,Probability distributions
• Discrete
– Binomial distribution
– Poisson distribution
• Continuous
– Normal distribution
,• Discrete random variable
– Variable is the characteristic of interest that
assumes different values for different elements of
the sample/population.
– If the value of the variable depends on the outcome
of an experiment it is called a random variable.
– Discrete random variable takes on a countable
number of values.
, • Discrete distribution function - example
– Toss a coin twice.
– S = {HH; HT; TH; TT}
– Each outcome in S has a probability of ¼.
– Random variable X – number of heads
– Collection of probabilities – probability distribution
– associates a probability with each value of
random variable.
x 0 1 2
1 2 1
P(X = x) = P(x)
4 4 4
• Discrete
– Binomial distribution
– Poisson distribution
• Continuous
– Normal distribution
,• Discrete random variable
– Variable is the characteristic of interest that
assumes different values for different elements of
the sample/population.
– If the value of the variable depends on the outcome
of an experiment it is called a random variable.
– Discrete random variable takes on a countable
number of values.
, • Discrete distribution function - example
– Toss a coin twice.
– S = {HH; HT; TH; TT}
– Each outcome in S has a probability of ¼.
– Random variable X – number of heads
– Collection of probabilities – probability distribution
– associates a probability with each value of
random variable.
x 0 1 2
1 2 1
P(X = x) = P(x)
4 4 4
