/*! 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 18 Verify that the following functi... [FREE SOLUTION] | 91Ó°ÊÓ

91Ó°ÊÓ

Verify that the following functions are probability mass functions, and determine the requested probabilities. \(f(x)=(8 / 7)(1 / 2)^{x}, \quad x=1,2,3\) (a) \(P(X \leq 1)\) (b) \(P(X>1)\) (c) \(P(21)\)

Short Answer

Expert verified
(a) \(\frac{4}{7}\), (b) \(\frac{3}{7}\), (c) 0, (d) 1.

Step by step solution

01

Verify the Probability Mass Function

To verify that \(f(x) = \frac{8}{7}\left(\frac{1}{2}\right)^{x}\) is a probability mass function, we need to ensure that the sum of \(f(x)\) over all possible values of \(x\) is equal to 1.Calculate the sum: \[\sum_{x=1}^{3} f(x) = \frac{8}{7}\left(\frac{1}{2}\right)^1 + \frac{8}{7}\left(\frac{1}{2}\right)^2 + \frac{8}{7}\left(\frac{1}{2}\right)^3 \]Calculate each term:\(\frac{8}{7} \times \frac{1}{2} = \frac{8}{14}\),\(\frac{8}{7} \times \frac{1}{4} = \frac{8}{28}\),\(\frac{8}{7} \times \frac{1}{8} = \frac{8}{56}\).Add these probabilities: \[\frac{8}{14} + \frac{8}{28} + \frac{8}{56} = \frac{32}{56} + \frac{16}{56} + \frac{8}{56} = \frac{56}{56} = 1\]The function \(f(x)\) satisfies the properties of a probability mass function.
02

Calculate \(P(X \leq 1)\)

To find \(P(X \leq 1)\), calculate the probability when \(x = 1\).\[P(X \leq 1) = f(1) = \frac{8}{7}\left(\frac{1}{2}\right)^1 = \frac{8}{14} = \frac{4}{7} \]
03

Calculate \(P(X > 1)\)

\(P(X > 1)\) requires adding up \(f(x)\) for \(x = 2\) and \(x = 3\).\[P(X > 1) = f(2) + f(3) = \frac{8}{7}\left(\frac{1}{2}\right)^2 + \frac{8}{7}\left(\frac{1}{2}\right)^3 \]\[= \frac{8}{28} + \frac{8}{56} = \frac{16}{56} + \frac{8}{56} = \frac{24}{56} = \frac{3}{7}\]
04

Calculate \(P(2 < X < 6)\)

Since \(f(x)\) is only defined for \(x = 1, 2, 3\), \(P(2 < X < 6)\) means finding \(0\) since no values satisfy this.There are no \(x\) values in the range \(2 < x < 6\), so:\[P(2 < X < 6) = 0\]
05

Calculate \(P(X \leq 1 \text{ or } X > 1)\)

This requires adding probabilities from Steps 2 and 3 since they cover disjoint sets:\[P(X \leq 1 \text{ or } X > 1) = P(X \leq 1) + P(X > 1)\]Using the results:\[= \frac{4}{7} + \frac{3}{7} = \frac{7}{7} = 1\]
06

Conclusion

All calculations are verified, and the function is a probability mass function. The probabilities requested are:\(P(X \leq 1) = \frac{4}{7}\),\(P(X > 1) = \frac{3}{7}\),\(P(2 < X < 6) = 0\),\(P(X \leq 1 \text{ or } X > 1) = 1\).

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.

Discrete Probability
In the world of probability, a discrete probability distribution describes events where the outcomes are distinct and separate. These events are not continuous, meaning each has its own probability of occurring. Think of rolling a die, where the possible outcomes are specific numbers like 1, 2, 3, 4, 5, and 6. Each number appears with its own probability.

