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Chapter 5
Discrete Probability Distributions
Random Variables
x is a random variable which is a numerical description of the outcome of an experiment.

Discrete: If the possible values change by steps or jumps.
Example: Suppose we flip a coin 5 times and count the number of tails. The number of tails
could be 0, 1, 2, 3, 4 or 5. Therefore, it can be any integer value between (and including) 0
and 5. However, it could not be any number between 0 and 5. We could not, for example, get
2.5 tails. Therefore, the number of tails must be discrete.

Continuous: If the possible values can take any value within some range.
Example: The height of trees is an example of continuous data. Is it possible for a tree to be
2.105m tall? Sure. How about 2.10567m? Yes. How about 2.105679821014m? Definitely!
Discrete Random Variables
Consider the sales of cars at a car dealership over the past 300 days.
Frequency Distribution:
Number of cars sold per day
0
1
2
3
4
5

Number of days
(frequency)
54
117
72
42
12
3
300
Define the random variable:
Let x = the number of cars sold during a day.

Note: We make the assumption that no more than 5 cars are sold per day.

Sample Space: S = {0, 1, 2, 3, 4, 5}

Notation:
P(X = 0) = f(0) =
P(X = 1) = f(1) =
P(X = 2) = f(2) =
P(X = 3) = f(3) =
P(X = 4) = f(4) =
P(X = 5) = f(5) =
probability of 0 cars sold
probability of 1 car sold
probability of 2 cars sold
probability of 3 cars sold
probability of 4 cars sold
probability of 5 cars sold
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
Note: f(x) = probability function

The probability function provides the probability for each value of the random variable

Probability distribution for the number of cars sold per day at a car dealership
f(x)
x
Number of days
(frequency)
0
54
1
117
2
72
3
42
4
12
5
3
300

1
Question: Does the above mentioned probability function fulfill the required conditions for a
discrete probability function?
There are two requirements:
(i)
for all
∑
(ii)
Yes, both requirements are fulfilled.
Probability
Graphical representation of the probability
distribution for the number of cars sold per day
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
0.39
0.24
0.18
0.14
0.04
0
1
2
3
4
0.01
5
Number of cars sold per day
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Questions:
a)
The probability that 2 cars are sold per day?
b)
The probability that, at most, 2 cars are sold per day?
c)
The probability that more than 2 cars are sold per day?
d)
The probability that at least 2 cars are sold per day?
e)
The probability that more than 1 but less than 4 cars are sold per day?
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Discrete Uniform probability function:
where n = the number of values the random variable may assume
Example: Dice
for x = 1, 2, 3, 4, 5, 6
x
1
f(x)
2
3
4
5
6
Does the above mentioned probability function fulfill the required conditions for a discrete probability
function?
There are two requirements:
(i)
for all
∑
(ii)
Yes, both requirements are fulfilled.
Another example of a random variable x with the following discrete probability distribution
for x = 1, 2, 3, 4
x
1
2
3
4
f(x)
Does the above mentioned probability function fulfill the required conditions for a discrete probability
function?
There are two requirements:
(i)
for all
∑
(ii)
Yes, both requirements are fulfilled.
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Expected value, variance, standard deviation and median:
Probability
Graphical representation of the probability
distribution for the number of cars sold per day
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
0.39
0.24
0.18
0.14
0.04
0
1
2
3
4
0.01
5
Number of cars sold per day
Expected Value
∑
1.5
Variance
∑
1.25
Standard deviation
√
√
Median
0
0.18
0 and 1
0.18 + 0.39 = 0.57 > 0.5
Therefore, the median = 1
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OR use a table to calculate the expected value, variance and standard deviation:
x
0
1
2
3
4
5
f(x)
0.18
0.39
0.24
0.14
0.04
0.01
x f(x)
0
0.39
0.48
0.42
0.16
0.05
= 1.5
-1.5
-0.5
0.5
1.5
2.5
3.5
2.25
0.25
0.25
2.25
6.25
12.25
0.4050
0.0975
0.0600
0.3150
0.2500
0.1225
= 1.25
√
Example:
A psychologist has determined that the number of hours required to obtain the trust of a new patient is
either 1, 2 or 3 hours.
Let x = be a random variable indicating the time in hours required to gain the patient’s trust.
The following probability function has been proposed:
for x = 1, 2, 3
Questions:
a) Set up the probability function of x.
b) Is this a valid probability function? Explain.
c) Give a graphical representation of the probability function of x.
d) What is the probability that it takes exactly 2 hours to gain the patient’s trust?
e) What is the probability that it takes at least 2 hours to gain the patient’s trust?
f) Calculate the expected value, variance and standard deviation.
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Answers:
a)
x
1
f(x)
⁄
2
⁄
3
⁄
̇
̇
1
b) There are two requirements:
(i)
for all
∑
(ii)
Yes, both requirements are fulfilled.
c)
Graphical representation of the probability
distribution
0.6
Probability
0.5
0.4
0.3
0.2
0.1
0
1
2
3
x
̇
⁄
d)
⁄
e)
x
1
2
3
f(x)
0.1667
0.3333
0.5
̇
̇
∑
√
⁄
̇
∑
f)
⁄
√
̇
x f(x)
0.1667
0.6667
1.5000
2.3333
-1.3333
-0.3333
0.6667
1.7778
0.1111
0.4444
0.2963
0.0370
0.2222
0.5556
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Binomial distribution
1.
The experiment consists of a sequence of n identical trials.
2.
3.
Two outcomes are possible on each trial. We refer to a
 Success
 Failure
The probability of a success, denoted by p does not change from trial to trial. Consequently, the
probability of a failure, denoted by 1 – p, does not change from trial to trial.
4. The trials are independent
In general:
 Let: x = number of successes
 Then x has a binomial distribution of n trials and the probability of a success of p.
The Binomial probability function is:
( )
Martin clothing store problem:
Let us consider the purchase decisions of the next 3 customers who enter the Martin clothing store. On the
basis of past experience, the store manager estimates the probability that any one customer will make a
purchase is 0.3.
Let: S = customer makes a purchase (success)
F = customer does not make a purchase (failure)
The above mentioned is a Binomial experiment, because:
1. n = 3 identical trials
2. Two possible outcomes
• customer makes a purchase (success)
• customer does not make a purchase (failure)
3. Probability of a success p = 0.3 and a failure 1 – p = 0.7
4. The trials are independent
Let x = number of customers that make a purchase
OR
x = number of successes
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Tree Diagram:
1st
2nd
S
S
3rd
Value of x
S
(S, S, S)
3
F
(S, S, F)
2
S
(S, F, S)
2
(S, F, F)
1
(F, S, S)
2
F
(F, S, F)
1
S
(F, F, S)
1
F
(F, F, F)
0
F
F
S
F
Outcomes
S
F
Total number of experimental outcomes:
Using the tree diagram we count 8 experimental outcomes.
Using the counting rule for multiple-step experiments we get (n1)(n2)(n3) = (2)(2)(2) = 8.
Since the binomial distribution only as two possible outcomes on each step (success or failure), we can
use the formula
which in this case equals
where n denotes the number of trials in the binomial
experiment.
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Calculating binomial probabilities:
( )
Question 1:
Calculate the probability that 2 out of the 3 customers make a purchase.
Answer 1:
( )
Question 2:
Calculate the probability that 1 out of the 3 customers make a purchase.
Answer 2:
( )
Question 3:
Calculate the probability that 3 out of the 3 customers make a purchase.
Answer 3:
( )
Question 4:
Calculate the probability that 0 out of the 3 customers make a purchase.
Answer 4:
( )
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The probability distribution for the number of customers making a purchase:
x
0
1
2
3
f(x)
0.343
0.441
0.189
0.027
1
Does it fulfill the basic requirements for a discrete probability function?
There are two requirements:
(iii)
for all
∑
(iv)
Yes, both requirements are fulfilled.
Calculate the expected value, variance and standard deviation of x:
x
0
1
2
3
f(x)
0.343
0.441
0.189
0.027
x f(x)
0
0.441
0.378
0.081
0.9
-0.9
0.1
1.1
2.1
0.81
0.01
1.21
4.41
0.27783
0.00441
0.22869
0.11907
0.63
∑
∑
√
√
Formulas of
and
for the Binomial Probability Distribution:
Test:
√
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EXCEL:
BINOMDIST(x, n, p, false) – normal probability
BINOMDIST(x, n, p, true) – cumulative probability
Formula Worksheet
Value Worksheet
Value Worksheet with explanations
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Example: (Extension of the Martin-experiment)

