Specification points
College Board AP Statistics statistics
210 points in 5 topics, with College Board's codes and titles.
Codes and titles are read by a script from College Board's published specification and have not been checked by a person. What each point covers is in the specification itself: College Board’s specification.
1 Exploring One-Variable Data and Collecting Data
- 1.1 Introducing Statistics: What Can We Learn from Data?
- 1.1.A Identifying the components of a statistical study
- 1.1.B Setting an investigative question for a study
- 1.2 Variables
- 1.2.A Observational units, variables, parameters and statistics
- 1.2.B Types of variables
- 1.2.C Types of quantitative variables
- 1.3 Tabular Representation and Summary Statistics for One Categorical Variable
- 1.3.A Building one-variable tables for categorical data
- 1.3.B Summary statistics for categorical one-variable tables
- 1.4 Graphical Representations for One Categorical Variable
- 1.4.A Graphing one categorical variable
- 1.4.B Justifying a claim from a categorical graph
- 1.4.C Comparing categorical tables and graphs
- 1.5 Graphical Representations for One Quantitative Variable
- 1.5.A Graphing one quantitative variable
- 1.6 Descriptions for One Quantitative Variable Distributions
- 1.6.A Describing distributions of quantitative data
- 1.6.B Justifying a claim from a quantitative distribution
- 1.7 Summary Statistics for One Quantitative Variable
- 1.7.A Measures of centre and position
- 1.7.B Measures of variability
- 1.7.C Summary statistics in different units of measurement
- 1.7.D Identifying outliers in quantitative data
- 1.7.E Comparing quantitative summary statistics
- 1.7.F Choosing a summary statistic for quantitative data
- 1.8 Graphical Representations of Summary Statistics for One Quantitative Variable
- 1.8.A Graphing summary statistics for one quantitative variable
- 1.8.B Describing distributions from the mean and median
- 1.9 Comparisons of the Distributions for One Quantitative Variable
- 1.9.A Comparing quantitative graphs
- 1.9.B Comparing graphs of summary statistics
- 1.9.C Justifying a claim from several quantitative graphs
- 1.9.D z-scores from population parameters
- 1.9.E Comparing z-scores as relative position
- 1.10 The Investigative Question Revisited and Data Collection
- 1.10.A Components of an investigative question
- 1.10.B Identifying a census
- 1.10.C Identifying an experiment
- 1.10.D Identifying an observational study
- 1.10.E When a study's results can be generalised
- 1.11 Random Sampling
- 1.11.A Identifying a sampling method
- 1.11.B Justifying a sampling method
- 1.12 Potential Problems with Sampling
- 1.12.A Sources of bias in sampling
- 1.13 Experimental Design
- 1.13.A Elements of a well-designed experiment
- 1.13.B Types of experimental design
- 1.13.C Justifying an experimental design
- 1.13.D Justifying conclusions from a well-designed experiment
2 Probability, Random Variables, and Probability Distributions
- 2.1 Tabular and Graphical Representations for the Distributions of Two Categorical Variables
- 2.1.A Comparing tables and graphs of two categorical variables
- 2.1.B Justifying a claim from two categorical distributions
- 2.2 Summary Statistics for Two Categorical Variables
- 2.2.A Summary statistics from two-way tables
- 2.2.B Comparing summary statistics for two categorical variables
- 2.2.C Justifying a claim from two-variable categorical statistics
- 2.3 Estimating Probabilities Using Simulation
- 2.3.A Estimating probabilities by simulation
- 2.4 Introduction to Probability
- 2.4.A Probabilities of events and complements
- 2.5 Mutually Exclusive Events
- 2.5.A Mutually exclusive events and joint probability
- 2.6 Conditional Probability
- 2.6.A Conditional probability
- 2.7 Independent Events and Unions of Events
- 2.7.A Independent events and the union of two events
- 2.8 Introduction to Random Variables and Probability Distributions
- 2.8.A Probability distributions for discrete random variables
- 2.9 Parameters of Random Variables
- 2.9.A Mean and standard deviation of a discrete random variable
