I don't understand the exponential distribution at all

In summary, the conversation discusses two interpretations of the exponential distribution. The first states that it models the lifetime of something that does not age, meaning the probability of functioning for another time unit does not depend on its current age. The second interpretation states that it arises naturally when modeling the time between independent events that happen at a constant average rate. The two interpretations are equivalent because the exponential distribution gives the probability for the waiting time between events, and the concept of "does not age" and "constant average rate" both refer to a constant probability of an event occurring. Additionally, in the context of X~exp(lambda), lambda represents the average number of events per unit of time, making it a "rate."
  • #1
samh
46
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This is driving me completely crazy!

QUESTION 1: There are two interpretations I find for the exponential distribution:

1) It models the lifetime of something that does not age in the sense that the probability of functioning yet another time unit does not depend on its current age. So, P(X > x+y | X > y) = P(X > x).

2) It arises naturally when modeling the time between independent events that happen at a constant average rate (whatever that means). For instance the rate of incoming phone calls.

How do these two imply each other?? How are they equivalent? I.e., if it models something that doesn't age, then how does it also model the time between events with "constant average rate"? And if it models the time between events with "constant average rate," then how does it model something that doesn't age?

QUESTION 2: For X~exp(lambda), how is the lambda a "rate"?
 
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  • #2
Question 2 first:
Lambda = average number of events per unit of time
Such as the average number of accidents per month

Question 1:
"Does not age" means that just because something has not happened in a while it is not any more likely to happen.
"Constant average rate" means Lambda is not changing.The exponential distribution gives the probability for the waiting time between Poisson events. (From Stewart's Probability for Risk Management)
 

What is the exponential distribution and how is it used in science?

The exponential distribution is a probability distribution that models the time between events occurring at a constant rate. It is commonly used in science to model the probability of events such as radioactive decay, chemical reactions, and particle collisions.

Why is it difficult to understand the exponential distribution?

The exponential distribution can be difficult to understand because it involves complex mathematical concepts such as probability density functions and cumulative distribution functions. It also requires a solid understanding of calculus and statistics.

How is the exponential distribution different from other probability distributions?

The main difference between the exponential distribution and other probability distributions is that it only has one parameter, the rate parameter, which determines the shape and scale of the distribution. Other distributions, such as the normal distribution, have multiple parameters that affect the shape and location of the distribution.

What are some real-world applications of the exponential distribution?

The exponential distribution has many real-world applications in fields such as engineering, physics, and biology. For example, it can be used to model the lifetimes of electronic components, the decay of radioactive materials, and the time between earthquakes.

How can I better understand the exponential distribution?

To better understand the exponential distribution, it is important to have a strong foundation in calculus and statistics. It can also be helpful to visualize the distribution using graphs and to practice solving problems that involve the distribution. Seeking out additional resources and working with a mentor or tutor can also aid in understanding the concept.

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