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Section 9.2 Working with Events

Subsection 9.2.1 Complementary Events

Now let us examine the probability that an event does not happen. As in the previous section, consider the situation of rolling a six-sided die and first compute the probability of rolling a six: the answer is \(P\left( E \right) = \frac{1}{6}\text{.}\) Now consider the probability that we do not roll a six: there are 5 outcomes that are not a six, so the answer is \(P\left( \text{not } E \right) = \frac{5}{6}\text{.}\) Notice that

\begin{equation*} P\left(\text{six}\right) + P\left( \text{not six}\right) = \frac{1}{6} + \frac{5}{6} = 1 \end{equation*}

This is not a coincidence. Consider a generic situation with \(n\) possible outcomes and an event \(E\) that corresponds to \(m\) of these outcomes. Then the remaining \(n - m\) outcomes correspond to \(E\) not happening, thus

\begin{equation*} P\left( \text{not } E \right) = \frac{n - m}{n} = \frac{n}{n} - \frac{m}{n} = 1 - P\left(E \right) \end{equation*}
Definition 9.2.1. Complement of an Event.

The complement of an event is the event “E doesn’t happen”

The notation \(E^c\) is used for the complement of event \(E\text{.}\)

We can compute the probability of the complement using \(P\left(E^c\right) = 1 - P\left(E\right)\text{.}\)

Notice also that \(P\left(E\right)=1-P\left(E^c\right)\)

Example 9.2.2.

If you pull a random card from a deck of playing cards, what is the probability it is not a heart?

Solution.

There are 13 hearts in the deck, so \(P\left(\text{heart}\right)=\frac{13}{52}=\frac{1}{4}\text{.}\)

The probability of not drawing a heart is the complement:

\(P\left(\text{not heart}\right)=1-P\left(\text{heart}\right)=1-\frac{1}{4}=\frac{3}{4}\)

Subsection 9.2.2 Probability of two independent events

Example 9.2.3.

Suppose we flipped a coin and rolled a die, and wanted to know the probability of getting a head on the coin and a 6 on the die.

Solution.

We could list all possible outcomes: {H1, H2, H3, H4, H5, H6, T1, T2, T3, T4, T5, T6}

Notice there are \(2\cdot 6=12\) total outcomes. Out of these, only 1 is the desired outcome, so the probability is \(\frac{1}{12}\text{.}\)

The prior example was looking at two independent events.

Definition 9.2.4. Independent Events.

Events A and B are independent events if the probability of Event B occurring is the same whether or not Event A occurs.

Example 9.2.5.

Are these events independent?

  1. A fair coin is tossed two times. The two events are (1) first toss is a head and (2) second toss is a head.

  2. The two events (1) "It will rain tomorrow in Houston" and (2) "It will rain tomorrow in Galveston” (a city near Houston).

  3. You draw a card from a deck, then draw a second card without replacing the first.

Solution.
  1. The probability that a head comes up on the second toss is \(\frac{1}{2}\) regardless of whether or not a head came up on the first toss, so these events are independent.

  2. These events are not independent because it is more likely that it will rain in Galveston on days it rains in Houston than on days it does not.

  3. The probability of the second card being red depends on whether the first card is red or not, so these events are not independent.

When two events are independent, the probability of both occurring is the product of the probabilities of the individual events.

Note 9.2.6. P(\(A\) and \(B\)) for Independent Events.

If events \(A\) and \(B\) are independent, then the probability of both \(A\) and \(B\) occurring is

\begin{equation*} P\left(A \text{ and } B\right)=P\left(A\right)\cdot P\left(B\right) \end{equation*}

where \(P\left(A \text{ and } B \right)\) is the probability of events \(A\) and \(B\) both occurring, \(P\left(A\right)\) is the probability of event \(A\) occurring, and \(P\left(B \right)\) is the probability of event \(B\) occurring.

If you look back at the coin and die example from earlier, you can see how the number of outcomes of the first event multiplied by the number of outcomes in the second event multiplied to equal the total number of possible outcomes in the combined event.

Example 9.2.7.

In your drawer you have 10 pairs of socks, 6 of which are white, and 7 tee shirts, 3 of which are white. If you randomly reach in and pull out a pair of socks and a tee shirt, what is the probability both are white?

Solution.

The probability of choosing a white pair of socks is \(\frac{6}{10} = \frac{3}{5}\text{.}\)

The probability of choosing a white tee shirt is \(\frac{3}{7}\)

.

The probability of both being white is \(\frac{3}{5} \cdot \frac{3}{7}=\frac{9}{35}\)

Problem 9.2.8. Try It Now.

A card is pulled a deck of cards and noted. The card is then replaced, the deck is shuffled, and a second card is removed and noted. What is the probability that both cards are Aces?

