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Probability

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Probability is the measure of the likelihood of an event occurring. The sample space (S) is the set of all possible outcomes of an experiment, while an event (E) is any subset of that sample space. For example, when tossing a die, the sample space is S = {1, 2, 3, 4, 5, 6}, and the event of rolling an even number is A = {2, 4, 6}. Events can be mutually exclusive, meaning they cannot occur together, or non-mutually exclusive, meaning they can occur together. The probability of an event E is given by P(E) = n(E)/n(S), and for two events A and B, the addition law is P(A ∪ B) = P(A) + P(B) − P(A ∩ B) if they are not mutually exclusive. Independent events are those whose outcomes do not affect each other, such as tossing a coin and rolling a die, while dependent events, like drawing two cards without replacement, affect each other’s probabilities.

1. Introduction to Probability
Probability is the measure of the likelihood of an event occurring.

Sample space (S): All possible outcomes of an experiment.

Event (E): A subset of the sample space.

Example:
Tossing a die: S = {1, 2, 3, 4, 5, 6}
Event A (even numbers) = {2, 4, 6}

2. Types of Events

Mutually Exclusive Events: Two events that cannot occur together.
Example: Rolling even (A) or odd (B) numbers → A ∩ B = ∅

Non-Mutually Exclusive Events: Events that can occur together.
Example: Rolling even (A) or prime (C) numbers → A ∩ C = {2}

3. Probability Formulas

Probability of an event: P(E) = n(E) / n(S)

Addition Law:

For mutually exclusive events: P(A ∪ B) = P(A) + P(B)

For non-mutually exclusive events: P(A ∪ B) = P(A) + P(B) − P(A ∩ B)

Example:
Dice: P(even or prime) = 3/6 + 3/6 − 1/6 = 5/6

4. Independent and Dependent Events

Independent: Occurrence of one event does not affect the other.
Example: Tossing a coin and rolling a die → P(H ∩ 4) = 1/2 × 1/6 = 1/12

Dependent: Occurrence of one event affects the other.
Example: Drawing two cards without replacement → P(K ∩ A) = 4/52 × 4/51 = 4/663

5. Multiplication Principle

For independent events: P(A ∩ B) = P(A) × P(B)

Sample space when tossing a coin and rolling a die:
S = {(H,1),(H,2),…,(T,6)}, n(S) = 12

6. Probability with Cards

Standard deck: 52 cards, 4 suits, 13 cards each.

Examples:

King, Queen, or Jack: P(K ∪ Q ∪ J) = 1/13 + 1/13 + 1/13 = 3/13

Drawing two cards (with replacement): P(K ∩ A) = 1/13 × 1/13 = 1/169

Drawing two cards (without replacement): P(K ∩ A) = 4/52 × 4/51 = 4/663

7. Probability with Balls

Bag with 5 red, 6 blue balls:

With replacement:
P(R ∩ B) = 5/11 × 6/11 = 30/121

Without replacement:
P(R ∩ B) = 5/11 × 6/10 = 3/11

8. Tree Diagram

Visual representation of outcomes and probabilities.

Example 1: Tossing a coin twice → S = {HH, HT, TH, TT}
P(at least 2 heads) = 1/4

Example 2: Coin + die → P(H ∩ 4) = 1/12

9. Frequency Distribution & Measures of Central Tendency

Frequency Table: Organizes raw data into classes.

Mean: Average value

Median: Middle value

Mode: Most frequent value

Range: Max − Min

Quartiles: Divide data into 4 equal parts

Example: Heights of 50 people → Construct frequency table, find mean and median

10. Important Formulas Recap

P(E) = n(E)/n(S)

P(A ∪ B) = P(A) + P(B) − P(A ∩ B)

P(A ∩ B) = P(A) × P(B) (for independent events)

5 Most Important & Common Questions

1. Probability of getting an even or prime number on a die

Sample space: {1,2,3,4,5,6}

Even (A) = {2,4,6}, P(A) = 3/6 = 1/2

Prime (C) = {2,3,5}, P(C) = 3/6 = 1/2

Intersection: {2}, P(A ∩ C) = 1/6

P(A ∪ C) = 1/2 + 1/2 − 1/6 = 5/6

2. Probability of getting a king, queen, or jack from a deck

Total cards = 52

P(K) = P(Q) = P(J) = 4/52 = 1/13

P(K ∪ Q ∪ J) = 1/13 + 1/13 + 1/13 = 3/13

3. Probability of drawing two cards

With replacement: P(K ∩ A) = 1/13 × 1/13 = 1/169

Without replacement: P(K ∩ A) = 1/13 × 4/51 = 4/663

4. Probability using tree diagram (coin + die)

P(H ∩ 4) = 1/2 × 1/6 = 1/12

Tree diagram:
H → 1,2,3,4,5,6
T → 1,2,3,4,5,6

5. Probability of drawing two balls from a bag

Bag: 5 red, 6 blue

With replacement: P(R ∩ B) = 5/11 × 6/11 = 30/121
Without replacement: P(R ∩ B) = 5/11 × 6/10 = 3/11

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