# Probability

**Probability**

Probability is the measure of how likely an event is. And an event is one or more outcomes of an experiment. Probability formula is the ratio of number of favorable outcomes to the total number of possible outcomes.

**How to Solve Probability Problems **

A probability is determined from an experiment, which is any activity that has an observable outcome like tossing a coin and observing whether it lands heads up or tails up. The possible outcomes of an experiment are called sample space of the experiment.

Steps to find the probability:

**Step 1: **List the outcomes of the experiment.

**Step 2: **Count the number of possible outcomes of the experiment.

**Step 3: **Count the number of favorable outcomes.

**Step 4:** Use the probability formula.

**Sample Space**

The set of all possible outcomes of a random experiment is called the sample space for that experiment. It is usually denoted by S.

**Example: **

**A).** When a die is thrown, any one of the numbers 1, 2, 3, 4, 5, 6 can come up.

Therefore, sample space:

S = {1, 2, 3, 4, 5, 6}

**B).** When a coin is tossed either a head or tail will come up, then the sample space w.r.t. the tossing of the coin is:

S = {H, T}

**C).** When two coins are tossed, then the sample space is

**Event**

A subset of the sample space is called an event.

**Problem of Events**

Sample space S plays the same role as universal set for all problems related to the particular experiment.

**(i).** ϕ is also the subset of S and is an impossible Event.

**(ii).** S is also a subset of S which is called a sure event or a certain event.

**5. Types of Events**

**A. Simple Event or Elementary Event**

An event is called a Simple Event if it is a singleton subset of the sample space S.

**Example: **

**A)**. When a coin is tossed, then the sample space is

S = {H, T}

Then A = {H} occurrence of head and

B = {T} occurrence of tail are called Simple events.

**B). **When two coins are tossed, then the sample space is

S = {(H,H); (H,T); (T,H); (T,T)}

Then A = {(H,T)} is the occurrence of head on 1st and tail on 2nd is called a Simple event.

**B. Mixed Event or Compound Event or Composite Event**

A subset of the sample space S which contains more than one element is called a mixed event or when two or more events occur together, their joint occurrence is called a **Compound Event**.

**Example:**

When a dice is thrown, then the sample space is

S = {1, 2, 3, 4, 5, 6}

Then let A = {2, 4 6} is the event of occurrence of even and B = {1, 2, 4} is the event of occurrence of exponent of 2 are Mixed events.

Compound events are of two type:

**(i).** Independent Events, and

**(ii).** Dependent Events

**C. Equally likely events**

Outcomes are said to be equally likely when we have no reason to believe that one is more likely to occur than the other.

**Example: **

When an unbiased die is thrown all the six faces 1, 2, 3, 4, 5, 6 are equally likely to come up.

**D. Exhaustive Events**

A set of events is said to be exhaustive if one of them must necessarily happen every time the experiments is performed.

**Example: **

When a die is thrown events 1, 2, 3, 4, 5, 6 form an exhaustive set of events.

**Important: **We can say that the total number of elementary events of a random experiment is called the exhaustive number of cases.

**E. Mutually Exclusive Events**

Two or more events are said to be mutually exclusive if one of them occurs, others cannot occur. Thus if two or more events are said to be mutually exclusive, if not two of them can occur together.

Hence, A1,A2,A3,…,An are mutually exclusive if and only if Ai∩Aj=ϕ, for i≠j

**Example: **

**A).** When a coin is tossed the event of occurrence of a head and the event of occurrence of a tail are mutually exclusive events because we cannot have both head and tail at the same time.

**B).** When a die is thrown, the sample space is S = {1, 2, 3, 4, 5, 6}

Let A is an event of occurrence of number greater than 4 i.e., {5, 6}

B is an event of occurrence of an odd number {1, 3, 5}

C is an event of occurrence of an even number {2, 4, 6}

Here, events B and C are Mutually Exclusive but the event A and B or A and C are not Mutually Exclusive.

