Chapter 4: Discrete Probability Distributions

Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Chapter 4: Discrete Probability Distributions
Department of Mathematics
Izmir University of Economics
Week 5-7
2014-2015
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Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Introduction
In this chapter we will focus on
random variables and the classification of random variables,
probability and cumulative probability distributions for discrete random
variables,
expected value, variance, and standard deviation of a discrete random
variable, and
some special discrete distributions.
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Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Random Variables
Definition:
A random variable X is a function which assigns a unique numerical value
to each outcome of a random experiment. (X : S → R)
Example. Consider the experiment of flipping a coin three times and let X be
the number of heads. Find the values of the random variable X .
Solution.
The sample space is S = {HHH, THH, HTH, HHT , TTH, THT , HTT , TTT }.
X (HHH) = 3, X (THH) = X (HTH) = X (HHT ) = 2,
X (TTH) = X (THT ) = X (HTT ) = 1, X (TTT ) = 0.
X can take values 0, 1, 2, and 3.
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Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Random Variables
Example. Consider the experiment of flipping a coin repeatedly until the first
occurrence of a head and let Y be the number of flips. Find the values of the
random variable Y .
Solution.




. . . T} H, . . . .
The sample space is S = H, TH, TTH, TTTH, . . . , |TT {z


k


Y (H) = 1, Y (TH) = 2, Y (TTH) = 3, . . . , Y TT
. . . T} H  = k + 1, . . .
| {z
k
Y can take values 1, 2, 3 . . . .
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Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Random Variables
It is very important to distinguish between a random variable and the possible
values that it can take. We use capital letters such as X to denote the
random variable and the corresponding lower-case letter, x, to denote a
possible value.
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Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Random Variables
Definition:
A random variable is a discrete random variable if it can take on no more than
countable (finitely many or countably infinite) number of values.
Some examples of discrete random variables are:
1
The number of sales resulting from 10 customers.
2
The number of defective items in a sample of 20 items from a large
shipment.
3
The number of customers arriving at a checkout counter in an hour.
4
The number of errors detected in a corporation’s accounts.
5
The number of claims on a medical insurance policy in a particular year.
ieu.logo.png
Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Random Variables
Definition:
A random variable is a continuous random variable if it can take any value in
an interval.
Some examples of continuous random variables are:
1
The yearly income for a family.
2
The amount of oil imported into Turkey in a particular month.
3
The change in the price of a store of IBM common stock in a month.
4
The time that elapses between the installation of a new component and
its failure.
5
The percentage of impurity in a batch of chemicals.
Chapter 4: Discrete Probability Distributions
ieu.logo.png
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Probability Distributions for Discrete Random
Variables
Definition:
The probability distribution function, P (x), of a discrete random variable X
represents the probability that X takes the value x, as a function of x. That is,
P (x) = P {X = x} for all values of x.
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Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Example. Consider the experiment of flipping a coin three times and let X be
the number of heads. Find P (x) for all values of x and show the result in a
table, a graph, and a function representation.
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Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Properties of Probability Distribution for Discrete Random Variables
Let X be a discrete random variable with probability distribution P (x). Then
1
2
0 ≤ P (x) ≤ 1 for any value x, and
P
x P (x) = 1, where summation is over all possible values of x.
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Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Example. Check whether the following functions can serve as probability
distribution functions of appropriate random variables:
a) f (x) =
b) f (x) =
c) f (x) =
(x3)
8
, x = 0, 1, 2, 3,
x+2
,
12
2
x = 1, 2, 3,
x −1
,
25
x = 0, 1, 2, 3, 4.
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Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Cumulative Probability Distributions for Discrete
Random Variables
Definition:
The cumulative probability distribution function, F (x), of a discrete random
variable X represents the probability that X does not exceed the value x0 , as
a function of x0 . That is,
F (x0 ) = P {X ≤ x0 } ,
where the function is evaluated at all values of x0 .
Let X be a random variable with probability distribution P (x) and cumulative
probability distribution F (x0 ). Then
X
F (x0 ) =
P (x) ,
x≤x0
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where the summation is over all possible values of x less than or equal to x0 .
Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Properties of Cumulative Probability Distribution for Discrete Random
Variables
Let X be a discrete random variable with cumulative probability distribution
F (x0 ). Then
1
0 ≤ F (x0 ) ≤ 1 for every number x0 , and
2
If x0 and x1 are two numbers with x0 < x1 , then F (x0 ) ≤ F (x1 ).
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Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Example. In a geography assignment the grade obtained is the random
variable X . It has been found that students have these probabilities of getting
a specific grade: A : 0.18, B : 0.32, C : 0.25, D : 0.07, E : 0.03, and F : 0.15.
Based on this,
a) Calculate the cumulative probability distribution of X and show the result
in a table and a graph representation.
b) Calculate the probability of getting a higher grade than B.
c) Calculate the probability of getting a lower grade than C.
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Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Expected Value of a Discrete Random Variable
Definition:
The expected value, E (X ), of a discrete random variable X is defined by
X
E (X ) =
xP (x) ,
x
where the summation is over all possible values of x.
The expected value of a random variable is also called its men and is
denoted by µ.
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Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Example. A review textbooks in a segment of the business area found that
81% of all pages of texts were error free, 17% of all pages contained one
error, and the remaining 2% contained two errors.Find the mean number of
errors per page.
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Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Example. The police chief of a city knows that the probabilities for 0, 1, 2, 3,
4, or 5 car thefts on any given day are respectively 0.21, 0.37, 0.25, 0.13,
0.03, and 0.01. How many car thefts can he expect per day?
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Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Variance and Standard Deviation of a Discrete
Random Variable
Definition:
The variance, Var (X ), of a discrete random variable X is denoted by σ 2 and
is given by
X
σ 2 = Var (X ) = E (X − µ)2 =
(x − µ)2 P (x) ,
x
where the summation is over all possible values of x.
The variance of a discrete random variable X can also be expressed as
X 2
σ 2 = E X 2 − (E (X ))2 =
x P (x) − µ2 .
x
The standard deviation, σ, is the positive square root of the variance.
Chapter 4: Discrete Probability Distributions
ieu.logo.png
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Example. An automobile dealer calculates the proportion of new cars sold
that have been returned a various number of times for the correction of
defects during the warranty period. The results are shown in the following
table
Number of returns
Proportion
0
0.28
1
0.36
2
0.23
3
0.09
4
0.04
a) Graph the probability distribution.
b) Calculate the cumulative probability distribution.
c) Find the mean of the number of returns of an automobile for corrections
for defects during the warranty period.
d) Find the variance of the number of returns of an automobile for
corrections for defects during the warranty period.
Chapter 4: Discrete Probability Distributions
ieu.logo.png
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Expected Value of Functions of Discrete Random
Variables
Definition:
Let X be a discrete random variable with probability distribution P (x) and let
g (X ) be some function of X . Then the expected value, E (g (X )), of that
function is defined as follows:
X
E (g (X )) =
g (x) P (x) ,
x
where the summation is over all possible values of x.
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Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Expected Value of Functions of Discrete Random
Variables
Properties for Linear Functions of a Random Variable
Let X be a random variable with mean µX and variance σX2 and let a and b be
any constants. Define the random variable Y as a + bX . Then, the mean and
variance of Y are
µY = E (Y ) = E (a + bX ) = E (a) + E (bX ) = a + bE (X ) = a + bµX
and
σY2 = Var (Y ) = E Y 2 − µ2Y = E (a + bX )2 − (a + bµX )2 = · · · = b2 σX2
so that the standard deviation of Y is
σY = |b|σX .
Chapter 4: Discrete Probability Distributions
ieu.logo.png
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Example. A contractor is interested in the total cost of a project on which she
intends to bid. She estimates that the materials will cost $25000 and that her
labor will be $900 per day. If the project takes X days to complete, the total
labor cost will be $900X and the total cost of the project (in dollars) will be
C = 25000 + 900X .
