Download Business Statistics: A Decision

Introduction to Statistics
Chapter 5
Random Variables
Business Statistics: A Decision-Making Approach, 6e © 2005 Prentice-Hall, Inc.
Chap 4-1
Chapter Goals
After completing this chapter, you should be
able to:
 Understand the discrete random variables and
there probability distribution.
 Compute the expected value and standard
deviation for a discrete probability distribution
 Apply the binomial distribution to applied
problems
Business Statistics: A Decision-Making Approach, 6e © 2005 Prentice-Hall, Inc.
Chap 4-2
Introduction to Probability
Distributions

Random Variable
 Represents a possible numerical value from
a random event
Random
Variables
Discrete
Random Variable
Business Statistics: A Decision-Making Approach, 6e © 2005 Prentice-Hall, Inc.
Continuous
Random Variable
Chap 4-3
Discrete Probability Distributions

A discrete random variable is a variable that can
assume only a countable number of values
Many possible outcomes:

number of complaints per day

number of TV’s in a household

number of rings before the phone is answered
Only two possible outcomes:

gender: male or female

defective: yes or no

spreads peanut butter first vs. spreads jelly first
Business Statistics: A Decision-Making Approach, 6e © 2005 Prentice-Hall, Inc.
Chap 4-4
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)
Business Statistics: A Decision-Making Approach, 6e © 2005 Prentice-Hall, Inc.
Chap 4-5
Discrete Probability Distribution
Experiment: Toss 2 Coins.
4 possible outcomes
T
H
H
T
Probability Distribution
x Value
Probability
0
1/4 = .25
1
2/4 = .50
2
1/4 = .25
H
T
H
Probability
T
Let x = # heads.
Business Statistics: A Decision-Making Approach, 6e © 2005 Prentice-Hall, Inc.
.50
.25
0
1
2
x
Chap 4-6
Discrete Probability Distribution

A list of all possible [ xi , P(xi) ] pairs
xi = Value of Random Variable (Outcome)
P(xi) = Probability Associated with Value

xi’s are mutually exclusive
(no overlap)
xi’s are collectively exhaustive
(nothing left out)
0  P(xi)  1 for each xi

S P(xi) = 1


Business Statistics: A Decision-Making Approach, 6e © 2005 Prentice-Hall, Inc.
Chap 4-7
Discrete Random Variable
Summary Measures

Expected Value of a discrete distribution
(Weighted Average)
E(x) = Sxi P(xi)

Example: Toss 2 coins,
x = # of heads,
compute expected value of x:
x
P(x)
0
.25
1
.50
2
.25
E(x) = (0 x .25) + (1 x .50) + (2 x .25)
= 1.0
Business Statistics: A Decision-Making Approach, 6e © 2005 Prentice-Hall, Inc.
Chap 4-8
Discrete Random Variable
Summary Measures

(continued)
Standard Deviation of a discrete distribution
σx 
 {x  E(x)}
2
P(x)
where:
E(x) = Expected value of the random variable
x = Values of the random variable
P(x) = Probability of the random variable having
the value of x
Business Statistics: A Decision-Making Approach, 6e © 2005 Prentice-Hall, Inc.
Chap 4-9
Discrete Random Variable
Summary Measures

(continued)
Example: Toss 2 coins, x = # heads,
compute standard deviation (recall E(x) = 1)
σx 
 {x  E(x)}
2
P(x)
σ x  (0  1)2 (.25)  (1  1)2 (.50)  (2  1)2 (.25)  .50  .707
Possible number of heads
= 0, 1, or 2
Business Statistics: A Decision-Making Approach, 6e © 2005 Prentice-Hall, Inc.
Chap 4-10
Two Discrete Random Variables

Expected value of the sum of two discrete
random variables:
E(x + y) = E(x) + E(y)
= S x P(x) + S y P(y)
(The expected value of the sum of two random
variables is the sum of the two expected
values)
Business Statistics: A Decision-Making Approach, 6e © 2005 Prentice-Hall, Inc.
Chap 4-11
The Binomial Distribution

