A.P. Statistics Chapter 6 - 2024-2025

Front

What is a continuous random variable?

Back

A continuous random variable is a variable that can take on an infinite number of values within a given range. An example is the time it takes for a student to complete an exam.

Front

What is a discrete random variable?

Back

A discrete random variable is a variable that can take on a finite number of values. An example is the number of cheeseburgers a fast-food restaurant serves each day.

Front

What is the expected value in probability?

Back

The expected value is the average outcome of a random variable, calculated by multiplying each possible outcome by its probability and summing the results.

Front

How do you calculate the standard deviation of independent random variables?

Back

To calculate the standard deviation of independent random variables, square the standard deviations of each variable, sum them, and then take the square root of the result.

Front

What is the probability of getting at least 13 heads when flipping a coin 20 times?

Back

The probability of getting at least 13 heads when flipping a coin 20 times is approximately 0.1316.

Front

What is the formula for calculating the expected value of a game?

Back

The expected value (EV) is calculated as EV = Σ (Probability of outcome × Value of outcome) for all possible outcomes.

Front

What is the difference between a population and a sample in statistics?

Back

A population includes all members of a specified group, while a sample is a subset of the population used to represent the group.

Front

What is a normal distribution?

Back

A normal distribution is a probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean.

Front

What does it mean for two variables to be independent?

Back

Two variables are independent if the occurrence of one does not affect the probability of the occurrence of the other.

Front

What is the central limit theorem?

Back

The central limit theorem states that the distribution of the sample means approaches a normal distribution as the sample size increases, regardless of the shape of the population distribution.