Chapter 5 - Discrete random variables. After mastering the material in this chapter, you will be able to: Explain the difference between a discrete random variable and a continuous random variable, find a discrete probability distribution and compute its mean and standard deviation, use the binomial distribution to compute probabilities,...
Discrete Random Variables
5.1
Two Types of Random Variables
5.2
Discrete Probability Distributions
5.3
The Binomial Distribution
5.4 The Poisson Distribution (Optional)
5.5 The Hypergeometric Distribution
(Optional)
5.6 Joint Distributions and the Covariance
(Optional)
52
LO5-1: Explain the
difference between a
discrete random
5.1 Two Types of Random Variables
variable and a
continuous random
variable.
Random variable: a variable that assumes
numerical values that are determined by the
outcome of an experiment
◦ Discrete
◦ Continuous
Discrete random variable: Possible values can be
counted or listed
◦ The number of defective units in a batch of 20
◦ A listener rating (on a scale of 1 to 5) in an AccuRating
music survey
Continuous random variable: May assume any
numerical value in one or more intervals
◦ The waiting time for a credit card authorization
◦ The interest rate charged on a business loan
53
LO5-2: Find a discrete
probability distribution
and compute its mean
5.2 Discrete Probability Distributions
and standard deviation.
The probability distribution of a discrete
random variable is a table, graph or formula
that gives the probability associated with
each possible value that the variable can
assume
Notation: Denote the values of the random
variable by x and the value’s associated
probability by p(x)
54
LO5-2
Discrete Probability Distribution Properties
1. For any value x of the random variable,
p(x) 0
2. The probabilities of all the events in the
sample space must sum to 1, that is…
px 1
all x
55
LO5-3: Use the binomial
distribution to compute
probabilities.
5.3 The Binomial Distribution
The binomial experiment
characteristics…
1. Experiment consists of n identical trials
2. Each trial results in either “success” or “failure”
3. Probability of success, p, is constant from trial
to trial
The probability of failure, q, is 1 – p
1. Trials are independent
If x is the total number of successes in n
trials of a binomial experiment, then x is a
binomial random variable
56
LO5-3
Binomial Distribution Continued
For a binomial random variable x, the probability of
x successes in n trials is given by the binomial
distribution:
n! x n x
p x = p q
x! n x !
n! is read as “n factorial” and n! = n × (n1) × (n2)
× ... × 1
0! =1
Not defined for negative numbers or fractions
57
LO5-4: Use the Poisson
distribution to compute
probabilities (Optional).
5.4 The Poisson Distribution
Consider the number of times an event
occurs over an interval of time or space,
and assume that
1. The probability of occurrence is the same for
any intervals of equal length
2. The occurrence in any interval is independent of
an occurrence in any nonoverlapping interval
If x = the number of occurrences in a
specified interval, then x is a Poisson
random variable
58
LO5-5: Use the
hypergeometric
distribution to compute
probabilities (Optional).
5.5 The Hypergometric Distribution
(Optional)
Population consists of N items
◦r of these are successes
◦(Nr) are failures
If we randomly select n items without
replacement, the probability that x of the n
items will be successes is given by the
hypergeometric probability formula
r N r
x n x
P ( x)
N
n
59
LO5-5
The Mean and Variance of a Hypergeometric
Random Variable
Mean
r
x n
N
Variance
2 r r N n
x n 1
N N N 1
510
LO5-6: Compute and
understand the
covariance between two
random variables
(Optional).
5.6 Joint Distributions and the
Covariance (Optional)
511
LO5-6
Four Properties of Expected Values and
Variances
1. If a is a constant and x is a random
variable, then μax=aμx
2. If x1,x2,…,xn are random variables, then
μ(x1,x2,…,xn)= μx1 + μx2 + … + μxn
3. If a is a constant and x is a random
variable, then σ2ax=a2σ2x
4. If x1,x2,…,xn are statistically
independent random variables, then
the covariance is zero
◦ Also, σ2(x1,x2,…,xn)= σ2x1+ σ2x2+…+ σ2xn
512
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