Discrete probability deals with these distinct outcomes and helps us understand the likelihood of each event. It's a core part of probability theory and essential for calculating how likely certain scenarios are, based on these discrete events.
  • Each event in discrete probability has its own specific probability.
  • These probabilities help us model and predict outcomes.
  • Discrete events are separate; they don't merge into each other.
Sum of Probabilities
The sum of probabilities for a discrete distribution is a crucial concept that ensures the legitimacy of a probability model. For any probability mass function (PMF), the sum of all individual probabilities must equal 1. This total sum represents the entire possible outcome space, making sure that we account for all potential events.

In simpler terms, if you add up the probabilities of every possible outcome, you'll get 100%. That's how we verify whether a PMF is correct. For the example provided, summing the probabilities as shown verifies that they add up to 1, hence confirming that it's a true probability mass function.
  • The sum must equal 1, representing all possible outcomes.
  • This is a fundamental truth for any probability model.
  • It confirms the completeness and correctness of the probability distribution.
Probability Calculations
Probability calculations involve mathematical methods used to determine the likelihood of specific events happening within a discrete probability distribution. To calculate these probabilities, apply the given PMF to each outcome to find the probability associated with it.

For instance, finding the probability of a single event like \(P(X \leq 1)\) involves selecting the correct data points and applying the PMF. Likewise, calculating \(P(X > 1)\) involves summing the probabilities of all outcomes greater than 1, which can be seen in the solution by adding the probabilities when \(x = 2\) and \(x = 3\).
  • Identify the range of interest for the calculation.
  • Apply the PMF to calculate the probability for each event in the range.
  • Sum the probabilities to find the total likelihood of the range.
Verification of PMF
Verifying a probability mass function ensures that the distribution used makes sense mathematically and corresponds to real-world expectations. This involves checking that the total sum of probabilities is 1 and that each individual probability is between 0 and 1.

By confirming these criteria, you ensure that the PMF correctly models the scenario. For the exercise solution given, this process reaffirmed that each calculated probability is logically sound and the distribution adds up perfectly to 1.

This step is essential:
  • It makes sure each value is a true probability (0 ≤ value ≤ 1).
  • The sum of all values equals 1, confirming completeness.
  • Verification builds confidence in the overall accuracy of your model.

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

Let the random variable \(X\) be equally likely to assume any of the values \(1 / 8,1 / 4,\) or \(3 / 8 .\) Determine the mean and variance of \(X\).

Suppose that \(X\) has a hypergeometric distribution with \(N=100, n=4,\) and \(K=20 .\) Determine the following: (a) \(P(X=1)\) (b) \(P(X=6)\) (c) \(P(X=4)\) (d) Mean and variance of \(X\)

A large bakery can produce rolls in lots of either \(0,\) \(1000,2000,\) or 3000 per day. The production cost per item is \(\$ 0.10 .\) The demand varies randomly according to the following distribution: $$ \begin{array}{lcccc} \text { Demand for rolls } & 0 & 1000 & 2000 & 3000 \\ \text { Probability of demand } & 0.3 & 0.2 & 0.3 & 0.2 \end{array} $$ Every roll for which there is a demand is sold for \(\$ 0.30 .\) Every roll for which there is no demand is sold in a secondary market for \(\$ 0.05 .\) How many rolls should the bakery produce each day to maximize the mean profit?

Samples of 20 parts from a metal punching process are selected every hour. Typically, \(1 \%\) of the parts require rework. Let \(X\) denote the number of parts in the sample of 20 that require rework. A process problem is suspected if \(X\) exceeds its mean by more than 3 standard deviations. (a) If the percentage of parts that require rework remains at \(1 \%,\) what is the probability that \(X\) exceeds its mean by more than 3 standard deviations? (b) If the rework percentage increases to \(4 \%,\) what is the probability that \(X\) exceeds \(1 ?\) (c) If the rework percentage increases to \(4 \%,\) what is the probability that \(X\) exceeds 1 in at least one of the next five hours of samples?

Suppose that the random variable \(X\) has a geometric distribution with a mean of \(2.5 .\) Determine the following probabilities: (a) \(P(X=1)\) (b) \(P(X=4)\) (c) \(P(X=5)\) (d) \(P(X \leq 3)\) (e) \(P(X>3)\)

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.