I needed many hours of study to master the concepts behind it. Even now, after many years of using it on an everyday basis, I should (sometimes) go and double check before I write down the results.
Today I want to write about probability distributions and what are some of the most used distributions with real-world examples.
Let's start.
Uniform distribution
Used to distribute probability equally over all possible outcomes (discrete) or equal ranges of outcomes (continuous). This distribution is especially useful in virtual experiments or simulations to explore real‐world phenomena.
Binomial distribution
Model the number of successes that can occur in a certain number of attempts when only two outcomes are possible (heads‐or‐tails coin‐flip, for example).
Poisson distribution
The Poisson (discrete) and exponential (continuous) distributions complement one another. Say that there is a crossroad in your area that has a lot of accidents. A Poisson distribution answers the question, “What is the probability that such‐and‐such number of accidents will occur there within a month?” And an exponential distribution answers the question, “What is the probability that the time until the next accident is a such‐and‐such length of time?”
Normal distribution or the bell curve.
These distributions model phenomena that tend toward some most likely value (the top of the bell in the bell curve) with values at the two extremes becoming less likely.
Source Wikipedia :
Gama distribution
You can use it to make predictions. For example, you can make use of it for predicting time-to-failure of a machinery or device in an industrial context.
That's it for today on statistics. I plan to post more about it soon so have a nice afternoon.
