Probability is the ratio of favorable outcomes to the total outcomes. The probability distribution is used in statistics to find out the possibility of happening an event or experiment. The result of probability distribution will be a probability only. The probability distribution is the base of probability because it contains all the outcomes of an event. We find probability based on these outcomes or favorable chances. It contains random spaces or random experiments whose probability we need to find.
Random variables are a set of unknown results. They are the total outcomes that can occur in a random experiment. Probability distribution uses these random variables to find out the probability of an event. Random variables can be continuous and discrete. Therefore, probability distribution may consist of a probability mass function of random variables or even a finite or specific or countable list of random variables to find the probability of an event.
Two events with the same random variables can have different probabilities. This is because of their independent occurrence in an event. Random variates are the recognition of a random variable. In this, the outcomes are chosen as per the probability distribution of the variable.
Binomial distribution | P (X) = nCx ax bn-x Where x = random variable of success of an event n = number of trials a = probability of success b = probability of failure |
Cumulative distribution function | F (x) = -∞xfx t dt |
Discrete probability distribution | P (x) = n!r!n-r! Pr (1-p)n-r P (x) = C (n,r) pr (1-p)n-r |
1. Cumulative or Normal probability distribution – It is a continuous probability distribution. In this distribution, a continuous range of possible outcomes is formed. For instance, in a set of natural numbers, the possible outcomes will be continuous and given by cumulative probability distribution. This distribution is used in almost all situations in real life. From measuring temperatures to finding the probability of tossing a coin, this method can be used ample times. The formula for the normal distribution is-
Where, μ = Mean Value
σ = Standard Distribution of probability
If mean (μ) = 0 and standard deviation (σ) = 1, then this distribution is known to be a normal distribution.
x = Normal random variable
2. Discrete probability distribution – This probability is used when the set of outcomes are non-continuous. For example, a list of used or unused materials during manufacturing, the numbers of men and women working in the industry, etc., are all discrete data. The formula for a discrete probability distribution is-
Where, n = Total number of events
r = Total number of successful events.
p = Success on a single trial probability.
nCr = [n!/r!(n−r)]!
1–p = Failure Probability
3. Negative binomial distribution – Negative binomial distribution is a condition in the statistics and probability theory of mathematics. If the number of successes is independent and identical in a discrete probability distribution, they are eliminated by Bernoulli trials before execution. This is known as a negative binomial distribution. For example, if the failure of getting an outcome is 1, and if it occurs in an event, then the probability of that failure outcome will be negative.