**F. Independent Events or Mutually Independent events**

Two or more event are said to be independent if occurrence or non-occurrence of any of them does not affect the probability of occurrence of or non-occurrence of their events.

Thus, two or more events are said to be independent if occurrence or non-occurrence of any of them does not influence the occurrence or non-occurrence of the other events.

**Example: **

Let bag contains 3 Red and 2 Black balls. Two balls are drawn one by one **with replacement**.

Let A is the event of occurrence of a red ball in first draw.

B is the event of occurrence of a black ball in second draw.

Then probability of occurrence of B has not been affected if A occurs before B. As the ball has been replaced in the bag and once again we have to select one ball out of 5(3R + 2B) given balls for event B.

**Some Basic Probability Theorems**

**Theorem 1**

Let E’ be the complement of E defined by E′=S−E

The following always holds: P(E)=1−P(E′)

**Proof:**

Since E′=S−E, we have E∪E′=S and E∪E’=ϕ. Hence

P(E)+P(E′)=P(E∪E′) (By Axiom 3)

=P(S)

=1 (By Axiom 2)

Solving the equation for P(E) completes the proof.

**Theorem 2**

P(ϕ)=0

**Proof:**

Since ϕ′=0, By theorem 1 we have

P(ϕ)=1−P(S)

=1−1 (By Axiom 2)

=0

**Theorem 3**

If A⊆B ,then P(A)≤P(B)

**Proof:**

Since B=A∪(B−A)where A and B−A are disjoint, Axiom3 implies.

=> P(B)=P(A)+P(B−A).

By Axiom1, P(B−A)≥0.

Hence P(A)≤P(B).

**Theorem 4**

For any event E, 0≤P(E)≤1

**Proof:**

Since Axiom1 gives us P(E)≥0 we need only show that P(E)≤1.

But E⊆S implies P(E)≤P(S) by Theorem 3.

Therefore P(E)≤1 by Axiom 2.

**Theorem 5**

For A and B, P(A∪B)=P(A)+P(B)−P(A∩B)

**Proof:**

Since A,B and (A∪B) can be partitioned as follows:

A=(A∩B′)∪(A∩B)

B=(B∩A′)∪(A∩B)

A∪B=(A∩B′)∪(B∩A′)∪(A∩B)

We have

P(A)=P(A∩B′)+P(A∩B)’ ‘

P(B)=P(B∩A′)+P(A∩B)

P(A∪B)=P(A∩B′)+P(B∩A′)+P(A∩B)

Therefore,

P(A∪B)=P(A∩B′)+P(B∩A′)+P(A∩B)

=[P(A∩B′)+P(A∩B)]+[P(B∩A′)+P(A∩B)]−P(A∩B)

=P(A)+P(B)−P(A∩B)

**Theorem 6 – (Conditional probability)**

P(A∪B∪C)=P(A)+P(B)+P(C)−P(AB)−P(AC)−P(BC)+P(ABC)

Definition (**Conditional probability**)

P(A|B)=P(A∩B)P(B) provided P(B)≠0

**Theorem 7 **

P(A∩B)=P(A)*P(B|A)

=P(B)*P(A|B)

**8.8 Theorem 8**

P(A∩B∩C)=P(A)*P(B|A)*P(C|A∩B)

**8.9 Theorem 9 **

If A1,A2,A3……An are mutually independent events, then

P(A1∩A2∩…∩An)=P(A1)*P(A2)*…*P(An)

**8.10 Theorem 10 – (Theorem of total probability)**

(**Theorem of total probability**) If B1,B2,B3,…,Bn are mutually exclusive events with

∪Bi=S then

P(A)=P(A∩B1)+P(A∩B2)+P(A∩B3)+…+P(A∩Bn)

**Theorem 11 – (Bayes’ Theorem)**

(**Bayes’ Theorem**)

P(A|B)=P(B|A)*P(A)P(B|A)*P(A)+P(B|A′)*P(A′)

We have few basic formals that are used to calculate the probability and they are stated as: