Mutually Exclusive and Independent Events

This page delves deeper into two important concepts in probability theory: mutually exclusive events and independent events. It explains how these concepts affect probability calculations and provides examples using Venn diagrams.

Definition:

• Mutually Exclusive Events: Events that have no outcomes in common
• Independent Events: Events that do not affect each other's probabilities

The page introduces formulas for calculating probabilities of mutually exclusive and independent events:

Highlight:

• For mutually exclusive events A and B: P(A ∪ B) = P(A) + P(B)
• For independent events A and B: P(A ∩ B) = P(A) × P(B)

Several examples are provided to illustrate these concepts, including a Venn diagram representing students watching TV programs and a social club's charitable activities.

Example: A Venn diagram shows the probabilities of members of a social club participating in archery (A), raffle (R), and fun run (F) activities. Students are asked to find unknown probabilities and determine if events are independent.

The page also covers the addition rule for probability, which is useful when events are not mutually exclusive:

Highlight: Addition Rule: P(A ∪ B) = P(A) + P(B) - P(A ∩ B)

These examples and exercises help students understand how to apply probability formulas and interpret Venn diagrams in various scenarios.

Tree Diagrams and Successive Events

This page focuses on using tree diagrams to represent and calculate probabilities for events happening in succession. Tree diagrams are particularly useful for visualizing multi-step probability problems.

Definition: Tree Diagram: A visual representation of the possible outcomes of a sequence of events, where each branch represents a different outcome.

The page presents two main examples to illustrate the use of tree diagrams:

1. Charlie's commute to school:

Example: The probability of Charlie taking the bus is 0.4, and the probability of being late if he takes the bus is 0.2. If he walks (probability 0.6), the probability of being late is 0.3. Students are asked to draw a tree diagram and calculate the overall probability of Charlie being late to school.

2. Tossing a biased coin:

Example: A biased coin with P(heads) = 1/3 is tossed three times. Students are asked to draw a tree diagram, find the probability of getting heads all three times, and calculate the probability of getting heads only once.

The page also introduces more complex probability calculations involving repeated trials:

Highlight: The coin toss experiment is repeated for a second trial, and students are asked to find the probability of obtaining either 3 heads or 3 tails in both trials.

These examples demonstrate how tree diagrams can be used to break down complex probability problems into manageable steps, making it easier to calculate probabilities for sequences of events.

Vocabulary: Successive Events: Events that occur one after another in a sequence.

By working through these examples, students learn how to construct and interpret tree diagrams, as well as how to use them to solve multi-step probability problems.