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Consider the experiment of tossing a coin three times. a. Develop a tree diagram for the experiment. b. List the experimental outcomes. c. What is the probability for each experimental outcome?

Short Answer

Expert verified
Each outcome has a probability of \( \frac{1}{8} \).

Step by step solution

01

Understanding the Experiment

The experiment consists of tossing a coin three times. Each toss results in either heads (H) or tails (T). Thus, each toss represents one of two possible outcomes.
02

Developing the Tree Diagram

Start with the first toss: draw two branches, one for heads (H) and one for tails (T). From each of these branches, draw two more branches for the second toss (again H or T). Repeat this branching for the third toss. The tree diagram will have 2 branches after the first toss, 4 after the second, and 8 at the end of the third toss.
03

Listing the Experimental Outcomes

Follow each path from the tree diagram starting from the beginning to the end. These paths form the different experimental outcomes: HHH, HHT, HTH, HTT, THH, THT, TTH, TTT. This gives us a complete list of outcomes.
04

Calculating the Probability of Each Outcome

Each outcome has the same chance of occurring due to the coin being fair. There are 8 possible outcomes. The probability of each outcome is 1 divided by the total number of outcomes: \( \frac{1}{8} \).

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Experimental Outcomes
When conducting any experiment in probability theory, one of the foundational concepts is identifying the experimental outcomes. In the coin toss experiment, you toss a coin three times. For each toss, you can either get a Head (H) or a Tail (T). Each result from these three tosses forms an outcome.

Think of each outcome as a possible result of the entire sequence of activities. Here, an outcome is a specific order of Heads and Tails across the three tosses.
  • If you toss a coin three times, every sequence like HHH or THT is a distinct outcome.
  • Listing all outcomes means considering every possible arrangement of H and T for the three tosses.
  • For three tosses, you have outcomes: HHH, HHT, HTH, HTT, THH, THT, TTH, TTT.
  • Therefore, there are a total of 8 outcomes, as each toss has 2 possibilities and you calculate this as 2 multiplied by itself for 3 times: 2×2×2 = 8.
Tree Diagram
Tree diagrams are powerful visual tools in probability theory. They help you systematically display all possible outcomes of an experiment.

In the coin toss experiment, you start your tree diagram with the result of the first coin toss. Draw two branches to signify the two potential outcomes: Heads (H) or Tails (T). For each of these results, draw two more branches to represent the outcomes of the second toss — which again could be either Heads or Tails.

Finally, for each branch created from the second toss, draw another set of two branches for the third toss.
  • After the first toss, you have 2 branches.
  • After the second toss, the tree expands to 4 branches.
  • By the third toss, your diagram will have 8 branches.
Each path through the tree represents one of the possible experimental outcomes. By tracing a path from the start to the end of a branch, you can easily list the outcomes. It makes it clear to see how each outcome is formed by a sequence of Heads and Tails.
Coin Toss Experiment
Coin toss experiments provide an excellent illustration of basic probability concepts due to their simplicity and fairness. A fair coin inherently gives a 50/50 chance for each outcome: either Heads (H) or Tails (T).

In a coin toss experiment with three tosses, each throw is independent. This means the outcome of one toss does not affect the next toss. As such, this generates multiple pathways for outcomes, which, when considered together, form a comprehensive outcome set. Each distinct sequence of Hs and Ts represents a possible outcome.
  • The probability of landing on any specific sequence, like HTT or HHT, is equal across all possible outcomes.
  • Because the probability of heads or tails is \( \frac{1}{2} \), the chance of any specific sequence in three tosses is \( \frac{1}{8} \) (or \( \frac{1}{2} \times \frac{1}{2} \times \frac{1}{2} \)).
This concept of fairness ensures that each outcome is equally likely, which is a fundamental aspect of probability theory.

