/*! This file is auto-generated */ .wp-block-button__link{color:#fff;background-color:#32373c;border-radius:9999px;box-shadow:none;text-decoration:none;padding:calc(.667em + 2px) calc(1.333em + 2px);font-size:1.125em}.wp-block-file__button{background:#32373c;color:#fff;text-decoration:none} Problem 3 Consider the chance experiment i... [FREE SOLUTION] | 91Ó°ÊÓ

91Ó°ÊÓ

Consider the chance experiment in which the type of transmission-automatic (A) or manual (M) - is recorded for each of the next two cars purchased from a certain dealer. a. What is the set of all possible outcomes (the sample space)? b. Display the possible outcomes in a tree diagram. c. List the outcomes in each of the following events. Which of these events are simple events? i. \(B\) the event that at least one car has an automatic transmission ii. \(C\) the event that exactly one car has an automatic transmission iii. \(D\) the event that neither car has an automatic transmission d. What outcomes are in the event \(B\) and \(C ?\) In the event \(B\) or \(C\) ?

Short Answer

Expert verified
The sample space of car transmission outcomes is {AA, AM, MA, MM}. The event that at least one car has an automatic includes {AA, AM, MA}, exactly one car with an automatic includes {AM, MA}, neither car having automatic has {MM}. Outcomes in both event B and C are {AA, AM, MA}, and for B or C are also {AA, AM, MA}.

Step by step solution

01

Identify Sample Space

Identify the possible outcomes when purchasing two cars each with either automatic \(A\) or manual \(M\) transmission. This leads to four outcomes overall: \(AA, AM, MA, MM\).
02

Construct a Tree Diagram

From the origin point, create two branches representing the options for the first vehicle: automatic \(A\) and manual \(M\). Branch off each of these options again for the second vehicle, again with automatic \(A\) and manual \(M\). This creates outcomes \(AA, AM, MA, MM\).
03

List Outcomes for Each Event

i. Event \(B\) (at least one automatic transmission) includes outcomes \(AA, AM, MA\).\nii. Event \(C\) (exactly one automatic transmission) includes outcomes \(AM, MA\).\niii. Event \(D\) (neither car has automatic transmission) includes the outcome \(MM\). Here, \(B\) and \(C\) are compound events, while \(D\) is a simple one as it only includes one outcome.
04

Define Overlaps in Events

Event \(B\) and \(C\) (both cars are automatic or exactly one is automatic) includes outcomes \(AA, AM, MA\). The event \(B\) or \(C\) (at least one car is automatic or one car is automatic) includes outcomes \(AA, AM, MA\).

Unlock Step-by-Step Solutions & Ace Your Exams!

  • Full Textbook Solutions

    Get detailed explanations and key concepts

  • Unlimited Al creation

    Al flashcards, explanations, exams and more...

  • Ads-free access

    To over 500 millions flashcards

  • Money-back guarantee

    We refund you if you fail your exam.

Over 30 million students worldwide already upgrade their learning with 91Ó°ÊÓ!

Key Concepts

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

Sample Space
In probability, a sample space is the set of all possible outcomes from a chance experiment. When we talk about the probability of certain events occurring, we're checking to see if specific conditions are met within this space. Let's dive into the specific exercise involving car transmissions.
The exercise involves identifying the type of transmission, either automatic (A) or manual (M), for two cars. Here, the sample space consists of all possible combinations of these transmissions for both cars.
For an experiment like this, it's helpful to think about each car individually and then combine those outcomes. The possible outcomes for the first car - automatic or manual - can pair with similar outcomes for the second car. Thus, the sample space includes four combinations:
  • AA - both cars have automatic transmission.
  • AM - first car is automatic, second is manual.
  • MA - first car is manual, second is automatic.
  • MM - both cars have manual transmission.
Recognizing the sample space helps us calculate the probabilities of various events.
Tree Diagram
A tree diagram is an excellent tool for visually representing potential outcomes of a probability experiment. It helps you think through the sequencing of events and simplifies calculation of probabilities.
For our exercise, we use a tree diagram to map out the possible sequences of transmission types for two cars. Begin by drawing two branches, one for each possible outcome of the first car:
  • Automatic (A)
  • Manual (M)
From each of these branches, draw another two branches to represent the outcomes for the second car: automatic or manual. This results in the combined outcomes:
  • Start with A:
    • AA (both cars automatic)
    • AM (first automatic, second manual)
  • Start with M:
    • MA (first manual, second automatic)
    • MM (both cars manual)
Tree diagrams help you visually organize outcomes, making it easier to identify different events.
Simple and Compound Events
Understanding the difference between simple and compound events is crucial in probability. A simple event involves only one outcome from the sample space, whereas a compound event contains multiple outcomes.
In our example, we have events based on the combination of transmission types:
  • Event B: At least one car has an automatic transmission. This includes outcomes: AA, AM, MA. Since multiple outcomes satisfy this condition, it's a compound event.
  • Event C: Exactly one car has an automatic transmission. This involves outcomes: AM, MA, making it another compound event.
  • Event D: Neither car has an automatic transmission. This only involves one outcome: MM. Therefore, it is a simple event.
By distinguishing between these events, we get better insight into how probabilities operate in different situations. It helps in making predictions and understanding outcomes of real-world occurrences clearly.

One App. One Place for Learning.

All the tools & learning materials you need for study success - in one app.

Get started for free

Most popular questions from this chapter

Suppose that an individual is randomly selected from the population of all adult males living in the United States. Let \(A\) be the event that the selected individual is over 6 feet in height, and let \(B\) be the event that the selected individual is a professional basketball player. Which do you think is larger, \(P(A \mid B)\) or \(P(B \mid A)\) ? Why?

