bbs10_ppt_FOR MBA AND BBA STUDENTS......

Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc.. Chap 5-1
Chapter 5
Some Important Discrete
Probability Distributions
Basic Business Statistics
10th
Edition

Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-2
Learning Objectives
In this chapter, you learn:
 The properties of a probability distribution
 To calculate the expected value and variance of a
probability distribution
 To calculate the covariance and its use in finance
 To calculate probabilities from binomial,
hypergeometric, and Poisson distributions
 How to use the binomial, hypergeometric, and
Poisson distributions to solve business problems

Introduction to Probability
Distributions
 Random Variable
 Represents a possible numerical value from
an uncertain event
Random
Variables
Discrete
Random Variable
Continuous
Random Variable
Ch. 5 Ch. 6

Discrete Random Variables
 Can only assume a countable number of values
Examples:

Roll a die twice
Let X be the number of times 4 comes up
(then X could be 0, 1, or 2 times)
 Toss a coin 5 times.
Let X be the number of heads
(then X = 0, 1, 2, 3, 4, or 5)

Experiment: Toss 2 Coins. Let X = # heads.
T
T
Discrete Probability Distribution
4 possible outcomes
T
T
H
H
H H
Probability Distribution
0 1 2 X
X Value Probability
0 1/4 = 0.25
1 2/4 = 0.50
2 1/4 = 0.25
0.50
0.25
Probability

Discrete Random Variable
Summary Measures
 Expected Value (or mean) of a discrete
distribution (Weighted Average)
 Example: Toss 2 coins,
X = # of heads,
compute expected value of X:
E(X) = (0 x 0.25) + (1 x 0.50) + (2 x 0.25)
= 1.0
X P(X)
0 0.25
1 0.50
2 0.25





N
1
i
i
i )
X
(
P
X
E(X)

 Variance of a discrete random variable
 Standard Deviation of a discrete random variable
where:
E(X) = Expected value of the discrete random variable X
Xi = the ith
outcome of X
P(Xi) = Probability of the ith
occurrence of X
Summary Measures




N
1
i
i
2
i
2
)
P(X
E(X)]
[X
σ
(continued)





N
1
i
i
2
i
2
)
P(X
E(X)]
[X
σ
σ

 Example: Toss 2 coins, X = # heads,
compute standard deviation (recall E(X) = 1)
Summary Measures
)
P(X
E(X)]
[X
σ i
2
i

 
0.707
0.50
(0.25)
1)
(2
(0.50)
1)
(1
(0.25)
1)
(0
σ 2
2
2








(continued)
Possible number of heads
= 0, 1, or 2

The Covariance
 The covariance measures the strength of the
linear relationship between two variables
 The covariance:
)
Y
X
(
P
)]
Y
(
E
Y
)][(
X
(
E
X
[
σ
N
1
i
i
i
i
i
XY 




where: X = discrete variable X
Xi = the ith
outcome of X
Y = discrete variable Y
Yi = the ith
outcome of Y
P(XiYi) = probability of occurrence of the
ith
outcome of X and the ith
outcome of Y

Computing the Mean for
Investment Returns
Return per $1,000 for two types of investments
P(XiYi) Economic condition Passive Fund X Aggressive Fund Y
0.2 Recession - $ 25 - $200
0.5 Stable Economy + 50 + 60
0.3 Expanding Economy + 100 + 350
Investment
E(X) = μX = (-25)(0.2) +(50)(0.5) + (100)(0.3) = 50
E(Y) = μY = (-200)(0.2) +(60)(0.5) + (350)(0.3) = 95

Computing the Standard Deviation
for Investment Returns
0.2 Recession - $ 25 - $200
Investment
43.30
(0.3)
50)
(100
(0.5)
50)
(50
(0.2)
50)
(-25
σ 2
2
2
X







193.71
(0.3)
95)
(350
(0.5)
95)
(60
(0.2)
95)
(-200
σ 2
2
2
Y








Computing the Covariance
for Investment Returns
0.2 Recession - $ 25 - $200
Investment
8250
95)(0.3)
50)(350
(100
95)(0.5)
50)(60
(50
95)(0.2)
200
-
50)(
(-25
σXY











Interpreting the Results for
Investment Returns
 The aggressive fund has a higher expected
return, but much more risk
μY = 95 > μX = 50
but
σY = 193.71 > σX = 43.30
 The Covariance of 8250 indicates that the two
investments are positively related and will vary
in the same direction

The Sum of
Two Random Variables
 Expected Value of the sum of two random variables:
 Variance of the sum of two random variables:
 Standard deviation of the sum of two random variables:
XY
2
Y
2
X
2
Y
X σ
2
σ
σ
σ
Y)
Var(X 



 
)
Y
(
E
)
X
(
E
Y)
E(X 


2
Y
X
Y
X σ
σ 
 

Portfolio Expected Return
and Portfolio Risk
 Portfolio expected return (weighted average
return):
 Portfolio risk (weighted variability)
Where w = portion of portfolio value in asset X
(1 - w) = portion of portfolio value in asset Y
)
Y
(
E
)
w
1
(
)
X
(
E
w
E(P) 


