week 1 1

What is Probability? • Quantification of uncertainty.

• Mathematical model for things that occur randomly.

• Random – not haphazard, don’t know what will happen on any one experiment, but has a long run order.

• The concept of probability is necessary in work with physical biological or social mechanism that generate observation that can not be predicted with certainty. Example…

• The relative frequency of such ransom events with which they occur in a long series of trails is often remarkably stable. Events possessing this property are called random or stochastic events

week 1 2

Relative Frequency

• The relative frequency concept of probability does not provide a rigorous definition of probability.

• For our purpose, an interpretation based on relative frequency is a meaningful measure of out belief in the occurrence of the event.

• Relative frequency = proportion of times the even occurs.

• Goal: present an introduction to the theory of probability, which provides the foundation for modern statistical inference.

week 1 3

Basic Set Theory

• A set is a collection of elements. Use capital letters, A, B, C to denotes sets and small letters a1, a2, … to denote the elements of the set.

• Notation: means the element a1 is an element of the set A

A = {a1, a2, a3 }.

• The null, or empty set, denoted by Ф, is the set consisting of no points. Thus, Ф is a sub set of every set.

• The set S consisting of all elements under consideration is called the universal set.

Aa 1

week 1 4

Relationship Between Sets

• Any two sets A and B are equal if A and B has exactly the

same elements. Notation: A=B.

• Example: A = {2, 4, 6}, B = {n; n is even and 2 ≤ n ≤ 6}

• A is a subset of B or A is contained in B, if every point in A is

also in B. Notation:

• Example: A = {2, 4, 6}, B = {n; 2 ≤ n ≤ 6} = {2, 3, 4, 5, 6}

BA

week 1 5

Venn Diagram

• Sets and relationship between sets can be described by using

Venn diagram.

• Example: We toss a fair die. What is the universal set S? …

week 1 6

Union and Intersection of sets

• The union of two sets A and B, denoted by , is the set of all points that are in at least one of the sets, i.e., in A or B or both.

• Example 1: We toss a fair die…

• The intersection of two sets A and B, denoted by or AB, is the set of all points that are members of both A and B.

• Example 2: The intersection of A and B as defined in example 1 is …

BA

BA

week 1 7

Properties of unions and intersections

Unions and intersections are:

• Commutative, i.e., AB = BA and

• Associative, i.e.,

• Distributive, i.e.,

• These laws also apply to arbitrary collections of sets (not just pairs).

.ABBA

CBACBA

CABACBA

CABACBA

week 1 8

Disjoint Events

• Two sets A and B are disjoint or mutually exclusive if they

have no points in common. Then .

• Example 3: Toss a die. Let A = {1, 2, 3} and B = {4, 5}.

BA

week 1 9

Complement of a Set

• The complement of a set, denoted by Ac or A’ makes sense only with respect to some universal set. Ac is the set of all points of the universal set S that are not in A.

• Example: the complement of set A as defined in example 3 is…

• Note: the sets A and Ac are disjoint.

week 1 10

De Morgan’s Laws

• For any two sets A and B:

• For any collection of sets A1, A2, A3, … in any universal set S

CCC BABA

CCC BABA

11 n

Cn

C

nn AA

week 1 11

Finite, Countable Infinite and Uncountable

• A set A is finite if it contains a finite number of elements.

• A set A is countable infinite if it can be put into a one-to-one correspondence with the set of positive integers N.

• Example: the set of all integers is countable infinite because …

• The whole interval (0,1) is not countable infinite, it is uncountable.

week 1 12

The Probability Model

• An experiment is a process by which an observation is made.

For example: roll a die 6 times, toss 3 coins etc.

• The set of all possible outcomes of an experiment is called the sample space and is denoted by Ω.

• The individual elements of the sample space are denoted by ω and are often called the sample points.

• Examples ...

• An event is a subset of the sample space. Each sample point is a simple event.

• To define a probability model we also need an assessment of the likelihood of each of these events.

week 1 13

σ – Algebra

• A σ-algebra, F, is a collection of subsets of Ω satisfying the following properties:F contains Ф and Ω.F is closed under taking complement, i.e., F is closed under taking countable union, i.e.,

• Claim: these properties imply that F is closed under countable intersection.

• Proof: …

.FAFA C

FAAFAA ...,..., 2121

week 1 14

Probability Measure A probability measure P mapping F [0,1] must satisfy

• For , P(A) ≥ 0 .

• P(Ω) = 1.

• For , where Ai are disjoint,

This property is called countable additivity.

• These properties are also called axioms of probability.

FA

FAAA ,...,, 321

321321 ... APAPAPAAAP

week 1 15

Formal Definition of Probability Model

• A probability space consists of three elements (Ω, F, P)

(1) a set Ω – the sample space.

(2) a σ-algebra F - collection of subsets of Ω.

(3) a probability measure P mapping F [0,1].

week 1 16

Discrete Sample Space

• A discrete sample space is one that contains either a finite or a countable number of distinct sample points.

• For a discrete sample space it suffices to assign probabilities to each sample point.

