Results for “statistical-test”

Fit statistical models (OLS, GLM, ARIMA, mixed models) with detailed diagnostics, residuals, and inference for econometrics and time series analysis.

Computes the Anderson-Darling test statistic and p-value using scipy.stats.anderson for evaluating predictions against ground truth.

Applies quantitative and qualitative analysis techniques, interprets experimental data, validates procedures, and selects appropriate methods with uncertainty quantification.

Save backtest results to SQLite database for comparison. Trigger when: (1) tracking backtest history, (2) comparing model performance, (3) querying best backtests.

Analyzes test suites to measure assertion diversity, detect shallow or trivial assertions, and identify tests that lack meaningful verification.

Scan and audit AI agent skills for security risks before installation, producing a PASS/WARN/FAIL verdict with findings and remediation guidance.

Benchmarks automatic speech translation and recognition on English-French and English-Romanian datasets, reporting BLEU and WER on tokenized outputs.

Advanced data analysis, pattern detection, and insight generation from structured and unstructured datasets. Use when the user wants to analyze data, perform statistical analysis, find insights, detect patterns, identify anomalies, compare segments, test hypotheses, or generate data-driven recommendations. Triggers on phrases like 'analyze data', 'data analysis', 'find insights', 'analyze dataset', 'statistical analysis', 'find patterns', 'compare groups', 'test hypothesis', 'correlation analysis', or 'trend analysis'.

Independent model QA expert who audits ML and statistical models end-to-end - from documentation review and data reconstruction to replication, calibration testing, interpretability analysis, performance monitoring, and audit-grade reporting.

Use this skill to classify tabular data end-to-end in MATLAB — load a dataset, prepare and clean it, select promising classifiers, train them, and compare accuracies with cross-validation, holdout, or hyperparameter optimization plus statistical tests. TRIGGER when: user asks to classify tabular data, pick classifiers for a dataset, compare classifier accuracy, run cross-validation or a holdout evaluation, or find the best model with statistical uncertainty. DO NOT TRIGGER when: user has non-tabular inputs (images, sequences, time series), wants a regression model, is training a specific neural network architecture (use matlab-train-network), or wants cost-sensitive learning or an arbitrary class-prior vector (this skill only supports the built-in uniform-prior toggle for imbalanced data).