XY
2
Y
2
2
X
2
P w)σ
-
2w(1
σ
)
w
1
(
σ
w
σ 




Portfolio Example
Investment X: μX = 50 σX = 43.30
Investment Y: μY = 95 σY = 193.21
σXY = 8250
Suppose 40% of the portfolio is in Investment X and 60%
is in Investment Y:
The portfolio return and portfolio variability are between the values for
investments X and Y considered individually
77
(95)
(0.6)
(50)
0.4
E(P) 


133.30
)(8250)
2(0.4)(0.6
(193.71)
(0.6)
(43.30)
(0.4)
σ 2
2
2
2
P





Probability Distributions
Continuous
Probability
Distributions
Binomial
Hypergeometric
Poisson
Probability
Distributions
Discrete
Probability
Distributions
Normal
Uniform
Exponential
Ch. 5 Ch. 6

The Binomial Distribution
Binomial
Hypergeometric
Poisson
Probability
Distributions
Discrete
Probability
Distributions

Binomial Probability Distribution
 A fixed number of observations, n
 e.g., 15 tosses of a coin; ten light bulbs taken from a warehouse
 Two mutually exclusive and collectively exhaustive
categories
 e.g., head or tail in each toss of a coin; defective or not defective
light bulb
 Generally called “success” and “failure”
 Probability of success is p, probability of failure is 1 – p
 Constant probability for each observation
 e.g., Probability of getting a tail is the same each time we toss
the coin

Binomial Probability Distribution
(continued)
 Observations are independent
 The outcome of one observation does not affect the
outcome of the other
 Two sampling methods
 Infinite population without replacement
 Finite population with replacement

Possible Binomial Distribution
Settings
 A manufacturing plant labels items as either
defective or acceptable
 A firm bidding for contracts will either get a
contract or not
 A marketing research firm receives survey
responses of “yes I will buy” or “no I will not”
 New job applicants either accept the offer or
reject it

Rule of Combinations
 The number of combinations of selecting X
objects out of n objects is
X)!
(n
X!
n!
Cx
n


where:
n! =(n)(n - 1)(n - 2) . . . (2)(1)
X! = (X)(X - 1)(X - 2) . . . (2)(1)
0! = 1 (by definition)

P(X) = probability of X successes in n trials,
with probability of success p on each trial
X = number of ‘successes’ in sample,
(X = 0, 1, 2, ..., n)
n = sample size (number of trials
or observations)
p = probability of “success”
P(X)
n
X ! n X
p (1-p)
X n X
!
( )!



Example: Flip a coin four
times, let x = # heads:
n = 4
p = 0.5
1 - p = (1 - 0.5) = 0.5
X = 0, 1, 2, 3, 4
Binomial Distribution Formula

Example:
Calculating a Binomial Probability
What is the probability of one success in five
observations if the probability of success is .1?
X = 1, n = 5, and p = 0.1
0.32805
.9)
(5)(0.1)(0
0.1)
(1
(0.1)
1)!
(5
1!
5!
p)
(1
p
X)!
(n
X!
n!
1)
P(X
4
1
5
1
X
n
X












n = 5 p = 0.1
n = 5 p = 0.5
Mean
0
.2
.4
.6
0 1 2 3 4 5
X
P(X)
.2
.4
.6
0 1 2 3 4 5
X
P(X)
0
Binomial Distribution
 The shape of the binomial distribution depends on the
values of p and n
 Here, n = 5 and p = 0.1
 Here, n = 5 and p = 0.5

Binomial Distribution
Characteristics
 Mean
 Variance and Standard Deviation
np
E(x)
μ 

p)
-
np(1
σ2

p)
-
np(1
σ 
Where n = sample size
p = probability of success
(1 – p) = probability of failure

n = 5 p = 0.1
n = 5 p = 0.5
Mean
0
.2
.4
.6
0 1 2 3 4 5
X
P(X)
.2
.4
.6
0 1 2 3 4 5
X
P(X)
0
0.5
(5)(0.1)
np
μ 


0.6708
0.1)
(5)(0.1)(1
p)
np(1-
σ




2.5
(5)(0.5)
np
μ 


1.118
0.5)
(5)(0.5)(1
p)
np(1-
σ




Binomial Characteristics
Examples

Using Binomial Tables
n = 10
x … p=.20 p=.25 p=.30 p=.35 p=.40 p=.45 p=.50
0
1
2
3
4
5
6
7
8
9
10
…
…
…
…
…
…
…
…
…
…
…
0.1074
0.2684
0.3020
0.2013
0.0881
0.0264
0.0055
0.0008
0.0001
0.0000
0.0000
0.0563
0.1877
0.2816
0.2503
0.1460
0.0584
0.0162
0.0031
0.0004
0.0000
0.0000
0.0282
0.1211
0.2335
0.2668
0.2001
0.1029
0.0368
0.0090
0.0014
0.0001
0.0000
0.0135
0.0725
0.1757
0.2522
0.2377
0.1536
0.0689
0.0212
0.0043
0.0005
0.0000
0.0060
0.0403
0.1209
0.2150
0.2508
0.2007
0.1115
0.0425
0.0106
0.0016
0.0001
0.0025
0.0207
0.0763
0.1665
0.2384
0.2340
0.1596
0.0746
0.0229
0.0042
0.0003
0.0010
0.0098
0.0439
0.1172
0.2051
0.2461
0.2051
0.1172
0.0439
0.0098
0.0010
10
9
8
7
6
5
4
3
2
1
0
… p=.80 p=.75 p=.70 p=.65 p=.60 p=.55 p=.50 x
Examples:
n = 10, p = 0.35, x = 3: P(x = 3|n =10, p = 0.35) = 0.2522
n = 10, p = 0.75, x = 2: P(x = 2|n =10, p = 0.75) = 0.0004

