Podcast
Play an AI-generated podcast conversation about this lesson
Download our mobile app to listen on the go
Get App
Questions and Answers
Which of the following best describes a discrete random variable?
Which of the following best describes a discrete random variable?
- A random variable that can take on any real value within a given range.
- A random variable that can take on any positive integer value.
- A random variable that can take on only a finite number of values. (correct)
- A random variable that can take on any negative integer value.
What can be determined from a probability mass function?
What can be determined from a probability mass function?
- The cumulative distribution function.
- The mean and variance of a discrete random variable.
- The probability of occurrence of each value of a discrete random variable. (correct)
- The probability of occurrence of each value of a continuous random variable.
What is the purpose of a cumulative distribution function?
What is the purpose of a cumulative distribution function?
- To determine the mean and variance of a discrete random variable.
- To determine the probability of occurrence of each value of a discrete random variable. (correct)
- To determine the probability of occurrence of each value of a continuous random variable.
- To calculate probabilities for specific applications.
What can be calculated using a cumulative distribution function?
What can be calculated using a cumulative distribution function?
Signup and view all the answers
What assumptions are necessary for each discrete probability distribution?
What assumptions are necessary for each discrete probability distribution?
Signup and view all the answers
Flashcards are hidden until you start studying
Study Notes
Discrete Random Variables
- A discrete random variable is a variable that can only take on specific, distinct values
Probability Mass Function (PMF)
- A PMF describes the probability of each distinct value of a discrete random variable
- From a PMF, you can determine:
- The probability of each possible value of the discrete random variable
- The probability of a range of values of the discrete random variable
Cumulative Distribution Function (CDF)
- The purpose of a CDF is to describe the cumulative probability of a discrete random variable
- A CDF describes the probability that a discrete random variable takes on a value less than or equal to a given value
- Using a CDF, you can calculate:
- The probability that a discrete random variable takes on a value within a certain range
- The probability that a discrete random variable takes on a value less than or equal to a certain value
Discrete Probability Distributions
- Each discrete probability distribution has its own set of assumptions that must be met in order to use the distribution
- Examples of discrete probability distributions include the Binomial, Poisson, and Hypergeometric distributions
- Assumptions necessary for each distribution vary, but may include:
- Independence of trials
- Fixed number of trials
- Constant probability of success
- And others, depending on the specific distribution
Studying That Suits You
Use AI to generate personalized quizzes and flashcards to suit your learning preferences.