Answer.
\(\frac{1}{13} \cdot \frac{1}{13} = \frac{1}{169}\)

The previous examples looked at the probability of both events occurring. Now we will look at the probability of either event occurring.

Example 9.2.9.

Suppose we flipped a coin and rolled a die, and wanted to know the probability of getting a head on the coin or a 6 on the die.

Solution.

Here, there are still 12 possible outcomes: {H1,H2,H3,H4,H5,H6,T1,T2,T3,T4,T5,T6}

By simply counting, we can see that 7 of the outcomes have a head on the coin or a 6 on the die or both - we use or inclusively here (these 7 outcomes are H1, H2, H3, H4, H5, H6, T6), so the probability is \(\frac{7}{12}\text{.}\) How could we have found this from the individual probabilities?

As we would expect, \(\frac{1}{2}\) of these outcomes have a head, and \(\frac{1}{6}\) of these outcomes have a 6 on the die. If we add these, \(\frac{1}{2} + \frac{1}{6} = \frac{6}{12} + \frac{2}{12} = \frac{8}{12}\) , which is not the correct probability.

Looking at the outcomes we can see why: the outcome H6 would have been counted twice, since it contains both a head and a 6; the probability of both a head and rolling a 6 is \(\frac{1}{12}\text{.}\) If we subtract out this double count, we have the correct probability: \(\frac{8}{12} - \frac{1}{12} = \frac{7}{12}\)

Note 9.2.10. P(A or B).

The probability of either A or B occurring (or both) is

\begin{equation*} P\left(A \text{ or } B\right) = P\left(A\right)+P\left(B\right)-P\left(A\text{ and }B\right) \end{equation*}
Example 9.2.11.

Suppose we draw one card from a standard deck. What is the probability that we get a Queen or a King?

Solution.

There are 4 Queens and 4 Kings in the deck, hence 8 outcomes corresponding to a Queen or King out of 52 possible outcomes. Thus the probability of drawing a Queen or a King is:

P(King or Queen) = \(\frac{8}{52} = \frac{2}{13}\)

Note that in this case, there are no cards that are both a Queen and a King, so P(King and Queen) = 0. Using our probability rule, we could have said:

\(P\)(King or Queen)\(=P\)(King)\(+P\)(Queen)\(-P\)(King and Queen)\(= \frac{4}{52}+ \frac{4}{52} - 0 = \frac{8}{52}\)

In the last example, the events were mutually exclusive, so P(A or B) = P(A) + P(B).

Example 9.2.12.

Suppose we draw one card from a standard deck. What is the probability that we get a red card or a King?

Solution.

Half the cards are red, so \(P\left(\text{red}\right)=\frac{26}{52}\)

There are four kings, so \(P\left(\text{King}\right)=\frac{4}{52}\)

There are two red kings, so \(P\left(\text{Red and King}\right)=\frac{2}{52}\)

We can then calculate

\(P\left(\text{Red or King}\right) = P\left(\text{Red}\right) + P\left(\text{King}\right) - P\left(\text{Red and King}\right) = \frac{26}{52}+\frac{4}{52} - \frac{2}{52} = \frac{28}{52} = \frac{7}{13}\)

Problem 9.2.13. Try It Now.

In your drawer you have 10 pairs of socks, 6 of which are white, and 7 tee shirts, 3 of which are white. If you reach in and randomly grab a pair of socks and a tee shirt, what the probability at least one is white?

Answer.

P(white sock and white tee) = \(\frac{6}{10} \cdot \frac{3}{7} = \frac{18}{70} = \frac{9}{35}\)

P(white sock or white tee) = \(\frac{6}{10} + \frac{3}{7} - \frac{9}{35} = \frac{27}{35}\)

Example 9.2.14.

The table below shows the number of survey subjects who have received and not received a speeding ticket in the last year, and the color of their car. Find the probability that a randomly chosen person:

  1. Has a red car and got a speeding ticket.

  2. Has a red car or got a speeding ticket.

Table 9.2.15.
Speeding Ticket No Speeding Ticket Total
Red Car 15 135 150
Not Red Car 45 470 515
Total 60 605 665
Solution.
  1. We can see that 15 people of the 665 surveyed had both a red car and got a speeding ticket, so the probability is \(\frac{15}{665} \approx 0.0226\text{.}\)

    Notice that having a red car and getting a speeding ticket are not independent events, so the probability of both of them occurring is not simply the product of probabilities of each one occurring.

  2. We could answer this question by simply adding up the numbers: 15 people with red cars and speeding tickets + 135 with red cars but no ticket + 45 with a ticket but no red car = 195 people. So the probability is \(\frac{195}{665} \approx 0.2932\text{.}\)

    We also could have found this probability by:

    P(had a red car) + P(got a speeding ticket) - P(had a red car and got a speeding ticket)=\(\frac{150}{665}+\frac{60}{665}-\frac{15}{665}=\frac{195}{665}\text{.}\)