Normal Distribution PresentationPresentation Transcript
Discrete Distribution Presented by: Piyush Tyagi Rohit Deshmukh Sagar Malik Sanakarshan Joshi Sayantan Banerjee
The probability distribution for a random variable describes
how probabilities are distributed over the values of
the random variable.
Random Variable : A numeric outcome that results from an experiment
Types of Distribution:
Continuous Probability Distribution
Spread over an interval.
Does not attain a specific value.
Discrete Probability Distribution
Whose variables can take on only discrete value
Assign probability to each random variable.
A discrete distribution with probability function defined over k=1, 2, ...,
has distribution function
0≤P(x i ) ≤1
Discrete Distribution contd….
Probability Distribution Function:
Shows probability of each ‘x’ value.
Cumulative Distribution Function:
Shows cumulative sum of probabilities.
It can result in one of 2 outcomes: Success or Failure.
A Bernoulli random variable is the simplest random variable.
It models an experiment in which there are only two outcomes.
Mean and Variance : For a Bernoulli random variable with success probability π :
Variance= π (1- π )
James Bernoulli (Jacob I) born in Basel, Switzerland Dec. 27, 1654-Aug. 16, 1705.
Extension of Bernoulli’s experiment.
Arises when Bernoulli’s experiment is repeated n times.
Conditions for Binomial:
All trials should be independent.
All other conditions should remain same.
There are only two outcomes possible.
‘ π ’ should not be too large or too small.
Binomial Distribution contd….
Properties : π x (1-π) n-x
Mean: n π
Poisson Distribution Siméon Denis Poisson June 21, 1781-April 25, 1840
It describes the number of occurrences within a randomly chosen unit of time.
Event must occur randomly and independently over a continuum period of time or space.
λ = mean arrivals per unit of time or space
Poisson Distribution Example: Mercy Hospital Patients arrive at the emergency room of Mercy Hospital at the average rate of 6 per hour on weekend evenings. What is the probability of 4 arrivals in 30 minutes on a weekend evening? λ =6/per hour= 3/per half-hour. Ans: 0.168
Hyper geometric Distribution
Similar to binomial except sampling is without replacement.
Probability of each out come changes with each trial.
N – Number of items in population.
n – Number of items in a sample.
s – Number of successes in population.
Mean: n π where π=s/N
Standard Deviation :
Hypergeometric Distribution Example: Neveready Bob Neveready has removed two dead batteries from a flashlight and inadvertently mingled them with the two good batteries he intended as replacements. The four batteries look identical. Bob now randomly selects two of the four batteries. What is the probability he selects the two good batteries? n = 2 = number of batteries selected(sample size) N = 4 = number of batteries in total(population size) s = 2 = number of good batteries in total(success in population) x = 2 = number of good batteries selected. Ans: 0.167