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Two cards are drawn from a shuffled deck. What is the probability that both are red? If ar least one is red, what is the probability that both are red? If one is a red ace, what is the probability that both are red?

Short Answer

Expert verified
The probability that both cards are red is \( \frac{25}{102} \). Given at least one red card, the probability is \( \frac{25}{77} \). Given one is a red ace, the probability is \( \frac{25}{51} \).

Step by step solution

01

- Calculate the probability that both cards are red

First, calculate the probability of drawing one red card. There are 26 red cards (hearts and diamonds) in a standard 52-card deck. Probability = \ \ \[ \frac{26}{52} = \frac{1}{2} \] If the first card is red, then there are 25 red cards left out of the remaining 51 cards.Probability of second card being red = \ \ \[ \frac{25}{51} \] Thus, the probability that both cards are red is:\ \ \[ \frac{1}{2} \times \frac{25}{51} = \frac{25}{102} \]
02

- Calculate the probability that both are red given at least one is red

Use conditional probability. Let A be the event that both are red, and B be the event that at least one is red. We need to find P(A|B).Using the formula for conditional probability, \ \ \[ P(A|B) = \frac{P(A\cap B)}{P(B)} \] Since A is a subset of B, \ \ \[ P(A \cap B) = P(A) = \frac{25}{102} \] To find P(B), use complementary probability:Probability of no red cards (both black) = \ \ \[ \frac{26}{52} \times \frac{25}{51} = \frac{25}{102} \] Thus, \ \ \ \ \[ P(B) = 1 - \frac{25}{102} = \frac{77}{102} \] Then, \ \ \[ P(A|B) = \frac{\frac{25}{102}}{\frac{77}{102}} = \frac{25}{77} \]
03

- Calculate the probability that both are red given one is a red ace

There are 2 red aces in the deck. If one card is a red ace, there are 25 remaining red cards out of 51 cards since one red ace is already drawn. Thus, the probability that the other card is red = \ \ \[ \frac{25}{51} \]

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Key Concepts

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

conditional probability
Conditional probability is a measure of the probability of an event occurring given that another event has already occurred. It's denoted as P(A|B), meaning the probability of event A occurring given that event B has occurred. To calculate it, you use the formula: \[ P(A|B) = \frac{P(A \text{ and } B)}{P(B)} \] This concept helps in narrowing down the probability space by focusing only on specific known conditions.
For instance, in our exercise involving two cards: if you know at least one is red, the probability that both are red is simplified because you don't consider cases where both cards are black anymore. Calculating this involves understanding that event B (at least one red) changes the original sample space.
card probability
Card probability refers to the chances of drawing certain cards from a deck. A standard deck has 52 cards with 26 red cards (hearts, diamonds) and 26 black cards (spades, clubs).
In our problem, we first calculate the probability of drawing a red card. This is straightforward: \[ \frac{26}{52} = \frac{1}{2} \]
Once one red card is drawn, the deck has 51 remaining cards, 25 of which are red. So, the probability of drawing another red card: \[ \frac{25}{51} \]
This basic understanding of card distribution and subsequent changes in probabilities is key to solving more complex problems.
combinatorial probability
Combinatorial probability involves calculating probabilities where different combinations of events must be considered. It's useful when dealing with scenarios where order does not matter.
For example, in our exercise, after calculating the probability of both cards being red, we consider the combinations where at least one is red. This requires:
  • First, finding the probability of at least one red card, which is easier by determining the complementary event (no red cards).
  • Next, subtracting this probability from 1 to get the desired probability.
The key is to systematically consider all possible combinations and use the necessary formulas to calculate the probabilities of interest.
step-by-step solution
Here’s a breakdown of the step-by-step solution process for the given problem:

Step 1: Probability both cards are red:
Calculate the probability of first red card: \[ \frac{26}{52} = \frac{1}{2} \] Then, the second red card, given the first was red: \[ \frac{25}{51} \] The combined probability is: \[ \frac{1}{2} \times \frac{25}{51} = \frac{25}{102} \]

Step 2: Probability both are red given at least one is red:
Use conditional probability: \[ P(A|B) = \frac{P(A \text{ and } B)}{P(B)} \] Since A is within B: \[ P(A \text{ and } B) = P(A) = \frac{25}{102} \] Calculate P(B) as: \[ 1 - P(\text{no red cards}) = 1 - \frac{25}{102} = \frac{77}{102} \] Then: \[ P(A|B) = \frac{\frac{25}{102}}{\frac{77}{102}} = \frac{25}{77} \]

Step 3: Probability both are red given one is a red ace:
With one red ace drawn: \[ \frac{25}{51} \]These steps simplify complex problems into manageable calculations.

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Most popular questions from this chapter

Using the normal approximation to the binomial distribution, and tables [or calculator for \(\phi(t)\) ], find the approximate probability of each of the following: Find the probabilities for a normally distributed random variable to differ by more than \(\sigma\), \(2 \sigma, 3 \sigma, 4 \sigma\), from its mean value. Your answers should satisfy Chebyshev's inequality (for example, the probability of a deviation of more than \(2 \sigma\) is less than \(\frac{1}{4}\) ). You will find, however, that for the normal distribution, the probabilities are actually much smaller than Chebyshev's inequality requires.

Suppose it is known that \(1 \%\) of the population have a certain kind of cancer. It is also known that a test for this kind of cancer is positive in \(99 \%\) of the people who have it but is also positive in \(2 \%\) of the people who do not have it. What is the probability that a person who tests positive has cancer of this type?

Using the normal approximation to the binomial distribution, and tables [or calculator for \(\phi(t)\) ], find the approximate probability of each of the following: Between 195 and 205 tails in 400 tosses of a coin.

Using the normal approximation to the binomial distribution, and tables [or calculator for \(\phi(t)\) ], find the approximate probability of each of the following: Exactly 21 successes in 100 Bernoulli trials with probability \(\frac{1}{5}\) of saccess.

Show that the expectation of the sum of two random variables defined over the same sample space is the sum of the expectations. Hint: Let \(p_{1}, p_{2}, p_{3}, \cdots, P_{*}\) be the probabilitics associated with the \(n\) sample points; let \(x_{1}, x_{2}, \cdots, x_{n}\), and \(y_{1}, y_{2}, \cdots, y_{n}\), be the values of the random variables \(x\) and \(y\) for the \(n\) sample points. Write out \(E(x), E(y)\), and \(E(x+y)\).

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