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Browse all available quizzes, quizbooks, articles, textbooks, SynLearn templates, and SynPrep practice tracks.

1890 learning items

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Showing 565–576 of 1890 items

Quiz

ARIMA, Seasonal Models & Dynamic Regression

Integrate ARIMA and dynamic-regression reasoning through original monthly sales, energy and service examples. Practice differencing, order notation, ACF/PACF clues, seasonal polynomials, residual diagnostics and forecasts with regression errors. Prerequisites: AR and MA processes, stationarity, regression and basic forecasting. Single-select and multiselect questions emphasize model assumptions and diagnostic interpretation, with complete worked explanations. Reading: [Statistical reference 1](https://otexts.com/fpp3/arima.html); [Statistical reference 2](https://otexts.com/fpp3/dynamic-regression.html).

statistics

Oct 26, 2026

20 questions

Quiz

Forecast Baselines, Decomposition & Exponential Smoothing

Compare transparent forecasting baselines with exponential smoothing using original retail, utility and service examples. Calculate naive, seasonal-naive, drift and smoothing forecasts, examine trend damping and seasonal choices, and interpret state-space uncertainty. Prerequisites: temporal components, stationarity and conditional expectation. Includes single-select and multiselect problems progressing from direct calculations to model critique, with worked explanations. Reading: [Statistical reference 1](https://otexts.com/fpp3/toolbox.html); [Statistical reference 2](https://otexts.com/fpp3/exponential-smoothing.html); [Statistical reference 3](https://otexts.com/fpp3/decomposition.html).

statistics

Oct 26, 2026

20 questions

Quiz

Autoregressive & Moving Average Models

Study autoregressive and moving-average dependence using explicit innovation assumptions and original examples. Calculate AR(1) means, variances, ACFs and forecasts; derive MA(1) covariance and invertibility; contrast stationary processes with random walks and interpret ADF-test evidence carefully. Prerequisites: stationarity, covariance, geometric series and hypothesis testing. Both question types include worked calculations and misconception-focused explanations. Reading: [Statistical reference 1](https://otexts.com/fpp3/arima.html); [Statistical reference 2](https://www.statsmodels.org/stable/generated/statsmodels.tsa.stattools.adfuller.html).

statistics

Oct 26, 2026

25 questions

Quiz

Time Series Decomposition & Stationarity

Learn to describe temporal structure before fitting forecasts. Original demand, energy and environmental examples explore additive and multiplicative components, seasonal adjustment, stationarity definitions, autocovariance and sample ACF interpretation. Prerequisites: variance, covariance and introductory time-indexed data. The questions move from identification to calculations and counterexamples, with both single-select and multiselect items and worked explanations. Reading: [Statistical reference 1](https://otexts.com/fpp3/decomposition.html); [Statistical reference 2](https://otexts.com/fpp3/arima.html).

statistics

Oct 26, 2026

25 questions

Quiz

Multivariate Inference & Canonical Correlation

Practice joint inference for correlated responses and the interpretation of canonical correlation. Problems cover one-sample Hotelling statistics, finite-sample calibration, MANOVA, simultaneous conclusions, canonical variates and covariance regularization. Prerequisites: multivariate normal distributions, covariance inversion, hypothesis testing and linear algebra. Worked single-select and multiselect questions emphasize assumptions, multiplicity, high-dimensional limitations and the difference between association and causation. Reading: [Statistical reference 1](https://www.itl.nist.gov/div898/handbook/pmc/section5/pmc543.htm); [Statistical reference 2](https://online.stat.psu.edu/stat505/Lesson08); [Statistical reference 3](https://scikit-learn.org/stable/modules/cross_decomposition.html#canonical-correlation-analysis).

