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Suppose a playlist on an MP3 music player consisting of 100 songs includes 8 by a particular artist. Suppose that songs are played by selecting a song at random (with replacement) from the playlist. The random variable \(x\) represents the number of songs until a song by this artist is played. a. Explain why the probability distribution of \(x\) is not binomial. b. Find the following probabilities. (Hint: See Example \(6.31 .\) ) i. \(p(4)\) ii. \(P(x \leq 4)\) iii. \(P(x>4)\) iv. \(P(x \geq 4)\) c. Interpret each of the probabilities in Part (b) and explain the difference between them.

Short Answer

Expert verified
The probability distribution of $x$ is not binomial because it represents the number of trials until success occurs, and the trials do not have a fixed number. The appropriate distribution for this problem is a geometric distribution with a probability of success $p = \frac{8}{100}$. Using the geometric distribution, a) \(p(4) \approx 0.0575\): The probability that the first song by the particular artist is played as the 4th song. b) \(P(x\leq4) \approx 0.2819\): The probability that the first song by the particular artist is played within the first 4 songs. c) \(P(x>4) \approx 0.7181\): The probability that the first song by the particular artist is played after the first 4 songs. d) \(P(x \geq 4) \approx 0.7775\): The probability that the first song by the particular artist is played on or after the 4th song. Each probability represents a different way of looking at when the first song by the particular artist will be played, such as before, after, or on a certain song position.

Step by step solution

01

Explain why the probability distribution of x is not binomial

A binomial distribution is appropriate for a random variable that represents the number of successes in a fixed number of Bernoulli trials, which are independent with identical probability of success on each trial. In this case, however, x does not represent the number of successes (songs by the particular artist) in a fixed number of trials (songs played). Instead, x represents the number of trials until success occurs, and the trials do not have a fixed number. Therefore, the binomial distribution is not appropriate for this problem.
02

Analyze the probability distribution of x

Since the binomial distribution is not appropriate for this problem, let's consider another probability distribution, the geometric distribution. A geometric distribution is appropriate for a random variable that represents the number of trials until the first success (song by the particular artist) occurs. The probability mass function (PMF) of a geometric distribution is given by: \[p(x) = (1-p)^{(x-1)}p\] where p is the probability of success on each trial, which in our case is the probability of selecting a song by the particular artist (\(\frac{8}{100}\)). Now, let's find the required probabilities using the geometric distribution.
03

Find \(p(4)\)

\(p(4)\) represents the probability that the first song by the particular artist is played after exactly 4 songs. Using the PMF of a geometric distribution, we can calculate \(p(4)\) as follows: \[p(4)= (1-\frac{8}{100})^{(4-1)}\frac{8}{100} = (0.92)^3(0.08) \approx 0.0575\]
04

Find \(P(x \leq 4)\)

\(P(x \leq 4)\) represents the probability that the first song by the particular artist is played within the first 4 songs. To calculate this probability, we can sum up the individual probabilities for the events x=1, x=2, x=3, and x=4: \[P(x\leq 4) = p(1) + p(2) + p(3) + p(4)\] Using the PMF of a geometric distribution: \[P(x \leq 4) = (1-p)^{0}p + (1-p)^{1}p + (1-p)^{2}p + (1-p)^{3}p\] Substituting the value of p (\(\frac{8}{100}\)): \[P(x \leq 4) = (1-0.08)^0 (0.08) + (0.92)^1 (0.08) + (0.92)^2 (0.08) + (0.92)^3 (0.08) \approx 0.2819\]
05

Find \(P(x>4)\)

\(P(x>4)\) represents the probability that the first song by the particular artist is played after the first 4 songs. To calculate this probability, we can use the complement rule: \[P(x>4) = 1 - P(x\leq 4)\] Using the previously calculated value for \(P(x \leq 4)\): \[P(x>4) = 1 - 0.2819 \approx 0.7181\]
06

Find \(P(x \geq 4)\)

