Faculty materials
Lesson keys
Statistics as an Evidence Discipline
Unit 01 · Lesson 01
Cases, Variables, Populations and Samples
Unit 01 · Lesson 02
Categorical, Quantitative and Time-Indexed Data
Unit 01 · Lesson 03
Measurement, Operational Definitions and Validity
Unit 01 · Lesson 04
Data Quality, Missingness and Bias
Unit 01 · Lesson 05
Ethics, Privacy, Consent and Responsible Use
Unit 01 · Lesson 06
Reproducible Statistical Communication
Unit 01 · Lesson 07
Frequency Tables, Dot Plots and Histograms
Unit 02 · Lesson 01
Shape, Modality, Skewness and Unusual Features
Unit 02 · Lesson 02
Mean, Median and Resistant Center
Unit 02 · Lesson 03
Range, Quartiles, IQR and Box Plots
Unit 02 · Lesson 04
Variance and Standard Deviation
Unit 02 · Lesson 05
Standard Scores and Relative Standing
Unit 02 · Lesson 06
Transformations and Comparing Distributions
Unit 02 · Lesson 07
Scatterplots and Association
Unit 03 · Lesson 01
Pearson Correlation and Standardized Association
Unit 03 · Lesson 02
Least-Squares Regression Line
Unit 03 · Lesson 03
Residuals and Model Diagnostics
Unit 03 · Lesson 04
R², Prediction and Extrapolation
Unit 03 · Lesson 05
Transformations, Nonlinearity and Influential Points
Unit 03 · Lesson 06
Categorical Association, Confounding and Causal Claims
Unit 03 · Lesson 07
Sampling Frames and Simple Random Samples
Unit 04 · Lesson 01
Stratified, Cluster and Systematic Sampling
Unit 04 · Lesson 02
Survey Error, Nonresponse and Question Wording
Unit 04 · Lesson 03
Observational Studies and Confounding
Unit 04 · Lesson 04
Randomized Experiments, Control and Replication
Unit 04 · Lesson 05
Blocking, Matching, Blinding and Placebos
Unit 04 · Lesson 06
Simulation-Based Design and Randomization Logic
Unit 04 · Lesson 07
Sample Spaces, Events and Complements
Unit 05 · Lesson 01
Addition Rule and Inclusion–Exclusion
Unit 05 · Lesson 02
Conditional Probability and Tree Diagrams
Unit 05 · Lesson 03
Independence and Multiplication Rules
Unit 05 · Lesson 04
Bayes Theorem and Diagnostic Reasoning
Unit 05 · Lesson 05
Counting, Permutations and Combinations
Unit 05 · Lesson 06
Monte Carlo Simulation and Probability Validation
Unit 05 · Lesson 07
Random Variables and Probability Mass Functions
Unit 06 · Lesson 01
Expected Value as Long-Run Balance
Unit 06 · Lesson 02
Variance, Standard Deviation and Linear Transformations
Unit 06 · Lesson 03
Binomial Distribution
Unit 06 · Lesson 04
Geometric and Waiting-Time Models
Unit 06 · Lesson 05
Poisson Counts and Rate Models
Unit 06 · Lesson 06
Covariance, Sums and Decision Applications
Unit 06 · Lesson 07
Continuous Random Variables and Density Curves
Unit 07 · Lesson 01
Normal Models and the Empirical Rule
Unit 07 · Lesson 02
Standard Normal Probabilities and Percentiles
Unit 07 · Lesson 03
Sampling Distribution of a Proportion
Unit 07 · Lesson 04
Sampling Distribution of a Mean
Unit 07 · Lesson 05
Central Limit Theorem and Normal Approximation
Unit 07 · Lesson 06
t Distributions and Bootstrap Intuition
Unit 07 · Lesson 07
Confidence-Interval Logic and Interpretation
Unit 08 · Lesson 01
One-Proportion Confidence Intervals
Unit 08 · Lesson 02
One-Mean t Intervals
Unit 08 · Lesson 03
Two-Proportion Intervals
Unit 08 · Lesson 04
Two-Mean and Paired Intervals
Unit 08 · Lesson 05
Bootstrap Confidence Intervals
Unit 08 · Lesson 06
Margin of Error, Sample Size and Practical Precision
Unit 08 · Lesson 07
Logic of Significance Tests
Unit 09 · Lesson 01
Errors, Power and Study Planning
Unit 09 · Lesson 02
One-Proportion and One-Mean Tests
Unit 09 · Lesson 03
Two-Proportion and Two-Mean Tests
Unit 09 · Lesson 04
Paired Tests and Within-Subject Designs
Unit 09 · Lesson 05
Randomization and Permutation Tests
Unit 09 · Lesson 06
Practical Significance, Multiplicity and Evidence Quality
Unit 09 · Lesson 07
Chi-Square Goodness of Fit
Unit 10 · Lesson 01
Chi-Square Homogeneity
Unit 10 · Lesson 02
Chi-Square Independence
Unit 10 · Lesson 03
Standardized Residuals and Effect Size
Unit 10 · Lesson 04
One-Way ANOVA and the F Ratio
Unit 10 · Lesson 05
ANOVA Assumptions, Diagnostics and Robustness
Unit 10 · Lesson 06
Multiple Comparisons and Planned Contrasts
Unit 10 · Lesson 07
Multiple Regression and Partial Effects
Unit 11 · Lesson 01
Categorical Predictors and Indicator Coding
Unit 11 · Lesson 02
Interactions and Nonparallel Effects
Unit 11 · Lesson 03
Residuals, Influence and Collinearity
Unit 11 · Lesson 04
Model Selection and Adjusted Fit
Unit 11 · Lesson 05
Prediction Intervals and Cross-Validation
Unit 11 · Lesson 06
Regression Communication and Causal Limits
Unit 11 · Lesson 07
Bootstrap Distributions and Standard Errors
Unit 12 · Lesson 01
Permutation Tests and Exchangeability
Unit 12 · Lesson 02
Sign and Rank-Based Methods
Unit 12 · Lesson 03
Bayesian Updating: Prior, Likelihood and Posterior
Unit 12 · Lesson 04
Bayesian Estimation and Credible Intervals
Unit 12 · Lesson 05
Posterior Predictive Checks and Model Criticism
Unit 12 · Lesson 06
Frequentist and Bayesian Evidence in Context
Unit 12 · Lesson 07
Data Cleaning, Tidy Structure and Metadata
Unit 13 · Lesson 01
Missing Data, Imputation and Sensitivity
Unit 13 · Lesson 02
Visualization Design and Statistical Storytelling
Unit 13 · Lesson 03
Simulation, Monte Carlo and Computational Experiments
Unit 13 · Lesson 04
Reproducible Workflows, Versioning and Audit Trails
Unit 13 · Lesson 05
Fairness, Responsible AI and Statistical Harm
Unit 13 · Lesson 06
Capstone Question, Design and Evidence Plan
Unit 13 · Lesson 07
Technical Report, Oral Defense and Advanced Statistics Bridge
Unit 13 · Lesson 08