize and compute probabilities interactively: Distributions → Exponential. Enter your rate (λ) parameter, then use the "Compute probability" panel to enter your lower and upper bounds and read off P(m ≤ X ≤ n) directly, alongside a plot of the PDF and CDF.