\(P(x \geq 4)\) represents the probability that the first song by the particular artist is played on or after the 4th song. We can calculate this probability by summing up the individual probabilities for the events \(x=4, x=5, x=6, \dots\). However, we can use the following property to simplify the calculation: \[P(x \geq 4) = P(x\geq 3) - P(x=3)\] Using the PMF of a geometric distribution: \(P(x\geq 3) = 1 - [ p(1) + p(2) ] = 1 - [ (1-p)^0 p + (1-p)^1 p ]\) Substituting the value of p (\(\frac{8}{100}\)): \(P(x\geq 3) = 1 - [(1-0.08)^0 (0.08) + (0.92)^1 (0.08)] = 1 - [0.08 + 0.0752] = 0.8448\) Now, we can calculate \(p(3)\) using the PMF of a geometric distribution: \[p(3) = (1-p)^{(3-1)}p = (0.92)^2(0.08) \approx 0.0673\] Finally, we can find \(P(x \geq 4)\): \[P(x \geq 4) = P(x\geq 3) - P(x=3) = 0.8448 - 0.0673 \approx 0.7775\]
07

Interpret the probabilities and explain the difference

a) \(p(4) \approx 0.0575\): This represents the probability that the first song by the particular artist is played exactly after 3 other songs, as the 4th song. b) \(P(x\leq4) \approx 0.2819\): This denotes the probability that the first song by the particular artist is played within the first 4 songs. c) \(P(x>4) \approx 0.7181\): This probability suggests that the first song by the particular artist is played after the first 4 songs. d) \(P(x \geq 4) \approx 0.7775\): This represents the probability that the first song by the particular artist is played on or after the 4th song. The difference between these probabilities is based on the events they represent. Each probability represents a different way of looking at when the first song by the particular artist will be played. For example, \(P(x\leq4)\) accounts for the number of songs played before the first song by the artist, while \(P(x\geq4)\) accounts for the number of songs played from the fourth song onwards until the first song by the artist is played.

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Key Concepts

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

Probability Distribution
Unraveling the mystery of probability involves exploring various types of distributions, each describing a different scenario or experiment. In the context of our musical playlist, we delve into a specific type of probability distribution that governs the likelihood of events.

A probability distribution assigns a probability to each possible outcome of a random experiment. Think of it like a recipe that tells us how likely different results are when we're dealing with chance. In formal terms, it's a mathematical function that provides the probabilities of occurrence of different possible outcomes in an experiment.

For our MP3 player's playlist with a specific number of songs by a particular artist, the distribution of interest isn't binomial, where outcomes are fixed, but rather geometric, where we look at 'trials until success'. This cardinal difference needs to be appreciated to grasp the core of probability distributions in this scenario.
Probability Mass Function (PMF)
The PMF, or probability mass function, is a function that gives the probability that a discrete random variable is exactly equal to some value. Imagine a PMF as a list that matches each outcome of a random variable with its probability of occurrence.

In our MP3 playlist scenario, the PMF for the geometric distribution is formulated as \(p(x) = (1-p)^{(x-1)}p\), which beautifully captures the essence of our 'wait time' for a specific song. With each skipped song that isn't by our artist, the chances of hitting that tune the next time linger, defined mathematically by that raising of \(1-p\) to the power of \(x-1\).

Using this framework, it lays out how we calculate likelihoods for the exact order or position within our musical journey where we encounter a track by the sought-after artist.
Bernoulli Trials
A fundamental concept in probability theory is the notion of Bernoulli trials. Think about flipping a coin; you might call heads or tails and each flip would be a Bernoulli trial because it has two possible outcomes: success or failure.

In more technical terms, these are independent trials with only two possible outcomes and the same probability of success on each trial. But here's the kicker for our playlist problem: the scenario doesn't fit the Bernoulli framework snugly because we don't have a predefined number of flips or plays. Our goal post, the artist's song, might pop up on the first try or the tenth; thus, we aren't restricted to a 'fixed' number of trials, hence the shift to geometric distribution makes sense.
Geometric Series
The geometric series is an algebraic gem that helps us sum an infinite list of terms that have a constant ratio between successive terms. Why does it matter here? Because it's a cornerstone for understanding the geometric distribution and thus unraveling probabilities in sequences like our playlist.

When calculating probabilities like \(P(x \leq 4)\) in the provided example, we actually summed the first few terms of an infinite geometric series. These series are vital because they describe the cumulative probability distribution in a format that simplifies what could otherwise be a complex, daunting task.

This mathematical concept is not just applicable within the realm of probability but spans across various disciplines, showcasing the interconnected beauty of mathematics. In the realm of our MP3 player, mastering the geometric series provides clarity and efficiency in predicting the moment of musical satisfaction.

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Most popular questions from this chapter

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