### Learning Outcomes

- Construct probability models.
- Compute probabilities of equally likely outcomes.
- Compute probabilities of the union of two events.
- Use the complement rule to find probabilities.
- Compute probability using counting theory.

Residents of the Southeastern United States are all too familiar with charts, known as spaghetti models, such as the one above.^{[1]} They combine a collection of weather data to predict the most likely path of a hurricane. Each colored line represents one possible path. The group of squiggly lines can begin to resemble strands of spaghetti, hence the name. In this section, we will investigate methods for making these types of predictions.

## Construct Probability Models

Suppose we roll a six-sided number cube. Rolling a number cube is an example of an **experiment**, or an activity with an observable result. The numbers on the cube are possible results, or **outcomes**, of this experiment. The set of all possible outcomes of an experiment is called the **sample space** of the experiment. The sample space for this experiment is [latex]\left\{1,2,3,4,5,6\right\}[/latex]. An **event** is any subset of a sample space.

The likelihood of an event is known as **probability**. The probability of an event [latex]p[/latex] is a number that always satisfies [latex]0\le p\le 1[/latex], where 0 indicates an impossible event and 1 indicates a certain event. A **probability model** is a mathematical description of an experiment listing all possible outcomes and their associated probabilities. For instance, if there is a 1% chance of winning a raffle and a 99% chance of losing the raffle, a probability model would look much like the table below.

Outcome | Probability |
---|---|

Winning the raffle | 1% |

Losing the raffle | 99% |

The sum of the probabilities listed in a probability model must equal 1, or 100%.

### How To: Given a probability event where each event is equally likely, construct a probability model.

- Identify every outcome.
- Determine the total number of possible outcomes.
- Compare each outcome to the total number of possible outcomes.

### Example: Constructing a Probability Model

Construct a probability model for rolling a single, fair die, with the event being the number shown on the die.

### Q & A

#### Do probabilities always have to be expressed as fractions?

*No. Probabilities can be expressed as fractions, decimals, or percents. Probability must always be a number between 0 and 1, inclusive of 0 and 1.*

### Try It

Construct a probability model for tossing a fair coin.

## Computing Probabilities of Equally Likely Outcomes

Let [latex]S[/latex] be a sample space for an experiment. When investigating probability, an event is any subset of [latex]S[/latex]. When the outcomes of an experiment are all equally likely, we can find the probability of an event by dividing the number of outcomes in the event by the total number of outcomes in [latex]S[/latex]. Suppose a number cube is rolled, and we are interested in finding the probability of the event “rolling a number less than or equal to 4.” There are 4 possible outcomes in the event and 6 possible outcomes in [latex]S[/latex], so the probability of the event is [latex]\frac{4}{6}=\frac{2}{3}[/latex].

### A General Note: Computing the Probability of an Event with Equally Likely Outcomes

The probability of an event [latex]E[/latex] in an experiment with sample space [latex]S[/latex] with equally likely outcomes is given by

[latex]P\left(E\right)=\dfrac{\text{number of elements in }E}{\text{number of elements in }S}=\dfrac{n\left(E\right)}{n\left(S\right)}[/latex]

[latex]E[/latex] is a subset of [latex]S[/latex], so it is always true that [latex]0\le P\left(E\right)\le 1[/latex].

### Example: Computing the Probability of an Event with Equally Likely Outcomes

A number cube is rolled. Find the probability of rolling an odd number.

### Try It

A number cube is rolled. Find the probability of rolling a number greater than 2.

## Probability for Multiple Events

We are often interested in finding the probability that one of multiple events occurs. Suppose we are playing a card game, and we will win if the next card drawn is either a heart or a king. We would be interested in finding the probability of the next card being a heart or a king. The **union of two events** [latex]E\text{ and }F,\text{written }E\cup F[/latex], is the event that occurs if either or both events occur.

[latex]P\left(E\cup F\right)=P\left(E\right)+P\left(F\right)-P\left(E\cap F\right)[/latex]

Suppose the spinner below is spun. We want to find the probability of spinning orange or spinning a [latex]b[/latex].