Using her experience the contractor form probabilities of likely completion
times for the project as
Completion time (in days)
Probability
10
0.1
11
0.3
12
0.3
13
0.2
14
0.1
a) Find the mean and variance for the completion time X .
b) Find the mean, variance, and standard deviation for total cost C.
Chapter 4: Discrete Probability Distributions
ieu.logo.png
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Example. In a game of chance we win 5 TL if we roll at least one 4 or a sum
of 7 when a pair of dice is used and lose 3 TL otherwise.
a) Find the expected gain or loss.
b) If we pay 30 TL before playing the game 10 times what is the expected
total prize?
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Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Binomial Distribution
Consider a random experiment that can give rise to just two possible mutually
exclusive and collectively exhaustive outcomes, which for convenience we
label "success" and "failure". Let p denote the probability of success and
1 − p the probability of failure. Define the random variable X so that X takes
the value 1 if the outcome of the experiment is a success and 0 if it is a
failure, that is,
1 if the outcome of the experiment is a success,
X =
0 otherwise.
The probability distribution of X is then
P (0) = P {X = 0} = 1 − p
P (1) = P {X = 1} = p.
This distribution is known as the Bernoulli distribution.
Chapter 4: Discrete Probability Distributions
ieu.logo.png
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Binomial Distribution
Example. (Mean and variance of a Bernoulli random variable)
Let X be a Bernoulli random variable. Find µX and σX2 .
Solution.
µX
=
E (X )
=
1
X
xP (x)
=
0P (0) + 1P (1)
x=0
=
σX2
=
=
0 (1 − p) + 1 (p)
Var (X )
1
X
=
=
p
E X 2 − µ2X
x 2 P (x) − p2
=
02 P (0) + 12 P (1) − p2
x=0
=
0 (1 − p) + 1 (p) − p2
=
p − p2
=
p (1 − p)
Chapter 4: Discrete Probability Distributions
ieu.logo.png
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Binomial Distribution
An important generalization of the Bernoulli distribution concerns the case
where a random experiment with two possible outcomes is repeated several
times and the repetitions are independent.
Suppose that a random experiment can result in two possible mutually
exclusive and collectively exhaustive outcomes, "success" and "failure", and
that p is the probability of success in a single trial. If n independent trials are
carried out, the distribution of the number of the successes, X , is called the
binomial distribution.
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Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Binomial Distribution
The probability distribution of the binomial random variable X is given by
P (x)
=
=
P {X = x} = P {x successes occur in n independent trials}
!
n x
n!
px (1 − p)n−x , x = 0, 1, . . . , n.
p (1 − p)n−x =
x! (n − x)!
x
The mean and variance of a binomial random variable X are
µX = E (X ) = np
and
σX2 = Var (X ) = np (1 − p) .
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Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Binomial Distribution
In a binomial distribution application,
1
there are several trials, each of which has only to outcomes: yes/no,
on/off, success/failure,
2
the probability of the outcome is the same for each trial,
3
the probability of the outcome on one trial does not affect the probability
on other trials.
ieu.logo.png
Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Binomial Distribution
Example. Suppose that a real estate agent has 5 contacts and she believes
that for each contact the probability of making a sale is 0.40.
a) Find the probability that she makes at most 1 sale.
b) Find the probability that she makes between 2 and 4 sales.
c) Graph the probability distribution function.
ieu.logo.png
Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Binomial Distribution
Example. A study conducted at a certain college shows that 65% of the
school’s graduates obtain a job in their fields within a year after graduation.
Find the probabilities that within a year after graduation of 14 randomly
selected graduate of that college
a) at least 6 will find a job in their fields,
b) at most 3 will find a job in their fields,
c) between 5 and 8 will find a job in their fields.
ieu.logo.png
Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Binomial Distribution
Example. A student takes an eight-question multiple choice exam. Each
question has five choices for answers, only one of which is correct. The
student forgot to study for the exam and guesses each question. Let X be the
number of correct answers. Find P {X = 6}.