Suppose 10 customers go into the store.

The probability of purchasing something is 0.3

Let x = number of customers that make a purchase
Questions:
1. What is the distribution of x.
Binomial with n = 10 and p = 0.3
2. Calculate the expected value, variance and standard deviation of x.
√
3. Calculate the probability distribution of x.
x
0
f(x)
f(0) = (
)
1
f(1) = (
)
2
f(2) = (
)
3
f(3) = (
)
4
f(4) = (
)
5
f(5) = (
)
6
f(6) = (
)
7
f(7) = (
)
8
f(8) = (
)
9
f(9) = (
)
10
f(10) = (
)
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4. A graphical representation of the probability distribution of x.
Probability distribution of 10 customers
0.30
0.267
0.233
0.25
0.200
f(x)
0.20
0.15
0.121
0.103
0.10
0.05
0.037
0.028
0.009 0.001 0.000 0.000
0.00
0
1
2
3
4
5
6
7
8
9
10
x
5. Calculate the cumulative distribution of x.
Formula worksheet
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Value worksheet
Value worksheet with explanations
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6. Calculate the probability that:
(a) At most 3 clients purchase something:
(b) Only 3 clients purchase something:
or
(
)
(c) More than 1 client purchase something:
since
(d) More than 2 but less than 5 clients purchase something:
OR
(e) Less than 5 clients purchase something:
(f) At least 4 clients purchase something:
(g) Exactly 6 clients do not purchase anything:
If 6 clients do not purchase something, then 4 clients purchase something
or
(h) Difficult question: Calculate the probability that the first three clients make a purchase:
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Shape of the Binomial distribution:
Binomial: n = 10 and p < 0.5
Skewed to the right
0.35
Probability
0.3
0.25
0.2
0.15
0.1
0.05
0
1
2
3
4
5
6
7
8
9
10
11
10
11
10
11
x
Binomial: n = 10 and p = 0.5
Symmetric
0.3
Probability
0.25
0.2
0.15
0.1
0.05
0
1
2
3
4
5
6
7
8
9
x
Binomial: n = 10 and p > 0.5
Skewed to the left
0.35
Probability
0.3
0.25
0.2
0.15
0.1
0.05
0
1
2
3
4
5
6
7
8
9
x
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