There are a total of 6 sections, and 3 of them are orange. So the probability of spinning orange is [latex]\frac{3}{6}=\frac{1}{2}[/latex]. There are a total of 6 sections, and 2 of them have a [latex]b[/latex]. So the probability of spinning a [latex]b[/latex] is [latex]\frac{2}{6}=\frac{1}{3}[/latex]. If we added these two probabilities, we would be counting the sector that is both orange and a [latex]b[/latex] twice. To find the probability of spinning an orange or a [latex]b[/latex], we need to subtract the probability that the sector is both orange and has a [latex]b[/latex].

[latex]\dfrac{1}{2}+\dfrac{1}{3}-\dfrac{1}{6}=\dfrac{2}{3}[/latex]

The probability of spinning orange or a [latex]b[/latex] is [latex]\dfrac{2}{3}[/latex].

### A General Note: Probability of the Union of Two Events

The probability of the union of two events [latex]E[/latex] and [latex]F[/latex] (written [latex]E\cup F[/latex] ) equals the sum of the probability of [latex]E[/latex] and the probability of [latex]F[/latex] minus the probability of [latex]E[/latex] and [latex]F[/latex] occurring together [latex]\text{(}[/latex] which is called the **intersection** of [latex]E[/latex] and [latex]F[/latex] and is written as [latex]E\cap F[/latex] ).

[latex]P\left(E\cup F\right)=P\left(E\right)+P\left(F\right)-P\left(E\cap F\right)[/latex]

### Example: Computing the Probability of the Union of Two Events

A card is drawn from a standard deck. Find the probability of drawing a heart or a 7.

### Try It

A card is drawn from a standard deck. Find the probability of drawing a red card or an ace.

### Computing the Probability of Mutually Exclusive Events

Suppose the spinner from earlier is spun again, but this time we are interested in the probability of spinning an orange or a [latex]d[/latex]. There are no sectors that are both orange and contain a [latex]d[/latex], so these two events have no outcomes in common. Events are said to be mutually exclusive events when they have no outcomes in common. Because there is no overlap, there is nothing to subtract, so the general formula is

[latex]P\left(E\cup F\right)=P\left(E\right)+P\left(F\right)[/latex]

Notice that with mutually exclusive events, the intersection of [latex]E[/latex] and [latex]F[/latex] is the empty set. The probability of spinning an orange is [latex]\frac{3}{6}=\frac{1}{2}[/latex] and the probability of spinning a [latex]d[/latex] is [latex]\frac{1}{6}[/latex]. We can find the probability of spinning an orange or a [latex]d[/latex] simply by adding the two probabilities.

[latex]\begin{align}P\left(E\cup F\right)&=P\left(E\right)+P\left(F\right) \\ &=\frac{1}{2}+\frac{1}{6} \\ &=\frac{2}{3} \end{align}[/latex]

The probability of spinning an orange or a [latex]d[/latex] is [latex]\dfrac{2}{3}[/latex].

### A General Note: Probability of the Union of Mutually Exclusive Events

The probability of the union of two *mutually exclusive* events [latex]E[/latex] and [latex]F[/latex] is given by

[latex]P\left(E\cup F\right)=P\left(E\right)+P\left(F\right)[/latex]

### How To: Given a set of events, compute the probability of the union of mutually exclusive events.

- Determine the total number of outcomes for the first event.
- Find the probability of the first event.
- Determine the total number of outcomes for the second event.
- Find the probability of the second event.
- Add the probabilities.

### Example: Computing the Probability of the Union of Mutually Exclusive Events

A card is drawn from a standard deck. Find the probability of drawing a heart or a spade.

### Try It

A card is drawn from a standard deck. Find the probability of drawing an ace or a king.

### Find the Probability That an Even Will Not Happen

We have discussed how to calculate the probability that an event will happen. Sometimes, we are interested in finding the probability that an event will *not* happen. The **complement of an event** [latex]E[/latex], denoted [latex]{E}^{\prime }[/latex], is the set of outcomes in the sample space that are not in [latex]E[/latex]. For example, suppose we are interested in the probability that a horse will lose a race. If event [latex]W[/latex] is the horse winning the race, then the complement of event [latex]W[/latex] is the horse losing the race.

To find the probability that the horse loses the race, we need to use the fact that the sum of all probabilities in a probability model must be 1.