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Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Binomial Distribution
Example. If the probability of a set of a tennis match will go into tie-breaker is
0.18, what is the probability that two of three sets will go into tie-breaker?
Find the expected value and variance of the number of sets that go into
tie-breaker in a three sets tennis match.
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Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Binomial Distribution
Example. What is the expected number of correct answers in a multiple
choice exam consisting of 20 questions where each question has 4 choices
and all questions are answered only by guessing?
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Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Poisson Distribution
The random variable X is said to follow a Poisson distribution if it has the
probability distribution
P (x) = P {X = x} =
e−λ λx
,
x!
x = 0, 1, 2, . . . ,
where λ(> 0) is the expected number of occurrences per unit time and
e ≈ 2.7182.
The mean and variance of a Poisson random variable X are
µX = E (X ) = λ
and
σX2 = Var (X ) = λ.
ieu.logo.png
Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Poisson Distribution
We use Poisson distribution to determine the probability of a random variable
that is characterized as the number of occurrences or successes of a certain
event in a given continuous interval. Some examples of these random
variables are:
1
The number of failures in a large computer system during a given day.
2
The number of replacement orders for a part received by a firm in a
given month.
3
The number of delivery trucks to arrive at a central warehouse in an
hour.
4
The number of customers to arrive for flights during each 10-minute
interval from 3.00 pm to 6.00 pm on weekdays.
5
The number of pine trees per unit area in a mixed forest.
Chapter 4: Discrete Probability Distributions
ieu.logo.png
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Poisson Distribution
Assume that an interval is divided into a very large number of equal
subintervals so that the probability of the occurrence of an event in any
subinterval is very small. Then, we can use Poisson distribution if the
following are true:
1
The probability of the occurrence of an event is constant for all
subintervals.
2
There can be no more than one occurrence in each subinterval.
3
Occurrences are independent, that is, an occurrence in one subinterval
does not influence the probability of an occurrence in another
subinterval.
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Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Poisson Distribution
Example. Find
a) P {X = 7|λ = 3.5} and
b) P {X ≤ 2|λ = 5}.
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Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Poisson Distribution
Example. Customers arrive at a photocopying machine at an average rate of
2 every five minutes. Assuming that the arrivals are independent, find the
probability that more than two customers arrive in a 5-minute interval.
ieu.logo.png
Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Poisson Distribution
Example. An instructor receives an average 4.2 emails from students the
day before a final exam. What is the probability of receiving at least 3 emails
on such a day?
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Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Poisson Distribution
Note: The sum of k Poisson random variables with respective means
λ1 , λ2 , . . . , λk is a Poisson random variable with mean λ1 + λ2 + · · · + λk .
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Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Poisson Distribution
Example. A computer center manager reports that his computer system
experienced three component failures during the past 100 days.
a) What is the probability of no failures in a given day?
b) What is the probability of one or more component failures in a given day?
c) What is the probability of at least two failures in a 3-day period?
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Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Poisson Approximation to the Binomial Distribution
Let X be the number of successes resulting from n independent trials each
with probability of success p. Then the distribution of X is binomial with mean
np. If the number of trials (n) is large and the probability of success (p) is
small so that np is of moderate size (preferably np ≤ 7), this distribution can
be approximated by the Poisson distribution with λ = np. The probability
distribution of the approximating distribution is
P (x) =
e−np (np)x
,
x!
x = 0, 1, 2, . . .
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Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Poisson Approximation to the Binomial Distribution
Example. A corporation has 250 PCs. The probability that any of them will
require repair in a week is 0.01. Find the probability that fewer than 4 of them
will require repair in a particular week.
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Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Hypergeometric Distribution
Suppose that a random sample of n objects is chosen from a group of N
objects, s of which are successes. The distribution of the number of
successes, X , in the sample is called the hypergeometric distribution. The
probability distribution of the random variable X is
(N−s)!
s!
s N−s
P (x) = P {X = x} =
x
n−x
N
n
=
x!(s−x)! (n−x)!(N−s−n+x)!
N!
n!(N−n)!
