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In a binomial situation, \(n=5\) and \(\pi=.40 .\) Determine the probabilities of the following events using the binomial formula. a. \(x=1\) b. \(x=2\)

Short Answer

Expert verified
a. 0.2592; b. 0.3456

Step by step solution

01

Understand the Binomial Formula

The binomial probability formula is used to find the probability of achieving exactly \( x \) successes in \( n \) trials with a success probability of \( \pi \) on each trial. The formula is given by: \[ P(X = x) = \binom{n}{x} \pi^x (1-\pi)^{n-x} \] where \( \binom{n}{x} \) is the binomial coefficient calculated as \( \frac{n!}{x!(n-x)!} \).
02

Calculate for x=1

Substitute \( x = 1 \), \( n = 5 \), and \( \pi = 0.4 \) into the binomial formula: \[ P(X = 1) = \binom{5}{1} (0.4)^1 (0.6)^4 \]Calculate the binomial coefficient \( \binom{5}{1} = 5 \). Thus: \[ P(X = 1) = 5 \times 0.4 \times 0.6^4 \]Calculate \( 0.6^4 = 0.1296 \) and substitute it back: \[ P(X = 1) = 5 \times 0.4 \times 0.1296 = 0.2592 \]
03

Calculate for x=2

Similarly, substitute \( x = 2 \), \( n = 5 \), and \( \pi = 0.4 \) into the binomial formula: \[ P(X = 2) = \binom{5}{2} (0.4)^2 (0.6)^3 \]Calculate the binomial coefficient \( \binom{5}{2} = 10 \). Thus: \[ P(X = 2) = 10 \times 0.16 \times 0.216 \] Compute the values: \( 0.4^2 = 0.16 \) and \( 0.6^3 = 0.216 \). Substitute these back to get: \[ P(X = 2) = 10 \times 0.16 \times 0.216 = 0.3456 \]

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Key Concepts

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

Probability Calculation
Probability calculations allow us to measure the likelihood of an event happening. In the context of binomial distribution, we're often interested in the probability of a certain number of successes in repeated trials. This involves understanding how likely it is for a particular outcome to occur.

To do this in a binomial experiment, you start by identifying the probability of success (\( \pi \)) and consider the total number of trials (\( n \)). The probability of achieving exactly \( x \) successes is calculated using the binomial formula.

Remember, a probability value lies between 0 and 1, where 0 indicates impossibility and 1 indicates certainty. This calculation helps us quantify specific outcomes, like determining the probability of getting exactly 2 heads when tossing a coin 5 times. It's a valuable tool in decision-making processes by providing a mathematical basis for predictions.
Binomial Formula
The binomial formula is a powerful tool used in statistics to find the probability of \( x \) successes in \( n \) trials. This formula is especially handy when dealing with binary outcomes—outcomes that can be classified into two categories such as success or failure. For the given probability scenario, the formula is written as: \[P(X = x) = \binom{n}{x} \pi^x (1-\pi)^{n-x} \] where:
  • \( \binom{n}{x} \) is the binomial coefficient
  • \( \pi \) is the probability of success on a single trial
  • \( (1-\pi) \) is the probability of failure
By substituting the known values into the formula, you can calculate the probability of achieving a predefined number of successes in a series of trials. It's important to plug in accurate values of \( n \) and \( \pi \) to obtain precise results. The beauty of the binomial formula lies in its ability to adapt to various probability scenarios, making it a cornerstone in probability theory.
Binomial Coefficient
The binomial coefficient is a fundamental part of calculating probabilities in a binomial distribution. Often referred to as "combinations," this coefficient tells us the number of ways to choose \( x \) successes from \( n \) trials. The mathematical notation for binomial coefficient is\( \binom{n}{x} \), and it is computed using the formula:
\[\binom{n}{x} = \frac{n!}{x!(n-x)!}\]
where \( n! \) represents factorial of \( n \), meaning you multiply \( n \) by every whole number below it down to 1.

For instance, when calculating \( \binom{5}{2} \), you would compute as follows:
\[\binom{5}{2} = \frac{5 \times 4}{2 \times 1} = 10 \]
This means there are 10 possible ways to choose 2 successes out of 5 trials. The binomial coefficient is essential in determining the respective probabilities of different outcomes in a binomial experiment.

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Most popular questions from this chapter

Recent statistics suggest that \(15 \%\) of those who visit a retail site on the Internet make a purchase. A retailer wished to verify this claim. To do so, she selected a sample of 16 "hits" to her site and found that 4 had actually made a purchase. a. What is the likelihood of exactly four purchases? b. How many purchases should she expect? c. What is the likelihood that four or more "hits" result in a purchase?

An internal study by the Technology Services department at Lahey Electronics revealed company employees receive an average of two non-work-related e-mails per hour. Assume the arrival of these e-mails is approximated by the Poisson distribution. a. What is the probability Linda Lahey, company president, received exactly one non-work-related e-mail between 4 p.m. and 5 p.m. yesterday? b. What is the probability she received five or more non-work-related e-mails during the same period? c. What is the probability she did not receive any non-work-related e-mails during the period?

The U.S. Postal Service reports \(95 \%\) of first-class mail within the same city is delivered within 2 days of the time of mailing. Six letters are randomly sent to different locations. a. What is the probability that all six arrive within 2 days? b. What is the probability that exactly five arrive within 2 days? c. Find the mean number of letters that will arrive within 2 days. d. Compute the variance and standard deviation of the number that will arrive within 2 days.

Automobiles arrive at the Elkhart exit of the Indiana Toll Road at the rate of two per minute. The distribution of arrivals approximates a Poisson distribution. a. What is the probability that no automobiles arrive in a particular minute? b. What is the probability that at least one automobile arrives during a particular minute?

In a recent study, \(90 \%\) of the homes in the United States were found to have large-screen TVs. In a sample of nine homes, what is the probability that: a. All nine have large-screen TVs? b. Less than five have large-screen TVs? c. More than five have large-screen TVs? d. At least seven homes have large-screen TVs?

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