Answer:
a) Poisson distribution
use a Poisson distribution model when events happen at a constant rate over time or space.
Explanation:
Poisson distribution
- Counts based on events in disjoint intervals of time or space produce a Poisson random variable.
- A Poisson random variable has one parameter, its mean λ
- The Poisson model uses a Poisson random variable to describe counts in data.
use a Poisson distribution model when events happen at a constant rate over time or space.
Hyper geometric probability distribution:-
The Hyper geometric probability distribution is a discrete probability distribution that describes the probability of successes (random draws for which the object drawn has a specified feature) in draws without replacement, from a finite population of size that contains exactly objects with that feature where in each draw is either a success or failure.
This is more than geometric function so it is called the Hyper geometric probability distribution
Binomial distribution
- The number of successes in 'n' Bernoulli trials produces a Binomial distribution . The parameters are size 'n' success 'p' and failure 'q'
- The binomial model uses a binomial random variable to describe counts of success observed for a real phenomenon.
Finally use a Binomial distribution when you recognize distinct Bernoulli trials.
Normal distribution:-
- normal distribution is a continuous distribution in which the variate can take all values within a range.
- Examples of continuous distribution are the heights of persons ,the speed of a vehicle., and so on
- Associate normal models with bell shaped distribution of data and the empirical rule.
- connect Normal distribution to sums of like sized effects with central limit theorem
- use histograms and normal quantile plots to judge whether the data match the assumptions of a normal model.
Conclusion:-
Given data use a Poisson distribution model when events happen at a constant rate over time or space.