- 2.9.B Interpreting the mean and standard deviation of a random variable
- 2.10 The Binomial Distribution
- 2.10.A Deciding whether a variable is binomial
- 2.10.B Mean and standard deviation of a binomial distribution
- 2.10.C Interpreting binomial means, standard deviations and probabilities
- 2.10.D Estimating binomial probabilities by simulation
- 2.10.E Calculating binomial probabilities
- 2.11 The Normal Distribution
- 2.11.A Describing a normal distribution
- 2.11.B Mean and standard deviation of a normal distribution
- 2.11.C The empirical rule for normal distributions
- 2.11.D Probability of a value within a normal interval
- 2.11.E Intervals and areas of a normal distribution
- 2.11.F Comparing measures of relative position
- 2.12 Sampling Distributions and the Central Limit Theorem
- 2.12.A Describing sampling distributions by simulation
3 Inference for Categorical Data: Proportions
- 3.1 Estimators
- 3.1.A Unbiased and biased estimators
- 3.1.B Estimating a population parameter
- 3.2 Sampling Distributions for Sample Proportions
- 3.2.A Sampling distribution of a sample proportion: mean and SD
- 3.2.B Conditions for the sampling distribution of a proportion
- 3.2.C Interpreting the sampling distribution of a sample proportion
- 3.3 Constructing a Confidence Interval for a Population Proportion
- 3.3.A Choosing a confidence interval procedure for a proportion
- 3.3.B Checking conditions for a confidence interval for a proportion
- 3.3.C Confidence interval for a population proportion
- 3.3.D Standard error, margin of error and sample size for proportions
- 3.4 Justifying a Claim Based on a Confidence Interval for a Population Proportion
- 3.4.A Interpreting a confidence interval for a proportion
- 3.4.B Justifying a claim from a confidence interval for a proportion
- 3.4.C Sample size, interval width, confidence level and margin of error
- 3.5 Setting Up a Test for a Population Proportion
- 3.5.A Choosing a test method for a population proportion
- 3.5.B Hypotheses for a population proportion
- 3.5.C Checking conditions for a test of a proportion
- 3.6 p-Values
- 3.6.A Interpreting the p-value of a test of a proportion
- 3.7 Carrying Out a Test for a Population Proportion
- 3.7.A Test statistic and p-value for a population proportion
- 3.7.B Justifying a claim from a test of a proportion
- 3.8 Potential Errors When Performing Tests
- 3.8.A Identifying Type I and Type II errors
- 3.8.B Probability of Type I and Type II errors
- 3.8.C Factors affecting error probabilities in hypothesis tests
- 3.8.D Interpreting Type I and Type II errors
- 3.9 Sampling Distributions for the Difference Between Sample Proportions
- 3.9.A Sampling distribution of a difference in proportions: mean and SD
- 3.9.B Conditions for the sampling distribution of a difference in proportions
- 3.9.C Interpreting the sampling distribution of a difference in proportions
- 3.10 Constructing a Confidence Interval for the Difference Between Two Population Proportions
- 3.10.A Choosing a confidence interval procedure for a difference in proportions
- 3.10.B Checking conditions for an interval for a difference in proportions
- 3.10.C Confidence interval for a difference in two proportions
- 3.10.D Standard error and margin of error for a difference in proportions
- 3.11 Justifying a claim from an interval for a difference in proportions
- 3.11.A Interpreting an interval for a difference in proportions
- 3.11.B Justifying a claim from an interval for a difference in proportions
- 3.12 Setting Up a Test for the Difference Between Two Population Proportions
- 3.12.A Choosing a test for a difference in proportions
- 3.12.B Hypotheses for a difference in proportions
- 3.12.C Checking conditions for a test of a difference in proportions
- 3.13 Carrying Out a Test for the Difference Between Two Population Proportions
- 3.13.A Test statistic and p-value for a difference in proportions
- 3.13.B Interpreting the p-value for a difference in proportions
- 3.13.C Justifying a claim from a test of a difference in proportions
