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Kent's Tents has four red tents and three green tents in stock. Karin selects four of them at random. Let \(X\) be the number of red tents she selects. Give the probability distribution and find \(P(X \geq 2)\).

Short Answer

Expert verified
The probability distribution of the random variable \(X\) is as follows: \(P(X=0) = 0\) \(P(X=1) = \frac{4}{35}\) \(P(X=2) = \frac{18}{35}\) \(P(X=3) = \frac{12}{35}\) \(P(X=4) = \frac{1}{35}\) The expected number of red tents selected by Karin is \(\frac{80}{35}\).

Step by step solution

01

Before finding the probability distribution, we need to determine the possible values that the random variable \(X\) can take. Since \(X\) represents the number of red tents selected by Karin, its possible values are 0, 1, 2, 3, or 4. #Step 2: Calculate the probability for each possible value of X#

For each value of \(X\), we need to calculate the probability. We will do this by finding the different combinations of selecting red and green tents and dividing them by the total number of ways to select 4 tents from the 7. \(P(X=0)\): Karin selects no red tents, meaning she selects all green tents. There are \(\binom{4}{0}\binom{3}{4}\) ways to select 0 red tents and 4 green tents. The total number of possible selections is \(\binom{7}{4}\). Therefore, the probability is: \[P(X=0) = \frac{\binom{4}{0}\binom{3}{4}}{\binom{7}{4}} = 0\] \(P(X=1)\): Karin selects 1 red tent and 3 green tents. There are \(\binom{4}{1}\binom{3}{3}\) ways to select 1 red tent and 3 green tents. Therefore, the probability is: \[P(X=1) = \frac{\binom{4}{1}\binom{3}{3}}{\binom{7}{4}} = \frac{4}{35}\] \(P(X=2)\): Karin selects 2 red tents and 2 green tents. There are \(\binom{4}{2}\binom{3}{2}\) ways to select 2 red tents and 2 green tents. Therefore, the probability is: \[P(X=2) = \frac{\binom{4}{2}\binom{3}{2}}{\binom{7}{4}} = \frac{18}{35}\] \(P(X=3)\): Karin selects 3 red tents and 1 green tent. There are \(\binom{4}{3}\binom{3}{1}\) ways to select 3 red tents and 1 green tent. Therefore, the probability is: \[P(X=3) = \frac{\binom{4}{3}\binom{3}{1}}{\binom{7}{4}} = \frac{12}{35}\] \(P(X=4)\): Karin selects all 4 red tents. There are \(\binom{4}{4}\binom{3}{0}\) ways to select 4 red tents. Therefore, the probability is: \[P(X=4) = \frac{\binom{4}{4}\binom{3}{0}}{\binom{7}{4}} = \frac{1}{35}\] #Step 3: Write the probability distribution of X#
02

The probability distribution of the random variable \(X\) is given by: \(P(X=0) = 0\) \(P(X=1) = \frac{4}{35}\) \(P(X=2) = \frac{18}{35}\) \(P(X=3) = \frac{12}{35}\) \(P(X=4) = \frac{1}{35}\) #Step 4: Calculate the expected number of red tents selected (E[X])#

To find the expected number of red tents selected, we multiply the value of each \(X\) by its corresponding probability and sum the results: E[X] = 0 * P(X=0) + 1 * P(X=1) + 2 * P(X=2) + 3 * P(X=3) + 4 * P(X=4) E[X] = 0 * 0 + 1 * \(\frac{4}{35}\) + 2 * \(\frac{18}{35}\) + 3 * \(\frac{12}{35}\) + 4 * \(\frac{1}{35}\) E[X] = \(\frac{4 + 36 + 36 + 4}{35}\) E[X] = \(\frac{80}{35}\) So, on average, the expected number of red tents selected by Karin is \(\frac{80}{35}\).

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Key Concepts

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

Combinatorics
Combinatorics is a powerful tool in probability and mathematics that helps us determine the number of possible ways to arrange or select items from a set. When solving problems like Karin's tent selection, understanding combinatorics allows us to count the different ways to choose tents without listing every possibility.

In this exercise, Karin has four red tents and three green tents in stock, making a total of seven tents. When she selects four tents, we are interested in the different combinations of these tents. Combinatorics introduces the concept of combinations, often denoted as \(\binom{n}{r}\), which represents the number of ways to choose \(r\) items from \(n\) items without regard to order. For instance, the expression \(\binom{7}{4}\) calculates the total ways to choose four tents from seven.

The combinations are calculated using the formula:
  • \(\binom{n}{r} = \frac{n!}{r!(n-r)!}\)
Here, the "!" symbol represents a factorial, which means multiplying all positive integers up to that number. Different values of \(X\), the count of red tents selected, are calculated using combinations of selecting red tents and the complement of green tents. This technique simplifies the calculation of probabilities.
Expected Value
The concept of expected value is quite helpful in understanding the average outcome we can expect in a random process. For instance, when Karin selects tents, she is interested in knowing how many red tents she is likely to pick on average, without making selections multiple times.

Expected value in probability helps us find this "average" outcome when dealing with random variables like \(X\). It is calculated as the weighted average of all potential values that the variable can take. Each outcome value \(x_i\) is multiplied by its probability \(P(X=x_i)\), then summed across all possible outcomes:
  • \(E[X] = \sum_{i}(x_i \cdot P(X=x_i))\)
In Karin's problem, the expected value of \(X\) (number of red tents selected) is computed by calculating:
  • \(0 \cdot P(X=0) + 1 \cdot P(X=1) + 2 \cdot P(X=2) + 3 \cdot P(X=3) + 4 \cdot P(X=4)\)
These calculations use the probabilities from the probability distribution. Essentially, the expected value tells us that, if the selection process was repeated many times, the average number of red tents selected approaches this calculated number.
Discrete Random Variable
In probability theory, a discrete random variable is a variable that can take on a finite or countably infinite number of distinct values. It is important when we deal with outcomes that are not continuous and can be counted individually. Karin's selection process involves a discrete random variable, denoting the count of red tents selected from her choices.

The discrete random variable \(X\), in this context, represents the possible outcomes of red tents selected by Karin, which are 0, 1, 2, 3, or 4. For each possible value of \(X\), there is a corresponding probability that can be determined using combinatorial methods. The collection of these probabilities is known as the probability distribution of \(X\).

The probability distribution for a discrete random variable assigns a probability to each possible value that the variable can take, ensuring the sum of all probabilities equals one. It is expressed mathematically as:
  • \(P(X=x)\) for all values \(x\) in the set of possible outcomes.
In summary, discrete random variables like \(X\) in the problem help us understand and compute probabilities of distinct, well-defined events or counts, enhancing our grasp of the situation and expected outcomes.

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