Explain a probability distribution that is not normal and how to apply that

Upasana | May 22, 2019 | 2 min read | 190 views

Explain a probability distribution that is not normal and how to apply that.

Answer : Though there are many kinds of distributions which are not normal distribution but we will be picking up only two here.

before that, lets first talk about probability distribution.

What is probability distribution?

Probability distribution is kind of equation which tells probability of occurence of each outcome of an experiment. Let’s say we do an experiment of tossing a fair coin. so, probability of getting head or tail for that occurance will be:

N(outcome)/Total N(outcome)

In this case, probability will be 0.5 which came from above function. That means, in this experiment probability distribution can be found by above equation. In other words, probability distribution tells us the likelihood of an event or outcome. It is of two types:

  • Discrete probability distribution (for discrete variables)

  • Continous probability distribution (for continous variables)

Binomial Distribution

Binomial distribution is a discrete probability distribution of an experiment of which there are only two outcomes, say success or failure. In case of tossing a fair coin, it will also hold binomial distribution as outcome can be only two : head or tail

Its probability density function is:

\$nCr * P^r * (1-P)^(n-r)\$

Exponential Distribution

Exponential distribution is a continous probability distribution that describes the time between events in a random mathematical experiment that consists of outcomes randomly located on a mathematical space, i.e., an experiment in which events occur continuously and independently at a constant average rate like amount of time until specific event occurs.

For example, amount of time from now until it rains would represent exponential distribution as

The probability density function (pdf) of an exponential distribution is

\$f(x;lambda) = {(lambdae^(-lambdax) , x>=0), (0, x <0.):}\$

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