• There are experiments for which the sample space is not countable and hence is not discrete. For example, the experiment consists of measuring the blood pressure of patients with heart disease.

week 1 17

Calculating Probabilities when Ω is Finite

• Suppose Ω has n distinct outcomes, Ω = {ω1, ω2,…, ωn}. The probability of an event A is

• In many situations, the outcomes of Ω are equally likely, then,

• Example, when rolling a die for i = 1, 2, …, 6.

• In these situations the probability that an event A occurs is

• Example:

A

i

i

PAP

.1

nP i

6

1iP

n

n

nAP a

occursA for which outcomes of #

week 1 18

Basic Combinatorics

• Multiplication Principle Suppose we are to make a series of decisions. Suppose there are c1 choices for decision 1 and for each of these there are c2 choices for decision 2 etc. Then the number of ways the series of decisions can be made is c1·c2·c3···.

• Example 1:

Suppose I need to choose an outfit for tomorrow and I have 2 pairs of jeans to choose from, 3 shirts and 2 pairs of shoes that matches with this shirts. Then I have 2·3·2 = 12 different outfits.

week 1 19

• Example 2:

The Cartesian product of sets A and B is the set of all pairs (a,b) where a A, b B. If A has 3 elements (a1,a2,a3) and B has 2 elements (b1,b2), then their Cartesian product has 6 members; that is

A B = {(a1,b1), (a1,b2), (a2,b1), (a2,b2), (a3,b1), (a3,b2)}.

• Example 3:

The number of subsets of a set of size n is 2n. E.g. if A = (a1,a2) then all the

possible subsets of A are: {a1}, {a2}, {a1,a2}, .

• Exercises: 2.38, 2.52 in textbook.

• Some more exercise:

1. We toss R different dies, what is the total number of possible outcome?

2. How many different digit numbers can be composed of the digits 1-7 ?

3. A questioneer consists of 5 questions: Gender (f / m), Religion

(Christian, Muslim, Jewish, Hindu, others), living arrangement

(residence, shared apartment, family), speak French (yes / no), marital

status. In how many possible ways this questioneer can be answered?

week 1 20

Permutation

• An order arrangement of n distinct objects is called a permutation.

• The number of ordered arrangements or permutation of n objects is

n! = n · (n – 1) · (n – 2) · · ·1 (“n factorial”).

• By convention 0! = 1.

• The number of ordered arrangements or permutation of k subjects selected from n distinct objects is n · (n – 1) · (n – 2) · · · (n – k +1). It is also the number of ordered subsets of size k from a set of size n. Notation:

• Example: n = 3 and k = 2

• The number of ordered arrangements of k subjects selected with replacement from n objects is n k.

)!(

!)1()2()1(

kn

nknnnnPn

k

week 1 21

Examples1. How many 3 letter words can be composed from the English Alphabet s.t:

(i) No limitation

(ii) The words has 3 different letters.

2. How many birthday parties can 10 people have during a year s.t.:

(i) No limitation

(ii) Each birthday is on a different day.

3. 10 people are getting into an elevator in a building that has 20 floors.

(i) In how many ways they can get off ?

(ii) In how many ways they can get off such that each person gets off on a

different floor ?

4. We need to arrange 4 math books, 3 physics books and one statistic book on a shelf.

(i) How many possible arrangements exists to do so?

(ii) What is the probability that all the math books will be together?

week 1 22

Combinations

• The number of subsets of size k from a set of size n when the order does not matter is denoted by or (“ n choose k”) .

The number of unordered subsets of size k selected (without replacement) from n available objects is

• Important facts:

• Exercise: Prove the above.

k

n nkC

)!(!

!

knk

n

k

n

10

n

nn

nn

nn

11

kn

n

k

n

week 1 23

Examples1. Exercise 2.54 from the textbook

2. Exercise 2.55 from the textbook

3. Exercise 2.56 from the textbook

4. We need to select 5 committee members form a class of 70 students.

(i) How many possible samples exists?

(ii) How many possible samples exists if the committee members all have

different rules?

week 1 24

The Binomial Theorem

• For any numbers a, b and any positive integer n

• The terms are referred to as binomial coefficient .

n

i

inin bai

nba

0

k

n

week 1 25

Multinomial Coefficients

• The number of ways to partitioning n distinct objects into k distinct groups containing n1, n2,…,nk objects respectively, where each object appears in exactly one group and is

• It is called the multinomial coefficients because they occur in the expansion

Where the sum is taken over all ni = 0,1,...,n such that

k

ii nn

1

!!!

!

... 2121 kk nnn

n

nnn

n

knk

nn

k

nk aaa

nnn

naaa 21

2121

21

k

ii nn

1

week 1 26

Examples

1. A small company gives bonuses to their employees at the end of the year. 15 employees are entitled to receive these bonuses of whom 7 employees will receive 100$ bonus, 3 will receive 1000$ bonus and the rest will receive 3000$ bonus.

In how many possible ways these bonuses can be distributed?

2. We need to arrange 5 math books, 4 physics books and 2 statistic book on a shelf.

(i) How many possible arrangements exists to do so?

(ii) How many possible arrangements exists so that books of the same subjects will lie side by side?