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Interpret function inputs, domains, composition, piecewise endpoints and an inverse branch before Calculus I. Use algebra skills from C1-00-03. Determine both the formula and the allowed inputs in a two-stage model.
Oct 27, 2026
Repair the algebra used later in Calculus I: factor, combine fractions, rationalize, expand changes and solve a rational inequality while retaining original restrictions. Basic algebra is assumed; no calculus operations are needed.
Oct 27, 2026
Diagnose radian and graph skills, identities and inverse branches, and exponential/logarithmic reasoning. No calculus is assumed. Review each strand separately and use the targeted trigonometry or exponential/logarithm repair feedback.
Oct 27, 2026
Diagnose algebra, functions and connecting skills before Calculus I. Basic numerical literacy is sufficient. Use the feedback to select algebra or function repair; a total score alone does not establish readiness.
Oct 27, 2026
Critique an inferential analysis from observation model to scientific claim. Derive likelihood components and estimators, assess boundary regularity, compare interval procedures through simulation and evaluate Bayesian sensitivity, predictive checks and causal assumptions. Prerequisites: core likelihood, asymptotics, simulation, Bayesian inference and causal reasoning; optional specialist material is not required. Single-select and multiselect practice includes worked explanations and distractor rationales. Study sources: Bayesian Data Analysis; Causal Inference: What If; Imputation. Photo by Mikhail Nilov on Pexels.
Oct 27, 2026
Design a prediction capstone with realistic dependence, missingness and deployment constraints. Define available information, evaluate full preprocessing/tuning workflows, compare meaningful baselines and examine calibration, predictive uncertainty and subgroup support. Prerequisites: core predictive modeling, grouped/time-dependent data and missing-data methods. Each scenario is independently answerable. Single-select and multiselect practice includes worked explanations and distractor rationales. Study sources: scikit-learn: Cross-validation; Pitfalls; Calibration. Photo by ThisIsEngineering on Pexels.
Oct 27, 2026
Plan and audit a survey or randomized-study capstone. Specify the population, estimand and design; reconcile supplied data and codebooks; calculate design-aware quantities; and communicate effects, uncertainty, missingness and limitations. Prerequisites: core sampling, inference, design, regression and missing-data methods. Standalone case questions support an auditable final project package. Single-select and multiselect practice includes worked explanations and distractor rationales. Study sources: Statistics Canada: Survey Methods and Practices; Weights; Survey Variance. Photo by Thirdman on Pexels.
Oct 27, 2026
Integrate the core Statistics and Probability curriculum through method-selection cases. Choose and critique methods from estimands, sampling/assignment designs, dependence, missingness, outcomes and validation targets. Prerequisites: the core course through multivariate, longitudinal, causal and predictive methods; optional specialist extensions are not required. This quiz supports capstone planning and does not replace project deliverables. Single-select and multiselect practice includes worked explanations and distractor rationales. Study sources: Bayesian Data Analysis; Causal Inference: What If; scikit-learn: Cross-validation. Photo by Vitaly Gariev on Pexels.
Oct 27, 2026
Plan sequential experiments and reason about adaptive decisions. Practice monitoring-error budgets, confidence-sequence interpretation, exploration and exploitation, regret and design-aware policy evaluation. Prerequisites: probability bounds, hypothesis testing, conditional expectation and experimental design. Explicit assumptions distinguish valid monitoring from unplanned repeated fixed-sample testing. Single-select and multiselect practice includes worked explanations and distractor rationales. Study sources: Howard et al.: Confidence Sequences; Bandit Algorithms. Photo by Francie on Pexels.
Oct 27, 2026
Explore spatial association, neighborhood definitions, variograms, Gaussian-process prediction and spatial validation through original environmental and urban-data scenarios. Prerequisites: covariance, conditional Gaussian distributions, regression and basic simulation. Work from interpretable patterns to quantitative checks, aggregation limits and design-sensitive critiques. Single-select and multiselect practice includes worked explanations and distractor rationales. Study sources: Esri: Spatial Autocorrelation; Variogram; scikit-learn: Gaussian Processes. Photo by K on Pexels.
Oct 27, 2026
Develop practical judgment in multivariate normal distribution & distance metrics through 20 original single-select and multiselect problems. Connect multivariate Gaussian models with covariance geometry, affine transformations, whitening, conditional distributions and carefully calibrated anomaly distances. Begin with concepts, then work through calculations, diagnostic scenarios and assumption-sensitive challenges. Prerequisites: probability, regression, statistical inference and the preceding syllabus topics. Detailed worked solutions explain the reasoning and each distractor. Intended easy-to-hard order is retained in the course record; app attempts may reorder questions. Cover: Photo by Kampus Production on Pexels Further reading: [Multivariate normal reference](https://www.statlect.com/probability-distributions/multivariate-normal-distribution).
Oct 26, 2026
Develop practical judgment in quasi-experiments & causal sensitivity through 20 original single-select and multiselect problems. Compare difference-in-differences, regression discontinuity and instrumental variables through calculations, design critiques, local estimands and sensitivity scenarios. Begin with concepts, then work through calculations, diagnostic scenarios and assumption-sensitive challenges. Prerequisites: probability, regression, statistical inference and the preceding syllabus topics. Detailed worked solutions explain the reasoning and each distractor. Intended easy-to-hard order is retained in the course record; app attempts may reorder questions. Cover: Photo by Vitaly Gariev on Pexels. Further reading: [did](https://mixtape.scunning.com/09-difference_in_differences), [rd](https://mixtape.scunning.com/06-regression_discontinuity), [iv](https://mixtape.scunning.com/07-instrumental_variables).
Oct 26, 2026