,
where max (0, n − N + s) ≤ x ≤ min (n, s) and its mean is µX = E (X ) = n Ns .
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Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Hypergeometric Distribution
Example. Let 4 of the tape recorders in a lot which contains a total of 16 are
defective. Suppose that we randomly select 3 of tape recorders from this lot.
Let X be the number of defective tape recorders in the selected 3.
a) Find P {X = 1}.
b) Graph the probability distribution of X .
c) Find E (X ).
ieu.logo.png
Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Hypergeometric Distribution
Example. A committee of 8 members is to be formed from a group of 8 men
and 8 women. If the choice of the committee members is made randomly,
what is the probability that precisely half of these members will be women?
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Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Jointly Distributed Discrete Random Variables
Recall the bivariate probabilities where the joint probabilities are given inside
the table and marginal probabilities as the sum of rows or columns.
We now examine two or more random variables instead of events.
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Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Jointly Distributed Discrete Random Variables
Definition:
Let X and Y be a pair of jointly distributed random variables. Their joint
probability distribution expresses the probability that simultaneously X takes
the specific value x and Y takes the specific value y as a function of x and y .
That is,
P (x, y ) = P {X = x, Y = y } = P {X = x ∩ Y = y } .
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Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Jointly Distributed Discrete Random Variables
Definition:
Let X and Y be a pair of jointly distributed random variables. The marginal
probability distribution of X is obtained by summing the joint probabilities over
all possible values of Y , that is,
X
X
PX (x) = P {X = x} =
P (x, y ) =
P {X = x, Y = y } .
y
y
Similarly, the marginal probability distribution of Y is obtained by summing
the joint probabilities over all possible values of X , that is,
X
X
PY (y ) = P {Y = y } =
P (x, y ) =
P {X = x, Y = y } .
x
x
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Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Jointly Distributed Discrete Random Variables
Properties of Joint Probability Distributions of Discrete Random
Variables
Let X and Y be discrete random variables with joint probability distribution
P (x, y ). Then
1
2
0 ≤ P (x, y ) ≤ 1 for any pair x and y , and
P P
x
y P (x, y ) = 1, that is, the sum of P (x, y ) over all possible pairs of
values must be 1.
ieu.logo.png
Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Jointly Distributed Discrete Random Variables
Example. Let X denote the income level of the audience for a particular
television show (X = 1 for low income, X = 2 for medium income, and X = 3
for high income) and Y the watching frequency (Y = 1 for regularly, Y = 2 for
occasionally, and Y = 3 for never). Find the marginal probabilities of the
random variables X and Y using the table
ieu.logo.png
Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Jointly Distributed Discrete Random Variables
Definition:
Let X and Y be a pair of jointly distributed random variables. The conditional
probability distribution of Y , given that X takes the value x, expresses the
probability that Y takes the value y , as a function of y , when the value x is
fixed for X . That is,
PY |X (y |x) = P {Y = y |X = x} =
P (x, y )
P {X = x, Y = y }
=
.
P {X = x}
PX (x)
Similarly, the conditional probability distribution of X , given Y = y , is
PX |Y (x|y ) = P {X = x|Y = y } =
P {X = x, Y = y }
P (x, y )
=
.
P {Y = y }
PY (y )
ieu.logo.png
Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Jointly Distributed Discrete Random Variables
Definition:
The jointly distributed random variables X and Y are said to be independent
if and only if their joint probability distribution is the product of their marginal
probability distributions, that is, if and only if,
P {X = x, Y = y } = P {X = x} P {Y = y }
or
P (x, y ) = PX (x) PY (y ) .
ieu.logo.png
Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Jointly Distributed Discrete Random Variables
Example. Suppose that Charlotte King has two stocks, A and B. Let X and
Y be random variables of possible percent returns (0%, 5%, 10%, and 15%)
for each of these two stocks with the joint probability distribution
a) Find the marginal probabilities of X and Y .
b) Determine if X and Y are independent.
c) Find the mean, variance, and standard deviation of X .
