Statistics interactive
Read Statistics · Course outline and scope · Subject glossary
Current practice follows the Fall 2026 course. Original captured readings keep their historical numbering, source provenance, answer notices and identified gaps.
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Current Fall 2026 response workspaces (45)
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- Introducing Statistics: Questions, Samples, and Context · Worked answers and rubrics
5 Fall 2026 practice tasks in the lesson; separate worked answers and rubrics. Browser printing includes lesson context.
- Variables: What Does Each Record Describe? · Worked answers and rubrics
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- One Categorical Variable: Tables and Summaries · Worked answers and rubrics
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- One Categorical Variable: Bar and Pie Charts · Worked answers and rubrics
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- One Quantitative Variable: Histograms, Stems, and Dots · Worked answers and rubrics
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- Describe a Quantitative Distribution in Context · Worked answers and rubrics
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- Summary Statistics: Calculate and Choose · Worked answers and rubrics
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- Five-Number Summaries and Boxplots · Worked answers and rubrics
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- Compare Quantitative Distributions and Relative Position · Worked answers and rubrics
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- Investigative Questions and Data Collection · Worked answers and rubrics
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- Random Sampling: Identify and Justify the Method · Worked answers and rubrics
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- Bias: Identify What Can Go Wrong · Worked answers and rubrics
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- Experimental Design: Make the Comparison Fair · Worked answers and rubrics
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- Scatterplots: Preserve, Describe, and Defend the Pattern · Worked answers and rubrics
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- Correlation: Linear Strength, Direction, and Limits · Worked answers and rubrics
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- Linear Predictions: Substitute, Interpret, and Check the Range · Worked answers and rubrics
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- Residuals: Signed Differences and Model Form · Worked answers and rubrics
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- Least Squares: Calculate the Fit and Interpret Its Coefficients · Worked answers and rubrics
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- 2.1 Seeing Relationships Between Categorical Variables · Worked answers and rubrics
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- 2.2 Joint, Marginal and Conditional Relative Frequencies · Worked answers and rubrics
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- 2.3 Chance Models and Simulation · Worked answers and rubrics
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- 2.4 Events, Sample Spaces and Complements · Worked answers and rubrics
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- 2.5 Joint Probability and Disjoint Events · Worked answers and rubrics
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- 2.6 Conditional Probability and Multiplication · Worked answers and rubrics
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- 2.7 Independence and the Union of Events · Worked answers and rubrics
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- 2.8 Constructing Discrete and Cumulative Distributions · Worked answers and rubrics
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- 2.9 Mean and Spread of a Discrete Distribution · Worked answers and rubrics
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- 2.10 Binomial Counts: Model, Calculate and Interpret · Worked answers and rubrics
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- 2.11 Normal Models: Areas, Cutoffs and Relative Position · Worked answers and rubrics
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- 2.12 Sampling and Randomization Distributions · Worked answers and rubrics
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- 3.1 Point Estimates and Unbiased Estimators · Worked answers and rubrics
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- 3.2 Sampling Distributions for Sample Proportions · Worked answers and rubrics
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- 3.9 Differences Between Sample Proportions · Worked answers and rubrics
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- 4.1 Sampling Distributions for Sample Means · Worked answers and rubrics
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- 4.6 Differences Between Two Sample Means · Worked answers and rubrics
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- Estimating a Proportion: Confidence, Precision and a Population Claim · Worked answers and rubrics
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- Testing a Proportion: What Would Happen Under the Null? · Worked answers and rubrics
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- Errors and Power: Detecting a Difference Without Claiming Certainty · Worked answers and rubrics
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- Two Proportions: Estimate Separately, Test a Common Null · Worked answers and rubrics
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- Two-Way Tables: Homogeneity and Independence · Worked answers and rubrics
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- One Mean and Paired Differences: A Complete t Investigation · Worked answers and rubrics
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- Two Independent Means: Add Variances, Keep the Populations Clear · Worked answers and rubrics
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- Choose the Inference Method from the Question and the Design · Worked answers and rubrics
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- Fall 2026 Rehearsal — Section I: Multiple Choice · Worked answers and rubrics
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- Fall 2026 Rehearsal — Section II: Free Response · Worked answers and rubrics
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Original captured galleries (53)
Galleries preserve captured images, original frame order and capture provenance.