The report "Twitter in Higher Education: Usage Habits and Trends of Today's College Faculty" (Magna Publications, September 2009) describes results of a survey of nearly 2000 college faculty. The report indicates the following: \- \(30.7 \%\) reported that they use Twitter and \(69.3 \%\) said that they did not use Twitter. \- Of those who use Twitter, \(39.9 \%\) said they sometimes use Twitter to communicate with students. \- Of those who use Twitter, \(27.5 \%\) said that they sometimes use Twitter as a learning tool in the classroom. Consider the chance experiment that selects one of the study participants at random and define the following events: \(T=\) event that selected faculty member uses Twitter \(C=\) event that selected faculty member sometimes uses Twitter to communicate with students \(L=\) event that selected faculty member sometimes uses Twitter as a learning tool in the classroom a. Use the given information to determine the following probabilities: i. \(\quad P(T)\) ii. \(P\left(T^{C}\right)\) iii. \(P(C \mid T)\) iv. \(P(L \mid T)\) v. \(P(C \cap T)\) b. Interpret each of the probabilities computed in Part (a). c. What proportion of the faculty surveyed sometimes use Twitter to communicate with students? [Hint: Use the law of total probability to find \(P(C) .]\) d. What proportion of faculty surveyed sometimes use Twitter as a learning tool in the classroom?

A radio station that plays classical music has a "by request" program each Saturday evening. The percentages of requests for composers on a particular night are as follows: $$ \begin{array}{lr} \text { Bach } & 5 \% \\ \text { Beethoven } & 26 \% \\ \text { Brahms } & 9 \% \\ \text { Dvorak } & 2 \% \\ \text { Mendelssohn } & 3 \% \\ \text { Mozart } & 21 \% \\ \text { Schubert } & 12 \% \\ \text { Schumann } & 7 \% \\ \text { Tchaikovsky } & 14 \% \\ \text { Wagner } & 1 \% \end{array} $$ Suppose that one of these requests is to be selected at random. a. What is the probability that the request is for one of the three B's (Bach, Beethoven, or Brahms)? b. What is the probability that the request is not for one of the two \(S^{\prime}\) s? c. All of the listed composers wrote at least one symphony except Bach and Wagner. What is the probability that the request is for a composer who wrote at least one symphony?

In a small city, approximately \(15 \%\) of those eligible are called for jury duty in any one calendar year. People are selected for jury duty at random from those eligible, and the same individual cannot be called more than once in the same year. What is the probability that a particular eligible person in this city is selected in each of the next 2 years? In each of the next 3 years?

The paper "Good for Women, Good for Men, Bad for People: Simpson's Paradox and the Importance of Sex-Spedfic Analysis in Observational Studies" (Journal of Women's Health and Gender-Based Medicine [2001]: \(867-872\) ) described the results of a medical study in which one treatment was shown to be better for men and better for women than a competing treatment. However, if the data for men and women are combined, it appears as though the competing treatment is better. To see how this can happen, consider the accompanying data tables constructed from information in the paper. Subjects in the study were given either Treatment \(\mathrm{A}\) or Treatment \(\mathrm{B}\), and survival was noted. Let \(S\) be the event that a patient selected at random survives, \(A\) be the event that a patient selected at random received Treatment \(\mathrm{A}\), and \(B\) be the event that a patient selected at random received Treatment \(\mathrm{B}\). a. The following table summarizes data for men and women combined: $$ \begin{array}{l|ccc} & \text { Survived } & \text { Died } & \text { Total } \\ \hline \text { Treatment A } & 215 & 85 & \mathbf{3 0 0} \\ \text { Treatment B } & 241 & 59 & \mathbf{3 0 0} \\ \text { Total } & \mathbf{4 5 6} & \mathbf{1 4 4} & \\ \hline \end{array} $$ i. Find \(P(S)\). ii. Find \(P(S \mid A)\). iii. Find \(P(S \mid B)\). iv. Which treatment appears to be better? b. Now consider the summary data for the men who participated in the study: $$ \begin{array}{l|rrr} & \text { Survived } & \text { Died } & \text { Total } \\ \hline \text { Treatment A } & 120 & 80 & \mathbf{2 0 0} \\ \text { Treatment B } & 20 & 20 & 40 \\ \text { Total } & \mathbf{1 4 0} & \mathbf{1 0 0} & \\ \hline \end{array} $$ i. Find \(P(S)\). ii. Find \(P(S \mid A)\). iii. Find \(P(S \mid B)\). iv. Which treatment appears to be better? c. Now consider the summary data for the women who participated in the study: $$ \begin{array}{l|rrc} & \text { Survived } & \text { Died } & \text { Total } \\ \hline \text { Treatment A } & 95 & 5 & \mathbf{1 0 0} \\ \text { Treatment B } & 221 & 39 & \mathbf{2 6 0} \\ \text { Total } & \mathbf{3 1 6} & \mathbf{1 4 4} & \\ \hline \end{array} $$ i. Find \(P(S)\). ii. Find \(P(S \mid A)\). iii. Find \(P(S \mid B)\). iv. Which treatment appears to be better? d. You should have noticed from Parts (b) and (c) that for both men and women, Treatment \(A\) appears to be better. But in Part (a), when the data for men and women are combined, it looks like Treatment \(\mathrm{B}\) is better. This is an example of what is called Simpson's paradox. Write a brief explanation of why this apparent inconsistency occurs for this data set. (Hint: Do men and women respond similarly to the two treatments?)

See all solutions

Recommended explanations on Math Textbooks

View all explanations

What do you think about this solution?

We value your feedback to improve our textbook solutions.

Study anywhere. Anytime. Across all devices.