Chapter 4: Discrete Probability Distributions
ieu.logo.png
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Jointly Distributed Discrete Random Variables
Definition:
Let X be a random variable with mean µX and Y be a random variable with
mean µY . The covariance of X and Y is
XX
Cov (X , Y ) = E [(X − µX ) (Y − µY )] =
(x − µX ) (y − µY ) P (x, y ) .
x
y
An alternative formula is
Cov (X , Y ) = E (XY ) − E (X ) E (Y ) =
XX
x
xyP (x, y ) − µX µY .
y
The correlation between X and Y is
ρ = Corr (X , Y ) =
Cov (X , Y )
.
σX σY
Chapter 4: Discrete Probability Distributions
ieu.logo.png
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Jointly Distributed Discrete Random Variables
Note: If two random variables are statistically independent, the
covariance (and hence the correlation) between them is 0. However, the
converse is not necessarily true.
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Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Jointly Distributed Discrete Random Variables
−1 ≤ ρ ≤ 1 always holds!
A correlation of 0 indicates that there is no linear relationship between
the two random variables.
A positive correlation indicates that the variables are positively
dependent. If ρ = 1, then there is a perfect positive linear dependency.
A negative correlation indicates that the variables are negatively
dependent. If ρ = −1, then there is a perfect negative linear
dependency.
ieu.logo.png
Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Jointly Distributed Discrete Random Variables
Example. Suppose that Charlotte King has two stocks, A and B. Let X and
Y be random variables of possible percent returns (0%, 5%, 10%, and 15%)
for each of these two stocks with the joint probability distribution
Find the covariance and correlation for the stocks A and B.
Chapter 4: Discrete Probability Distributions
ieu.logo.png
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Linear Functions of Random Variables
Let X and Y be a pair of random variables with means µX and µY and
variances σX2 and σY2 . Then the following properties hold:
1
E (X + Y ) = µX + µY
2
E (X − Y ) = µX − µY
3
Var (X + Y ) = σX2 + σY2 + 2Cov (X , Y )
(If Cov (X , Y ) = 0, then Var (X + Y ) = σX2 + σY2 )
4
Var (X − Y ) = σX2 + σY2 − 2Cov (X , Y )
(If Cov (X , Y ) = 0, then Var (X − Y ) = σX2 + σY2 )
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Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Linear Functions of Random Variables
Definition:
Let X and Y denote the price for stock A and stock B, respectively. The
portfolio market value, W , is the linear function
W = aX + bY ,
where a and b are the respective numbers of shares of stocks A and B.
The mean and variance of W are
E (W ) = µW = aµX + bµY
and
2
= a2 σX2 + b2 σY2 + 2abCov (X , Y ) .
Var (W ) = σW
Chapter 4: Discrete Probability Distributions
ieu.logo.png
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Linear Functions of Random Variables
Example. George Tiao has 5 shares of stock A and 10 shares of stock B,
whose price variations are modeled in
Find the mean and variance of the portfolio.
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Chapter 4: Discrete Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Linear Functions of Random Variables
Example. An investor has $1000 to invest and two investment opportunities,
each requiring a minimum of $500. The profit per $100 from the first
investment can be represented by a random variable X having the probability
distribution P {X = −5} = 0.4 and P {X = 20} = 0.6. The profit per $100
from the second is given by the random variable Y whose probability
distribution is P {Y = 0} = 0.6 and P {Y = 25} = 0.4. Random variables X
and Y are independent. The investor has three possible investment
strategies:
a) $1000 in the first investment
b) $1000 in the second investment
c) $500 in each investment
Find the mean and variance of the profit for each strategy.
Chapter 4: Discrete Probability Distributions
ieu.logo.png
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Properties of Discrete Random Variables
Some Special Discrete Distributions
Jointly Distributed Discrete Random Variables
Linear Functions of Random Variables
Example. Consider the joint probability distribution
a) Compute the marginal probability distributions of X and Y .
b) Compute the covariance and correlation for X and Y .
c) Compute the mean and variance for W = 2X − 3Y .
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Chapter 4: Discrete Probability Distributions

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