statistics

Oct 26, 2026

20 questions

Quiz

Clustering & Mixture Models

Develop practical judgment about k-means, hierarchical clustering and Gaussian mixture models. Use customer, environmental and instrument examples to calculate assignments, linkages and responsibilities, then examine scaling, local optima, label switching, cluster stability and overinterpretation. Prerequisites: Euclidean distance, covariance, probability and likelihood. Includes single-select and multiselect questions with detailed worked reasoning. Reading: [Statistical reference 1](https://scikit-learn.org/stable/modules/clustering.html); [Statistical reference 2](https://scikit-learn.org/stable/modules/mixture.html).

statistics

Oct 26, 2026

20 questions

Quiz

Factor Analysis & Latent Measurement Models

Explore factor analysis as a latent measurement model for correlated observed indicators. Work through communalities, uniquenesses, covariance reconstruction, rotation, identifiability and score uncertainty using original survey and laboratory scenarios. Prerequisites: covariance, matrix multiplication and PCA. Includes conceptual, numerical and diagnostic single-select and multiselect questions; factors are interpreted as model-based summaries rather than established causes. Reading: [Statistical reference 1](https://scikit-learn.org/stable/modules/decomposition.html#factor-analysis); [Statistical reference 2](https://stats.oarc.ucla.edu/spss/seminars/introduction-to-factor-analysis/a-practical-introduction-to-factor-analysis/).

statistics

Oct 26, 2026

20 questions

Quiz

Principal Component Analysis (PCA)

Build a working understanding of principal component analysis through sensor, laboratory and business examples. Practice spectral decomposition, component scores, explained variance, scree plots, scaling choices and reconstruction, then examine instability, leakage and prediction pitfalls. Prerequisites: covariance matrices, eigenvectors, linear algebra and multivariate distributions. Includes single-select and multiselect problems with worked solutions and distractor explanations. Reading: [Statistical reference 1](https://www.itl.nist.gov/div898/handbook/pmc/section5/pmc551.htm).

statistics

Oct 26, 2026

30 questions

Quiz

Meta-Analysis & Heterogeneity

Explore evidence synthesis using inverse-variance weights, common-effect and random-effects models, forest plots and heterogeneity diagnostics. Interpret prediction intervals and assess dependence, publication bias and sensitivity without treating pooling as a cure for study bias. Prerequisites: probability, statistical inference and the preceding core curriculum. Original single-select and multiselect scenarios progress from interpretation to worked applications and assumption-sensitive challenges. Detailed solutions explain the alternatives; this is educational statistical practice.

statistics

Oct 26, 2026

20 questions

Quiz

AFT Models, Competing Risks & Prediction

Compare accelerated failure-time and proportional-hazards interpretations, then analyze competing events through cause-specific hazards and cumulative incidence. Assess horizon-specific survival predictions, calibration and model assumptions. Prerequisites: probability, statistical inference and the preceding core curriculum. Original single-select and multiselect scenarios progress from interpretation to worked applications and assumption-sensitive challenges. Detailed solutions explain the alternatives; this is educational statistical practice.

statistics

Oct 26, 2026

20 questions

Quiz

Cox Proportional Hazards Models

Practice Cox proportional hazards regression with coefficient contrasts, risk-set likelihoods, baseline survival and diagnostic reasoning. Distinguish time-dependent covariates from time-varying effects and assess predictions without overinterpreting hazard ratios. Prerequisites: probability, statistical inference and the preceding core curriculum. Original single-select and multiselect scenarios progress from interpretation to worked applications and assumption-sensitive challenges. Detailed solutions explain the alternatives; this is educational statistical practice.

statistics

Oct 26, 2026

20 questions

Quiz

Censoring, Truncation & Survival Uncertainty

Develop censoring-aware inference with delayed entry, risk-set construction, Greenwood uncertainty and cumulative hazard. Examine informative dropout, confidence bands and the limits of sparse survival tails. Prerequisites: probability, statistical inference and the preceding core curriculum. Original single-select and multiselect scenarios progress from interpretation to worked applications and assumption-sensitive challenges. Detailed solutions explain the alternatives; this is educational statistical practice.

statistics

Oct 26, 2026

20 questions