[latex]P\left({E}^{\prime }\right)=1-P\left(E\right)[/latex]

The probability of the horse winning added to the probability of the horse losing must be equal to 1. Therefore, if the probability of the horse winning the race is [latex]\frac{1}{9}[/latex], the probability of the horse losing the race is simply

[latex]1-\dfrac{1}{9}=\dfrac{8}{9}[/latex]

### A General Note: The Complement Rule

The probability that the **complement of an event** will occur is given by

[latex]P\left({E}^{\prime }\right)=1-P\left(E\right)[/latex]

### Example: Using the Complement Rule to Calculate Probabilities

Two six-sided number cubes are rolled.

- Find the probability that the sum of the numbers rolled is less than or equal to 3.
- Find the probability that the sum of the numbers rolled is greater than 3.

### Try It

Two number cubes are rolled. Use the Complement Rule to find the probability that the sum is less than 10.

## Computing Probability Using Counting Theory

Many interesting probability problems involve counting principles, permutations, and combinations. In these problems, we will use permutations and combinations to find the number of elements in events and sample spaces. These problems can be complicated, but they can be made easier by breaking them down into smaller counting problems.

Assume, for example, that a store has 8 cellular phones and that 3 of those are defective. We might want to find the probability that a couple purchasing 2 phones receives 2 phones that are not defective. To solve this problem, we need to calculate all of the ways to select 2 phones that are not defective as well as all of the ways to select 2 phones. There are 5 phones that are not defective, so there are [latex]C\left(5,2\right)[/latex] ways to select 2 phones that are not defective. There are 8 phones, so there are [latex]C\left(8,2\right)[/latex] ways to select 2 phones. The probability of selecting 2 phones that are not defective is:

[latex]\begin{align}\frac{\text{ways to select 2 phones that are not defective}}{\text{ways to select 2 phones}}&=\frac{C\left(5,2\right)}{C\left(8,2\right)} \\[1mm] &=\frac{10}{28} \\[1mm] &=\frac{5}{14} \end{align}[/latex]

### Example: Computing Probability Using Counting Theory

A child randomly selects 5 toys from a bin containing 3 bunnies, 5 dogs, and 6 bears.

- Find the probability that only bears are chosen.
- Find the probability that 2 bears and 3 dogs are chosen.
- Find the probability that at least 2 dogs are chosen.

### Try It

A child randomly selects 3 gumballs from a container holding 4 purple gumballs, 8 yellow gumballs, and 2 green gumballs.

- Find the probability that all 3 gumballs selected are purple.
- Find the probability that no yellow gumballs are selected.
- Find the probability that at least 1 yellow gumball is selected.

## Key Equations

probability of an event with equally likely outcomes | [latex]P\left(E\right)=\dfrac{n\left(E\right)}{n\left(S\right)}[/latex] |

probability of the union of two events | [latex]P\left(E\cup F\right)=P\left(E\right)+P\left(F\right)-P\left(E\cap F\right)[/latex] |

probability of the union of mutually exclusive events | [latex]P\left(E\cup F\right)=P\left(E\right)+P\left(F\right)[/latex] |

probability of the complement of an event | [latex]P\left(E\text{‘}\right)=1-P\left(E\right)[/latex] |

## Key Concepts

- Probability is always a number between 0 and 1, where 0 means an event is impossible and 1 means an event is certain.
- The probabilities in a probability model must sum to 1.
- When the outcomes of an experiment are all equally likely, we can find the probability of an event by dividing the number of outcomes in the event by the total number of outcomes in the sample space for the experiment.
- To find the probability of the union of two events, we add the probabilities of the two events and subtract the probability that both events occur simultaneously.
- To find the probability of the union of two mutually exclusive events, we add the probabilities of each of the events.
- The probability of the complement of an event is the difference between 1 and the probability that the event occurs.
- In some probability problems, we need to use permutations and combinations to find the number of elements in events and sample spaces.

## Glossary

**complement of an event** the set of outcomes in the sample space that are not in the event [latex]E[/latex]

**event** any subset of a sample space

**experiment** an activity with an observable result

**mutually exclusive events** events that have no outcomes in common

**outcomes** the possible results of an experiment

**probability** a number from 0 to 1 indicating the likelihood of an event

**probability model** a mathematical description of an experiment listing all possible outcomes and their associated probabilities

**sample space** the set of all possible outcomes of an experiment

**union of two events** the event that occurs if either or both events occur

- The figure is for illustrative purposes only and does not model any particular storm. ↵