AP Statistics Calculators
Essential statistical tools to help you master AP Statistics concepts and excel in your exam
Aligned with AP Statistics Curriculum
AP Exam Weighting by Unit
Unit 1: Exploring One-Variable Data (15-23%)
Tools for analyzing distributions of quantitative and categorical variables.
Mean, Median, Mode
Calculate measures of center for data analysis and comparison.
Range, Variance, Standard Deviation
Calculate measures of spread to understand data variability.
Five-number Summary
Generate key summary statistics including quartiles.
Percentile, Quartile, and IQR
Identify potential outliers and analyze distribution spread.
Z-Score
Standardize data for comparison and identify unusual values.
Histogram
Visualize frequency distributions to analyze shape and spread.
Box Plot
Create box-and-whisker plots to identify outliers and compare distributions.
Stem-and-Leaf Plot
Create visualizations that preserve actual data values.
Dot Plot
Visualize individual data points for small datasets.
Unit 2: Exploring Two-Variable Data (5-7%)
Tools for exploring relationships between paired variables.
Scatter Plot
Visualize relationships between two variables and explore patterns.
Correlation Coefficient
Measure the strength and direction of linear relationships.
Simple Linear Regression
Create linear models to predict relationships between variables.
Residual Plot
Analyze the fit of linear models by examining residuals.
Covariance
Analyze how two variables change together and their relationship.
Unit 3: Collecting Data (12-15%)
Tools for planning and conducting studies, sampling, and experimental design.
Margin of Error
Understand sampling variability and estimate confidence bounds.
Sample Size Calculator
Determine needed sample size for desired precision or power.
Random Number Generator
Generate random samples for simulations and sampling exercises.
Frequency Table
Organize and summarize categorical data from surveys and studies.
Contingency Table
Analyze relationships between categorical variables in surveys.
Unit 4: Probability, Random Variables, and Probability Distributions (10-20%)
Tools for calculating probabilities and working with probability distributions.
Basic Probability
Calculate simple, joint, and conditional probabilities.
Binomial Distribution
Calculate probabilities for fixed-trial binary outcome experiments.
Normal Distribution
Work with the bell curve distribution's probabilities and critical values.
Geometric Distribution
Calculate probabilities for trials until first success.
Expected Value
Calculate the long-run average value of random variables.
Variance of Random Variables
Analyze the spread of probability distributions.
Uniform Distribution
Work with equally likely outcomes across an interval.
Unit 5: Sampling Distributions (7-12%)
Tools for understanding and working with sampling distributions.
Unit 6: Inference for Categorical Data: Proportions (12-15%)
Tools for statistical inference about population proportions.
One Proportion Confidence Interval
Calculate confidence intervals for population proportions.
One Proportion Z-Test
Test hypotheses about a single population proportion.
Two Proportion Z-Test
Compare proportions from two different populations.
Two Proportion Confidence Interval
Calculate confidence intervals for the difference in proportions.
Unit 7: Inference for Quantitative Data: Means (10-18%)
Tools for statistical inference about population means.
One Sample Z-Test
Test hypotheses about a population mean (known σ).
One Sample T-Test
Test hypotheses about a population mean (unknown σ).
Mean Confidence Interval
Calculate confidence intervals for population means.
Two Sample T-Test (Independent)
Compare means of two independent groups.
Two Sample T-Test (Paired)
Compare means of paired or matched observations.
Difference in Means CI
Calculate confidence intervals for difference between means.
Unit 8: Inference for Categorical Data: Chi-Square (2-5%)
Tools for using chi-square tests for categorical data.
Unit 9: Inference for Quantitative Data: Slopes (2-5%)
Tools for inference about linear regression models.