Learn on PengienVision, Mathematics, Grade 7Chapter 7: Probability

Lesson 2: Understand Theoretical Probability

In this Grade 7 lesson from enVision Mathematics Chapter 7, students learn how to determine theoretical probability using the formula P(event) = number of favorable outcomes divided by total number of possible outcomes. Students apply this concept to real-world scenarios involving spinners, tile bags, and number cubes to calculate probabilities and use proportional reasoning to make predictions about likely outcomes.

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

Using percent proportions to make population predictions

Property

A percent proportion can be used to make predictions about a population based on sample data. When we know what percent of a sample has a certain characteristic, we can predict how many individuals in the entire population will have that characteristic.

predicted amount in populationtotal population=percent from sample100\frac{\text{predicted amount in population}}{\text{total population}} = \frac{\text{percent from sample}}{100}

Book overview

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Chapter 7: Probability

  1. Lesson 1

    Lesson 1: Understand Likelihood and Probability

  2. Lesson 2Current

    Lesson 2: Understand Theoretical Probability

  3. Lesson 3

    Lesson 3: Understand Experimental Probability

  4. Lesson 4

    Lesson 4: Use Probability Models

  5. Lesson 5

    Lesson 5: Determine Outcomes of Compound Events

  6. Lesson 6

    Lesson 6: Find Probabilities of Compound Events

  7. Lesson 7

    Lesson 7: Simulate Compound Events

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

Using percent proportions to make population predictions

Property

A percent proportion can be used to make predictions about a population based on sample data. When we know what percent of a sample has a certain characteristic, we can predict how many individuals in the entire population will have that characteristic.

predicted amount in populationtotal population=percent from sample100\frac{\text{predicted amount in population}}{\text{total population}} = \frac{\text{percent from sample}}{100}

Book overview

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

Continue this chapter

Chapter 7: Probability

  1. Lesson 1

    Lesson 1: Understand Likelihood and Probability

  2. Lesson 2Current

    Lesson 2: Understand Theoretical Probability

  3. Lesson 3

    Lesson 3: Understand Experimental Probability

  4. Lesson 4

    Lesson 4: Use Probability Models

  5. Lesson 5

    Lesson 5: Determine Outcomes of Compound Events

  6. Lesson 6

    Lesson 6: Find Probabilities of Compound Events

  7. Lesson 7

    Lesson 7: Simulate Compound Events