Learn on PengiBig Ideas Math, Advanced 2Chapter 15: Probability and Statistics

Section 15.4: Compound Events

Property A sample space is the list of all possible outcomes for a probability experiment. An event is a subset of the sample space. Theoretical probability is calculated as: $$P(\text{event}) = \frac{\text{number of favorable outcomes}}{\text{total number of possible outcomes}}$$ A uniform probability model assigns equal probability to all outcomes in the sample space.

Section 1

Defining Theoretical Probability and Sample Space

Property

A sample space is the list of all possible outcomes for a probability experiment. An event is a subset of the sample space.
Theoretical probability is calculated as:

P(event)=number of favorable outcomestotal number of possible outcomesP(\text{event}) = \frac{\text{number of favorable outcomes}}{\text{total number of possible outcomes}}

A uniform probability model assigns equal probability to all outcomes in the sample space.

Examples

  • When rolling a standard six-sided die, the sample space is {1,2,3,4,5,6}\{1, 2, 3, 4, 5, 6\}. The probability of the event 'rolling an even number' is P(even)=36=12P(\text{even}) = \frac{3}{6} = \frac{1}{2}.
  • When flipping a coin, the sample space is {heads,tails}\{\text{heads}, \text{tails}\}. This is a uniform model where P(heads)=P(tails)=12P(\text{heads}) = P(\text{tails}) = \frac{1}{2}.
  • In a class of 20 students, if a student is selected at random, the probability that any specific student like Sam is selected is P(Sam)=120P(\text{Sam}) = \frac{1}{20}.

Explanation

Understanding sample spaces and theoretical probability provides the foundation for analyzing more complex situations. By identifying all possible outcomes and counting favorable ones, we can calculate exact probabilities for events. This theoretical approach will be essential when working with compound events involving multiple steps or conditions.

Section 2

Creating Tree Diagrams for Compound Events

Property

A tree diagram systematically displays all possible outcomes of compound events by creating branches for each outcome of the first event, then extending branches for each outcome of subsequent events from every existing branch.

Examples

Section 3

Fundamental Counting Principle

Property

If an event A can occur in mm ways and event B can occur in nn ways, then events A and B can occur in mnm \cdot n ways.
The Fundamental Counting Principle can be generalized to more than two events occurring in succession.

Examples

  • If you have 5 shirts and 3 pairs of pants, you can create 5×3=155 \times 3 = 15 different outfits.
  • A restaurant offers 4 appetizers, 6 main courses, and 3 desserts. The total number of different three-course meals is 4×6×3=724 \times 6 \times 3 = 72.
  • To create a code, you choose a letter from the alphabet (26 choices) and then a single digit from 0-9 (10 choices). There are 26×10=26026 \times 10 = 260 possible codes.

Explanation

Instead of listing every single possibility, just multiply the number of choices for each step. It is a powerful shortcut to find the total number of combinations, like for outfits, meal choices, or passwords.

Book overview

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Chapter 15: Probability and Statistics

  1. Lesson 1

    Section 15.1: Outcomes and Events

  2. Lesson 2

    Section 15.2: Probability

  3. Lesson 3

    Section 15.3: Experimental and Theoretical Probability

  4. Lesson 4Current

    Section 15.4: Compound Events

  5. Lesson 5

    Section 15.5: Independent and Dependent Events

  6. Lesson 6

    Section 15.6: Samples and Populations

  7. Lesson 7

    Section 15.7: Comparing Populations

Lesson overview

Expand to review the lesson summary and core properties.

Expand

Section 1

Defining Theoretical Probability and Sample Space

Property

A sample space is the list of all possible outcomes for a probability experiment. An event is a subset of the sample space.
Theoretical probability is calculated as:

P(event)=number of favorable outcomestotal number of possible outcomesP(\text{event}) = \frac{\text{number of favorable outcomes}}{\text{total number of possible outcomes}}

A uniform probability model assigns equal probability to all outcomes in the sample space.

Examples

  • When rolling a standard six-sided die, the sample space is {1,2,3,4,5,6}\{1, 2, 3, 4, 5, 6\}. The probability of the event 'rolling an even number' is P(even)=36=12P(\text{even}) = \frac{3}{6} = \frac{1}{2}.
  • When flipping a coin, the sample space is {heads,tails}\{\text{heads}, \text{tails}\}. This is a uniform model where P(heads)=P(tails)=12P(\text{heads}) = P(\text{tails}) = \frac{1}{2}.
  • In a class of 20 students, if a student is selected at random, the probability that any specific student like Sam is selected is P(Sam)=120P(\text{Sam}) = \frac{1}{20}.

Explanation

Understanding sample spaces and theoretical probability provides the foundation for analyzing more complex situations. By identifying all possible outcomes and counting favorable ones, we can calculate exact probabilities for events. This theoretical approach will be essential when working with compound events involving multiple steps or conditions.

Section 2

Creating Tree Diagrams for Compound Events

Property

A tree diagram systematically displays all possible outcomes of compound events by creating branches for each outcome of the first event, then extending branches for each outcome of subsequent events from every existing branch.

Examples

Section 3

Fundamental Counting Principle

Property

If an event A can occur in mm ways and event B can occur in nn ways, then events A and B can occur in mnm \cdot n ways.
The Fundamental Counting Principle can be generalized to more than two events occurring in succession.

Examples

  • If you have 5 shirts and 3 pairs of pants, you can create 5×3=155 \times 3 = 15 different outfits.
  • A restaurant offers 4 appetizers, 6 main courses, and 3 desserts. The total number of different three-course meals is 4×6×3=724 \times 6 \times 3 = 72.
  • To create a code, you choose a letter from the alphabet (26 choices) and then a single digit from 0-9 (10 choices). There are 26×10=26026 \times 10 = 260 possible codes.

Explanation

Instead of listing every single possibility, just multiply the number of choices for each step. It is a powerful shortcut to find the total number of combinations, like for outfits, meal choices, or passwords.

Book overview

Jump across lessons in the current chapter without opening the full course modal.

Continue this chapter

Chapter 15: Probability and Statistics

  1. Lesson 1

    Section 15.1: Outcomes and Events

  2. Lesson 2

    Section 15.2: Probability

  3. Lesson 3

    Section 15.3: Experimental and Theoretical Probability

  4. Lesson 4Current

    Section 15.4: Compound Events

  5. Lesson 5

    Section 15.5: Independent and Dependent Events

  6. Lesson 6

    Section 15.6: Samples and Populations

  7. Lesson 7

    Section 15.7: Comparing Populations