AP Statistics Chapter 6 Quiz

A probability distribution that describes the number of trials required to achieve the first success in a sequence of independent Bernoulli trials. It is characterized by a constant probability of success in each trial.

A probability distribution that describes the number of successes in a fixed number of independent Bernoulli trials. It is characterized by a constant probability of success in each trial.

In a geometric setting, the probability of success is constant for each trial.

In a geometric setting, each trial is independent of the others. The outcome of one trial doesn't affect the outcomes of other trials.

A variable that represents the number of trials needed to achieve the first success in a sequence of independent Bernoulli trials.

A random variable that represents the number of successes in a fixed number of independent Bernoulli trials.

A trial where there are only two possible outcomes - success or failure.

The distribution of a continuous random variable where the probability density function is bell-shaped.

The probability of a household having at least two cell phones is the sum of the probabilities of having 2, 3, 4, or 5 cell phones.

The geometric setting involves repeated trials until success is achieved, while the binomial setting involves a fixed number of trials.

If the probability of success is closer to 1, the distribution will be skewed to the left since it's more likely to succeed early on.

The probability of making at least two out of three free throws is the sum of the probabilities of making exactly two and making all three free throws.

The probability of exactly k successes in n independent trials, each with the same probability of success p, is given by the binomial probability formula.

The expected value of a binomial random variable is the product of the number of trials and the probability of success on each trial.

The expected value of a random variable represents the average value you would expect to obtain if you were to repeat the experiment many times.

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

A binomial distribution describes the probability of getting a certain number of successes in a fixed number of trials, where each trial has only two possible outcomes (success or failure) and the probability of success is constant.

The binomial probability formula calculates the probability of getting exactly k successes in n trials, where the probability of success on each trial is p. The formula uses combinations to account for different orderings.

The expected value of a geometric distribution represents the average number of trials needed to get the first success. It's the reciprocal of the probability of success on a single trial.

A geometric distribution describes the probability of the number of trials it takes to get the first success in a sequence of independent trials, where each trial has only two possible outcomes (success or failure) and the probability of success is constant.

The mean of a random variable is its expected value. It measures the central tendency of the distribution.

The standard deviation of a random variable measures the spread or variability of the distribution around its mean. A larger standard deviation indicates greater spread.

The probability of event A occurring given that event B has already occurred.

A way to organize all possible values a random variable can take and their corresponding probabilities.

The average value of a random variable, calculated by weighting each possible value by its probability.

The spread or variability of a random variable, calculated as the square root of the variance.

A random variable that can only take on a finite number of values or a countably infinite number of values.

The probability of a specific number of successes 'x' in 'n' trials, given a probability of success 'p' on each trial.

The distribution of sample statistics obtained by repeatedly drawing samples from a population.

The process of using sample data to draw conclusions about a population.

A standardized measure of how far a sample statistic deviates from a population parameter.

A range of values that is likely to contain the population parameter.

A statistical test used to compare observed frequencies with expected frequencies.

The average of all possible sample proportions (p̂) from a population. It's represented by μp̂ and equals the population proportion (p).

Measures the spread or variability of sample proportions around the population proportion (p). It's calculated as √[p(1-p)/n], where n is the sample size.

The sampling distribution of the difference between two sample proportions (p̂1 - p̂2) when the population proportions (p1 and p2) are assumed to be equal. It's centered around zero, indicating no difference between the populations.

The sampling distribution of the individual sample mean (X̄) for a given population. It's centered around the population mean (μ) and its shape depends on the distribution of the population.

The standard deviation of the sampling distribution of means, representing the spread of sample means around the population mean. It's calculated as σ/√n, where σ is the population standard deviation and n is the sample size.

The sampling distribution of the difference between two sample means (X̄1 - X̄2). It's centered around the difference between the population means (μ1 - μ2) and its spread depends on the population variances and sample sizes.

The sampling distribution of the estimated slope coefficient (b) in a simple linear regression model. It's centered around the true population slope (β) and its spread depends on the sample size and variability of the independent variable.

The standard deviation of the sampling distribution of the estimated slope coefficient (b), representing the spread of slope estimates around the true population slope (β). It's calculated as s/(√n * sx) where s is the standard error of the regression, n is the sample size, and sx is the standard deviation of the independent variable.

A probability distribution that describes the entire collection of possible sample proportions (p̂) that could be drawn from a population. It helps us understand the likelihood of observing specific sample proportions based on the population proportion (p) and sample size.

A probability distribution that describes the entire collection of possible sample means (X̄) that could be drawn from a population. It helps us understand the likelihood of observing specific sample means based on the population mean (μ) and sample size.

The standard deviation of the theoretical population, representing the typical deviation of individual observations from the population mean. It's symbolized by 'σ'. In contrast to the standard error, which refers to the spread of sample statistics.

The standard deviation of the sampling distribution of a statistic, representing the variability of the statistic across different samples. It's symbolized by 's' and is calculated differently for different statistics (like proportions and means).

A statistic that measures the amount of variation in the data around the regression line. It's a measure of the scatter of the data points about the regression line.

The sampling distribution of the difference between two sample proportions (p̂1 - p̂2) when the population proportions (p1 and p2) are not assumed to be equal.

A set of data points used to estimate the parameters of a statistical model.

A hypothetical population encompassing all possible observations of a variable.