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Most popular questions from this chapter

High school seniors with strong academic records apply to the nation's most selective colleges in greater numbers each year. Because the number of slots remains relatively stable, some colleges reject more early applicants. The University of Pennsylvania received 2851 applications for early admission. Of this group, it admitted 1033 students early, rejected 854 outright, and deferred 964 to the regular admissions pool for further consideration. In the past, Penn has admitted about \(18 \%\) of the deferred early admission applicants during the regular admission process. Counting the additional students who were admitted during the regular admission process, the total class size was 2375 (USA Today, January 24,2001 ). Let \(E, R,\) and \(D\) represent the events that a student who applies for early admission is admitted early, rejected outright, or deferred to the regular admissions pool. a. Use the data to estimate \(P(E), P(R),\) and \(P(D)\) b. Are events \(E\) and \(D\) mutually exclusive? Find \(P(E \cap D)\) c. For the 2375 students admitted to Penn, what is the probability that a randomly selected student was accepted for early admission? d. Suppose a student applies to Penn for early admission. What is the probability the student will be admitted for early admission or be deferred and later admitted during the regular admission process?

In a BusinessWeek/Harris Poll, 1035 adults were asked about their attitudes toward business (BusinessWeek, September 11, 2000). One question asked: "How would you rate large U.S. companies on making good products and competing in a global environment?" The responses were: excellent- \(18 \%\), pretty good \(-50 \%\), only fair- \(26 \%\), poor \(-5 \%\) and don't know/no answer- \(1 \%\) a. What is the probability that a respondent rated U.S. companies pretty good or excellent? b. How many respondents rated U.S. companies poor? c. How many respondents did not know or did not answer?

The prior probabilities for events \(A_{1}\) and \(A_{2}\) are \(P\left(A_{1}\right)=.40\) and \(P\left(A_{2}\right)=.60 .\) It is also known that \(P\left(A_{1} \cap A_{2}\right)=0 .\) Suppose \(P\left(B \text { ? } A_{1}\right)=.20\) and \(P\left(B | A_{2}\right)=.05\) a. Are \(A_{1}\) and \(A_{2}\) mutually exclusive? Explain. b. Compute \(P\left(A_{1} \cap B\right)\) and \(P\left(A_{2} \cap B\right)\) c. Compute \(P(B)\) d. Apply Bayes' theorem to compute \(P\left(A_{1} | B\right)\) and \(P\left(A_{2} | B\right)\).

The U.S. population by age is as follows (The World Almanac, 2004 ). The data are in millions of people. $$\begin{array}{lc} \text { Age } & \text { Number } \\ 19 \text { and under } & 80.5 \\ 20 \text { to } 24 & 19.0 \\ 25 \text { to } 34 & 39.9 \\ 35 \text { to } 44 & 45.2 \\ 45 \text { to } 54 & 37.7 \\ 55 \text { to } 64 & 24.3 \\ 65 \text { and over } & 35.0 \end{array}$$ Assume that a person will be randomly chosen from this population. a. What is the probability the person is 20 to 24 years old? b. What is the probability the person is 20 to 34 years old? c. What is the probability the person is 45 years or older?

Suppose that we have a sample space with five equally likely experimental outcomes: \(E_{1}\) \(E_{2}, E_{3}, E_{4}, E_{5} \text { . Let } \) \\[ \begin{aligned} \qquad \begin{aligned} A &=\left\\{E_{1}, E_{2}\right\\} \\ B &=\left\\{E_{3}, E_{4}\right\\} \\ C &=\left\\{E_{2}, E_{3}, E_{5}\right\\} \end{aligned} \end{aligned} \\] a. Find \(P(A), P(B),\) and \(P(C)\) b. Find \(P(A \cup B)\). Are \(A\) and \(B\) mutually exclusive? c. \(\quad\) Find \(A^{c}, C^{c}, P\left(A^{c}\right),\) and \(P\left(C^{c}\right)\) d. Find \(A \cup B^{c}\) and \(P\left(A \cup B^{c}\right)\) e. Find \(P(B \cup C)\)

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