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Questions and Answers
Which of the following describes a discrete probability distribution?
What are the two conditions that a discrete probability distribution must satisfy?
In a binomial distribution, what does the variable 'p' represent?
What is the formula for calculating the variance in a binomial distribution?
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How do you calculate the probability of getting exactly 4 successes in a binomial experiment with 12 trials?
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What is the mean for a binomial distribution given n trials and probability p of success?
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What type of distribution describes successes in trials from a finite population without replacement?
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If a student randomly guesses on a multiple-choice test with 10 questions, what is the probability of answering exactly 6 questions correctly?
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What is the mean number of trials required to achieve r successes in a Negative Binomial Distribution?
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In the context of Negative Binomial Distribution, what does the variance formula represent?
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For a survey with a 9% completion rate, what is the probability of getting the 3rd completed survey on the 10th call?
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What is the probability that the first defective tire will be identified on the 27th inspection, if the defect probability is 2%?
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How is the probability of receiving exactly 8 orders in one day calculated if the average is 12 orders?
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If a startup receives an average of 7 text messages over 3 hours, what is the probability of receiving exactly 9 messages in that same period?
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What is the probability that the third oil strike occurs on the seventh drilled well, given a 20% success rate?
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What is the variance of the number of trials needed to achieve three successful oil strikes when the probability is 20%?
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What does the variable $K$ represent in the probability formula?
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In the provided probability formulas, what does the value $N$ signify?
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If a sample of 10 marbles is drawn from an urn containing 400 red and 600 blue marbles, what is the probability of drawing exactly 3 red marbles using the appropriate formula?
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In a geometric distribution, what does the mean ($ ext{µ}$) equal?
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What is the variance ($ ext{σ}^2$) for a geometric distribution?
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When considering the probability that the 5th car is the first red car on a Tuesday, which type of probability distribution is being utilized?
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If the probability of a tire being defective is 2%, what is the probability that the 5th tire tested is a defect?
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In the given scenario of engineers, what does the variable $x$ signify in the context of the geometric distribution?
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What cumulative geometric distribution formula is used to find the probability that $X$ is less than or equal to $x$?
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To calculate the mean number of tires expected to test until finding the first defective one, which value is used?
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Study Notes
Probability Distributions
- A probability distribution defines possible values of a random variable and their associated probabilities.
- Discrete probability distributions cover values of discrete random variables and their likelihoods.
Conditions for Discrete Probability Distribution
- Probabilities must satisfy (0 ≤ P(X = x) ≤ 1).
- The sum of probabilities must equal (1): (\sum P(X = x) = 1).
Types of Discrete Probability Distributions
-
Binomial Distribution: Deals with two possible outcomes (success or failure).
- Probability formula: (P(X) = \binom{n}{x} p^x q^{n - x})
- Mean: (\mu = np)
- Variance: (\sigma^2 = np(1 - p))
-
Hypergeometric Distribution: Describes number of successes in n trials from a finite population without replacement.
- Probability formula: (P(x) = \frac{\binom{K}{x} \binom{N-K}{n-x}}{\binom{N}{n}})
-
Geometric Distribution: Defines chances of achieving first success in independent and identical trials.
- Probability: (P(X = x) = q^{x - 1} p)
- Cumulative distribution: (P(X ≤ x) = 1 - q^x)
- Mean: (\mu = \frac{1}{p})
- Variance: (\sigma^2 = \frac{q}{p^2})
-
Negative Binomial Distribution: Counts trials until the r-th success.
- Probability formula: (P(X) = \binom{n - 1}{r - 1} p^r q^{n - r})
-
Poisson Distribution: Models the number of events occurring in a fixed interval.
- Probability formula: (P(X = x) = \frac{\mu^x e^{-\mu}}{x!})
Applications of Discrete Probability Distributions
- Binomial Example: Rolling a die 12 times, calculate the probability of rolling a "4" five times, utilizing binomial formulas.
- Hypergeometric Example: Calculate probabilities for students enrolled in a course among a sample size.
- Geometric Example: Finding probabilities related to the sequence of car colors.
Specific Probability Problems
- Doctor and nurse selection scenario: Calculate probability of choosing 4 doctors from a total of 25 names without replacement.
- Marbles example: Find the likelihood of drawing a specific number of red marbles from an urn.
- Tire defect analysis: Determine probabilities of defective vs. non-defective tires.
Velocity of Events in Independence
- Returning probabilities concerning company operations like identifying defects or strikes in geological studies.
Summary Formulas
- Binomial:
- Mean: (\mu = np)
- Variance: (\sigma^2 = np(1-p))
- Negative Binomial:
- Mean: (\mu = \frac{r}{p})
- Variance: (\sigma^2 = \frac{rq}{p^2})
- Geometric:
- Mean: (\mu = \frac{1}{p})
- Variance: (\sigma^2 = \frac{q}{p^2})
- Poisson:
- For ( \mu) events per interval, use formulas for generating probability for x occurrences.
Real-World Applications
- Utilization in various industries like risk assessment, quality control, or service efficiency.
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Description
This quiz explores the concept of discrete probability distributions, focusing on the function that describes possible values of a discrete random variable and their associated probabilities. Test your knowledge on the key characteristics and applications of this fundamental concept in probability theory.