- Gallery 1.1.1 Joint and Marginal Distribution
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- Gallery 1.1.2 Conditional Frequencies and Distributions
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- Gallery 1.2.1 Example 1.2.3
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- Gallery 1.3.1 Boxplot Symmetries
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- Gallery 1.3.2 Example 1.3.1 (5-Number Summary and Graphing Boxplots on TI-83/84)
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- Gallery 1.5.1 Scatterplots and Correlations
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- Gallery 1.5.2 Example 1.5.1 (Creating Scatterplots and Least-Squares Regression Lines on TI-83/84)
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- Gallery 1.5.3 Example 1.5.1 (Performing Linear Regression and Two-Variable Summary Statistics on TI 83/84)
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- Gallery 1.6.1 Example 1.6.1 (Creating Residual Plots and Finding Standard Deviation of Residuals on TI 83-84)
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- Gallery 1.7.1 Example 1.7.1 (Performing Exponential and Power Regression on TI-83/84)
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- Gallery 1.7.2 Example 1.7.1
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- Interactive 2.1.1 Example 2.1.1
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- Interactive 2.2.1 Factors and Levels in an Experiment
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- Interactive 2.2.2 The Importance of Eliminating Lurking Variables
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- Gallery 2.2.1 More Examples of Eliminating Lurking Variables
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- Gallery 2.2.2 Steps in Different Types of Experimental Designs
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- Interactive 2.3.1 Detailed Breakdown of Figure 2.3.1
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- Gallery 3.1.1 Example 3.1.3
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- Gallery 3.2.1 Venn Diagrams
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- Gallery 3.3.1 Example 3.3.1
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- Gallery 3.3.2 Example 3.3.1 (Calculating binomial probabilities on TI-83/84)
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- Gallery 3.3.3 Example 3.3.2 (Calculating geometric probabilities on TI-83/84)
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- Gallery 3.3.4 Example 3.3.1
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- Gallery 3.4.1 Distribution Symmetry
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- Gallery 3.4.2 z-table
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- Gallery 3.4.3 Example 3.4.1
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- Gallery 3.5.1 Example 3.5.1/3.5.2 (Probability Distributions of Random Variables)
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- Gallery 3.6.1 Effects of Population Parameter, Sample Size, and Number of Samples Have on Sampling Distribution of p̂
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- Gallery 3.7.1 How a Sampling Distribution of the Sample Mean is Formed
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- Gallery 3.7.2 Application of the Central Limit Theorem
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- Gallery 4.1.1 Bias and Variability of Point Estimators
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- Gallery 4.1.2 Example 4.1.1 (One-Sample z Interval on TI 83/84)
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- Gallery 4.2.1 Different Ways to Change Statistical Power
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- Gallery 4.3.1 One and Two-sided Significance Tests for One Proportion
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- Gallery 4.3.2 Example 4.2.1 (One-sample z Test on TI 83/84)
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- Gallery 4.4.1 One and Two-sided Significance Tests for Two Proportions
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- Gallery 4.4.2 Example 4.4.1 (Two-sample z Test on TI-83/84)
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- Gallery 4.4.3 Example 4.4.1 (Two-sample z Interval on TI 83/84)
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- Gallery 4.5.1 One and two-sided significance tests for one mean
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- Gallery 4.5.2 t-table
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- Gallery 4.5.3 Example 4.5.1 (Checking the normal condition)
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- Gallery 4.5.4 Example 4.5.1 (one-sample t test on TI 83/84)
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- Gallery 4.5.5 Example 4.5.1 (one-sample t interval on TI 83/84)
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- Gallery 4.6.1 One and two-sided significance tests for two means
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- Gallery 4.6.2 Example 4.6.1 (two-sample t test on TI 83/84)
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- Gallery 4.6.3 Example 4.6.1 (two-sample t interval on TI 83/84)
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- Gallery 4.7.1 One and two-sided significance tests for regression line
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- Gallery 4.7.2 Example 4.7.1 (Checking various conditions)
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- Gallery 4.7.3 Example 4.7.1 (t-test for the slope on TI 83/84)
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- Gallery 4.8.1 Chi-square table
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- Gallery 4.8.2 Example 4.8.1 (one-way table chi-square test on TI-83/84)
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- Gallery 4.8.3 Example 4.8.2 (two-way table chi-square test on TI-83/84)
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- Gallery 4.8.4 Example 4.8.2 (two-way table chi-square components on TI-83/84)
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