AP Statistics Final Exam Review

Completely Randomized Design

An experimental design where all experimental units are randomly assigned treatments.

Randomized Block Design

An experimental design in which the experimental units are placed into homogeneous groups. The random assignment of the units to the treatments is carried out separately within each block.

Matched Pairs Design

An experimental design in which each subject receives both treatments in a random order or subjects are paired based upon closeness of a characteristic and one subject in each pair receives each treatment, determined at random.

Stratified Random Sample

A sampling method that begins by classifying the population into groups of similar individuals and then choosing a separate SRS in each group to get the sample.

Cluster Sample

A sampling method that begins by classifying the population into groups of individuals that are located near each other and then choosing an SRS of the groups.

Simple Random Sample SRS

A sampling method that is chosen in such a way that every group of n individuals has an equal chance to be selected as the sample.

Convenience Sample

A biased sampling method in which the individuals from the population are chosen because they are easy to reach.

Voluntary Response Sample

A biased sampling method that gathers a sample that consists of people who choose themselves by responding to a general invitation.

Distribution of a Variable

This indicates the values that a variable can take on and how often it takes these values.

Two-Way Table

This display organizes data about two categorical variables measured for the same set of individuals.

Shape, Center, Spread, Outliers (SOCS)

This is what you describe when you are asked to explain/describe the distribution of a quantitative variable.

Dotplot

A graphical display for quantitative data that shows the individual values on a number line.

Stemplot

A graphical display for quantitative data that separates each observation into a stem and one-digit leaf.

Histogram

A graphical display for quantitative data that plots the counts (frequencies) or percents (relative frequencies) of values into equal-width classes.

Outlier Rule

More than 1.5(IQR) above the third quartile or below the first quartile.

Standard Deviation

Typical distance of the values in a distribution from the mean. It is found by finding an average of the squared deviations and then taking the square root.

Standardized Score

This value indicates how many standard deviations an observation lies above or below the distribution mean.

68-95-99.7 Rule

This states what percent of observations fall within one, two, and three standard deviations of the mean in a Normal distribution.

Scatterplot

A graphical display that shows the relationship between two quantitative variables measured on the same individuals.

Form, Strength, and Direction

This is what you describe when examining the relationship between two quantitative variables.

Correlation Coefficient

This value measures the strength and direction of the linear relationship between two quantitative variables.

Coefficient of Determination

This is the percentage of the variation in response variable, y, that is accounted for by the least-squares regression line relating y to the explanatory variable, x.

Least-Squares Regression Line (LSRL) "SEb"

This value measures how far the estimated slope will be from the true slope, on average.

Least-Squares Regression Line (LSRL) "s"

This value measures the typical distance between the actual y-values and their predicted y-values. This is the standard deviation of the residuals.

Residual

This value measures the difference between the actual (observed) y-value in a scatterplot and the y-value that is predicted from the least-squares regression line.

Extrapolation

Using a least-squares regression line to predict values far outside the domain of the explanatory variable.

Expected Value

The long-run average outcome of a random phenomenon carried out a very large number of times.

Simulation

An imitation of chance behavior, most often carried out with random numbers.

Mutually Exclusive

Two events that have no outcomes in common and so can never occur together.

Conditional Probability

The likelihood that one event happens given that another event has already occurred.

Independent Events

The occurrence of one event does not change the probability that the other event will happen.

Discrete Random Variable

A random variable that has a fixed set of possible values with gaps between them.

Continuous Random Variable

A random variable that takes all the values in some interval of numbers.

Binomial Setting

This consists of n independent trials of the same chance process, each resulting in a success or a failure, with the probability of success p on each trial.

Geometric Setting

This consists of repeated trials of the same chance process in which the probability p of success is the same on each trial, and the goal is to count the number of trials it takes to get one success.

Central Limit Theorem CLT

This states that when n is sufficiently large, the sampling distribution of the sample mean is approximately Normal.

Confidence Level C

This value gives the overall success rate of the method for calculating the confidence interval.

Margin of Error

This value gets smaller as the confidence level decreases and the sample size increases.

P-value

The probability, assuming the null is true, that the statistic would take a value as extreme or more extreme than the one actually observed, in the direction specified by the alternative hypothesis.

Type I Error

This occurs when we reject the null hypothesis when it is actually true.

Type II Error

This occurs when we fail to reject the null hypothesis when the alternative is true.

Power of a Test

This is the probability that the test will reject the null hypothesis when the alternative is true.