- 3.14 Setting Up a Chi-Square Test for Homogeneity or Independence
- 3.14.A Chi-square distributions
- 3.14.B Choosing a test for two-way tables of categorical data
- 3.14.C Hypotheses for a chi-square test of homogeneity or independence
- 3.14.D Checking conditions for a chi-square test
- 3.15 Carrying Out a Chi-Square Test for Homogeneity or Independence
- 3.15.A Expected counts in two-way tables
- 3.15.B Test statistic and p-value for a chi-square test
- 3.15.C Interpreting the p-value of a chi-square test
- 3.15.D Justifying a claim from a chi-square test
4 Inference for Quantitative Data: Means
- 4.1 Sampling Distributions for Sample Means
- 4.1.A Sampling distribution of a sample mean: mean and SD
- 4.1.B Conditions for the sampling distribution of a sample mean
- 4.1.C Interpreting the sampling distribution of a sample mean
- 4.2 Constructing a Confidence Interval for a Population Mean or Population Mean Difference
- 4.2.A t-distributions
- 4.2.B Choosing a confidence interval procedure for a mean or mean difference
- 4.2.C Checking conditions for an interval for a mean or mean difference
- 4.2.D Confidence interval for a population mean or mean difference
- 4.2.E Standard error, margin of error and sample size for a t-interval
- 4.3 Justifying a claim from an interval for a mean
- 4.3.A Interpreting an interval for a mean or mean difference
- 4.3.B Justifying a claim from an interval for a mean
- 4.3.C Sample size, width, confidence level and margin of error for means
- 4.4 Setting Up a Test for a Population Mean or Population Mean Difference
- 4.4.A Choosing a test for a mean with unknown sigma
- 4.4.B Hypotheses for a mean or mean difference with unknown sigma
- 4.4.C Checking conditions for a test of a mean or mean difference
- 4.5 Carrying Out a Test for a Population Mean or Population Mean Difference
- 4.5.A Test statistic and p-value for a mean or mean difference
- 4.5.B Interpreting the p-value of a test of a mean
- 4.5.C Justifying a claim from a test of a mean
- 4.6 Sampling Distributions for the Difference Between Two Sample Means
- 4.6.A Sampling distribution of a difference in means: mean and SD
- 4.6.B Conditions for the sampling distribution of a difference in means
- 4.6.C Interpreting the sampling distribution of a difference in means
- 4.7 Constructing a Confidence Interval for the Difference Between Two Population Means
- 4.7.A Choosing a confidence interval procedure for a difference in means
- 4.7.B Checking conditions for an interval for a difference in means
- 4.7.C Confidence interval for a difference in two means
- 4.7.D Standard error and margin of error for a difference in means
- 4.8 Justifying a claim from an interval for a difference in means
- 4.8.A Interpreting an interval for a difference in means
- 4.8.B Justifying a claim from an interval for a difference in means
- 4.9 Setting Up a Test for the Difference Between Two Population Means
- 4.9.A Choosing a test for a difference in two means
- 4.9.B Hypotheses for a difference in two means
- 4.9.C Checking conditions for a test of a difference in means
- 4.10 Carrying Out a Test for the Difference Between Two Population Means
- 4.10.A Test statistic and p-value for a difference in means
- 4.10.B Interpreting the p-value for a difference in means
- 4.10.C Justifying a claim from a test of a difference in means
5 Regression Analysis
- 5.1 Graphical Representations Between Two Quantitative Variables
- 5.1.A Drawing scatterplots of two quantitative variables
- 5.1.B Describing a scatterplot
- 5.1.C Justifying a claim from a scatterplot
- 5.2 Correlation
- 5.2.A Interpreting correlation for a linear relationship
- 5.3 Linear Regression Models
- 5.3.A Predicting a response with a linear regression model
- 5.4 Residuals
- 5.4.A Residuals: observed minus predicted values
- 5.4.B Interpreting residuals
- 5.4.C Using residual plots to describe the form of association
- 5.5 Least-Squares Regression
- 5.5.A Coefficients of the least-squares regression line
- 5.5.B Interpreting least-squares regression coefficients