Dec 4 - Discrete Probability Distributions

Front

What is a discrete probability distribution?

Back

A discrete probability distribution is a statistical function that describes the likelihood of obtaining the possible values that a discrete random variable can take.

Front

Define a discrete random variable.

Back

A discrete random variable is a variable that can take on a countable number of distinct values, often representing counts or whole numbers.

Front

What is the probability mass function (PMF)?

Back

The probability mass function (PMF) is a function that gives the probability that a discrete random variable is equal to a specific value.

Front

How do you calculate the mean of a discrete probability distribution?

Back

The mean (expected value) is calculated by summing the products of each value of the random variable and its corresponding probability.

Front

What is the variance of a discrete random variable?

Back

Variance measures the spread of a set of values and is calculated as the expected value of the squared deviation of a random variable from its mean.

Front

What is the difference between discrete and continuous random variables?

Back

Discrete random variables can take on a finite or countably infinite number of values, while continuous random variables can take on any value within a given range.

Front

Provide an example of a discrete random variable.

Back

The number of students in a classroom is a discrete random variable because it can only take whole number values.

Front

What is the cumulative distribution function (CDF) for a discrete random variable?

Back

The cumulative distribution function (CDF) gives the probability that a discrete random variable is less than or equal to a certain value.

Front

What is the significance of the total probability in a discrete probability distribution?

Back

The total probability of all possible outcomes in a discrete probability distribution must equal 1.

Front

How do you find the probability of at least k successes in a binomial distribution?

Back

To find the probability of at least k successes, sum the probabilities of getting k, k+1, ..., up to n successes using the binomial probability formula.