This page summarizes what you have to study for the AP. It links back to earlier lectures. There are four main topics for the AP--descriptive statistics, experimental design, probability, and statistical inference.

Be able to define standard deviation, variance, mean, median, quartile, interquartile range, outlier, skewed left, skewed right. Consider a boxplot, a stemplot, a histogram, or a scatterplot. Be able to state whether each of these is useful for assessing whether data are normal or skewed, assessing whether a bivariate relationship is linear or nonlinear, comparing two distributions, or showing outliers.

Know how to do a simulation (such as our simulation of the NCAA brackets). Explain how an experiment is better than an observational study for showing causality. Be able to define and give examples of a lurking variable, a confounding variable, blind experiment, double-blind experiment, and blocking.

Understand the concepts of expected value, Failure Models (how the probability of a kipa falling off depends on the number of clips), Contingency tables and conditional probability, the binomial distribution and the geometric distribution.

Useful practice problems are:

• basic probability
• more basic probability
• expected value, binomial, geometric
• review of expected value, binomial, geometric
• more review
• cumulative review, problems 1-7

Be familiar with working with the normal distribution and with the terminology of statistical inference. (confidence interval, type I and type II error, etc.).

Understand when to use a Z-test, when to use the different t-tests (ordinary, matched-pairs, and two-sample), when to use one-sided or two-sided alternative.

Know when and how to use Chi-square tests. Know the basic formulas for regression, and be able to interpret regression output.

Useful practice problems are:

• working with the normal distribution
• terminology of hypothesis testing
• cumulative review, problems 8-20
• t-tests and z-tests
• more review of hypothesis testing