Characteristics of the Binomial Distribution:





A trial has only two possible outcomes – “success” or
“failure”
There is a fixed number, n, of identical trials
The trials of the experiment are independent of each
other
The probability of a success, p, remains constant from
trial to trial
If p represents the probability of a success, then
(1-p) = q is the probability of a failure
Business Statistics: A Decision-Making Approach, 6e © 2005 Prentice-Hall, Inc.
Chap 4-12
Binomial Distribution Settings

A manufacturing plant labels items as
either defective or acceptable

A firm bidding for a contract will either get
the 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
Business Statistics: A Decision-Making Approach, 6e © 2005 Prentice-Hall, Inc.
Chap 4-13
Counting Rule for Combinations

A combination is an outcome of an experiment
where x objects are selected from a group of n
objects
n!
C 
x! (n  x )!
n
x
where:
n! =n(n - 1)(n - 2) . . . (2)(1)
x! = x(x - 1)(x - 2) . . . (2)(1)
0! = 1
(by definition)
Business Statistics: A Decision-Making Approach, 6e © 2005 Prentice-Hall, Inc.
Chap 4-14
Binomial Distribution Formula
n!
x nx
P(x) 
p q
x ! (n  x )!
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)
p = probability of “success” per trial
q = probability of “failure” = (1 – p)
n = number of trials (sample size)
Business Statistics: A Decision-Making Approach, 6e © 2005 Prentice-Hall, Inc.
Example: Flip a coin four
times, let x = # heads:
n=4
p = 0.5
q = (1 - .5) = .5
x = 0, 1, 2, 3, 4
Chap 4-15
Binomial Distribution

The shape of the binomial distribution depends on the
values of p and n
Mean

Here, n = 5 and p = .1
P(X)
.6
.4
.2
0
X
0
P(X)

Here, n = 5 and p = .5
.6
.4
.2
0
1
2
3
4
5
n = 5 p = 0.5
X
0
Business Statistics: A Decision-Making Approach, 6e © 2005 Prentice-Hall, Inc.
n = 5 p = 0.1
1
2
3
4
5
Chap 4-16
Binomial Distribution
Characteristics


Mean
μ  E(x)  np
Variance and Standard Deviation
σ  npq
2
σ  npq
Where n = sample size
p = probability of success
q = (1 – p) = probability of failure
Business Statistics: A Decision-Making Approach, 6e © 2005 Prentice-Hall, Inc.
Chap 4-17
Binomial Characteristics
Examples
μ  np  (5)(.1)  0.5
Mean
σ  npq  (5)(.1)(1  .1)
 0.6708
P(X)
.6
.4
.2
0
X
0
μ  np  (5)(.5)  2.5
σ  npq  (5)(.5)(1  .5)
 1.118
P(X)
.6
.4
.2
0
1
2
3
4
5
n = 5 p = 0.5
X
0
Business Statistics: A Decision-Making Approach, 6e © 2005 Prentice-Hall, Inc.
n = 5 p = 0.1
1
2
3
4
5
Chap 4-18
Using Binomial Tables
n = 10
x
p=.15
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
0.1969
0.3474
0.2759
0.1298
0.0401
0.0085
0.0012
0.0001
0.0000
0.0000
0.0000
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=.85
p=.80
p=.75
p=.70
p=.65
p=.60
p=.55
p=.50
x
Examples:
n = 10, p = .35, x = 3:
P(x = 3|n =10, p = .35) = .2522
n = 10, p = .75, x = 2:
P(x = 2|n =10, p = .75) = .0004
Business Statistics: A Decision-Making Approach, 6e © 2005 Prentice-Hall, Inc.
Chap 4-19
Chapter Summary

Distinguished between discrete and continuous
probability distributions

Examined discrete probability distributions and
their summary measures

Apply the binomial distribution to applied
problems
Business Statistics: A Decision-Making Approach, 6e © 2005 Prentice-Hall, Inc.
Chap 4-20