The Poisson Distribution
Binomial
Hypergeometric
Poisson
Probability
Distributions
Discrete
Probability
Distributions

The Poisson Distribution
 Apply the Poisson Distribution when:
 You wish to count the number of times an event
occurs in a given area of opportunity
 The probability that an event occurs in one area of
opportunity is the same for all areas of opportunity
 The number of events that occur in one area of
opportunity is independent of the number of events
that occur in the other areas of opportunity
 The probability that two or more events occur in an
area of opportunity approaches zero as the area of
opportunity becomes smaller
 The average number of events per unit is  (lambda)

Poisson Distribution Formula
where:
X = number of events in an area of opportunity
 = expected number of events
e = base of the natural logarithm system (2.71828...)
!
X
e
)
X
(
P
x





Poisson Distribution
Characteristics
 Mean
 Variance and Standard Deviation
λ
μ 
λ
σ2

λ
σ 
where  = expected number of events

Using Poisson Tables
X

0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90
0
1
2
3
4
5
6
7
0.9048
0.0905
0.0045
0.0002
0.0000
0.0000
0.0000
0.0000
0.8187
0.1637
0.0164
0.0011
0.0001
0.0000
0.0000
0.0000
0.7408
0.2222
0.0333
0.0033
0.0003
0.0000
0.0000
0.0000
0.6703
0.2681
0.0536
0.0072
0.0007
0.0001
0.0000
0.0000
0.6065
0.3033
0.0758
0.0126
0.0016
0.0002
0.0000
0.0000
0.5488
0.3293
0.0988
0.0198
0.0030
0.0004
0.0000
0.0000
0.4966
0.3476
0.1217
0.0284
0.0050
0.0007
0.0001
0.0000
0.4493
0.3595
0.1438
0.0383
0.0077
0.0012
0.0002
0.0000
0.4066
0.3659
0.1647
0.0494
0.0111
0.0020
0.0003
0.0000
Example: Find P(X = 2) if  = 0.50
0.0758
2!
(0.50)
e
X!
e
2)
P(X
2
0.50
X
λ






λ

Graph of Poisson Probabilities
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0 1 2 3 4 5 6 7
x
P(x)
X
 =
0.50
0
1
2
3
4
5
6
7
0.6065
0.3033
0.0758
0.0126
0.0016
0.0002
0.0000
0.0000
P(X = 2) = 0.0758
Graphically:
 = 0.50

Poisson Distribution Shape
 The shape of the Poisson Distribution
depends on the parameter  :
0.00
0.05
0.10
0.15
0.20
0.25
1 2 3 4 5 6 7 8 9 10 11 12
x
P(x)
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0 1 2 3 4 5 6 7
x
P(x)
 = 0.50  = 3.00

The Hypergeometric Distribution
Binomial
Poisson
Probability
Distributions
Discrete
Probability
Distributions
Hypergeometric

The Hypergeometric Distribution
 “n” trials in a sample taken from a finite
population of size N
 Sample taken without replacement
 Outcomes of trials are dependent
 Concerned with finding the probability of “X”
successes in the sample where there are “A”
successes in the population

Hypergeometric Distribution
Formula



























 

n
N
X
n
A
N
X
A
C
]
C
][
C
[
P(X)
n
N
X
n
A
N
X
A
Where
N = population size
A = number of successes in the population
N – A = number of failures in the population
n = sample size
X = number of successes in the sample
n – X = number of failures in the sample

Properties of the
 The mean of the hypergeometric distribution is
 The standard deviation is
Where is called the “Finite Population Correction Factor”
from sampling without replacement from a
finite population
N
nA
E(x)
μ 

1
-
N
n
-
N
N
A)
-
nA(N
σ 2


1
-
N
n
-
N

Using the
■ Example: 3 different computers are checked from 10 in
the department. 4 of the 10 computers have illegal
software loaded. What is the probability that 2 of the 3
selected computers have illegal software loaded?
N = 10 n = 3
A = 4 X = 2
0.3
120
(6)(6)
3
10
1
6
2
4
n
N
X
n
A
N
X
A
2)
P(X 






















































The probability that 2 of the 3 selected computers have illegal
software loaded is 0.30, or 30%.

Chapter Summary
 Addressed the probability of a discrete random
variable
 Defined covariance and discussed its
application in finance
 Discussed the Binomial distribution
 Discussed the Poisson distribution
 Discussed the Hypergeometric distribution

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