{"enrichment":{"faq":[{"a":"Pywayne-statistics is a unified library for statistical hypothesis testing that covers normality assessment, group comparisons, correlation analysis, time series validation, and regression diagnostics. Each test returns consistent result objects with p-values, confidence intervals, and effect sizes, enabling straightforward validation of data assumptions and detection of significant effects in A/B tests and observational studies.","q":"What statistical hypothesis testing capabilities does pywayne-statistics provide?"},{"a":"Pywayne-statistics includes normality testing functionality such as Shapiro-Wilk tests to assess whether your data follows a normal distribution. The test returns standardized result objects containing p-values and effect sizes, allowing you to determine if your data meets the normality assumption required by many parametric statistical methods.","q":"How can I perform a normality test with shapiro wilk in pywayne-statistics?"},{"a":"Yes, pywayne-statistics supports A/B testing and group comparison workflows. It provides two-sample t-tests, Mann-Whitney U tests for non-parametric comparisons, and ANOVA for multiple group comparisons. Each test delivers p-values, confidence intervals, and effect sizes to help you assess whether observed differences between groups are statistically significant.","q":"Can pywayne-statistics help with A/B testing and statistical significance?"},{"a":"Pywayne-statistics includes comprehensive regression diagnostics to validate model assumptions and identify issues. It provides heteroscedasticity testing, variance inflation factor (VIF) calculations for multicollinearity detection, residual analysis tools, and Durbin-Watson autocorrelation detection to ensure your regression models meet key statistical assumptions.","q":"What regression model diagnostics does pywayne-statistics offer?"},{"a":"Pywayne-statistics provides time series validation tools including ADF (Augmented Dickey-Fuller) stationarity tests and Ljung-Box autocorrelation testing. These functions help you assess whether your time series data is stationary and identify significant autocorrelation patterns, which are critical for proper time series modeling and forecasting.","q":"How does pywayne-statistics handle time series stationarity and autocorrelation?"},{"a":"Pywayne-statistics supports multiple correlation and independence testing approaches including Pearson and Spearman correlation tests for continuous variables, and chi-square independence tests for categorical data. All tests return consistent result objects with p-values and effect sizes, plus support for multiple testing p-value corrections when conducting many tests simultaneously.","q":"What correlation and independence tests are available in pywayne-statistics?"}],"shadow_tags":["hypothesis-testing","ab-testing-framework","statistical-inference","model-validation","time-series-analysis","regression-diagnostics","parametric-nonparametric","effect-measurement"],"summary_rewrite":"Pywayne Statistics provides a unified library for statistical hypothesis testing across normality assessment, group comparisons, correlation analysis, time series validation, and regression diagnostics. Each test returns consistent result objects with p-values, confidence intervals, and effect sizes, making it straightforward to validate data assumptions and detect significant effects in A/B tests and observational studies."},"files":[{"bytes":9894,"path":"pywayne/statistics/SKILL.md","sha256":"2cb70574292ad40740c67058bfaa1020d92e3f524e2e7b76de37951d82105a3e","url":"https://skillfed.io/files/wangyendt/wayne-skills/statistics/9db48ebd/SKILL.md"}],"id":"wangyendt/wayne-skills/statistics","links":{"html":"https://skillfed.io/wangyendt/wayne-skills/statistics","md":"https://skillfed.io/wangyendt/wayne-skills/statistics.md","repo":"https://github.com/wangyendt/wayne-skills"},"meta":{"agents_supported":[],"first_seen":"2026-07-28","forks":0,"language":"Python","last_updated":"2026-07-21","license":"MIT","name":"pywayne-statistics","publisher":"wangyendt","stars":8},"relations":{"similar":[{"id":"K-Dense-AI/scientific-agent-skills/statsmodels"},{"id":"zLanqing/codex-claude-academic-skills/statsmodels"},{"id":"LeonChaoX/qinyan-academic-skills/statsmodels"},{"id":"tondevrel/scientific-agent-skills/statsmodels"},{"id":"synthetic-sciences/openscience/statsmodels"},{"id":"foryourhealth111-pixel/Vibe-Skills/statsmodels"},{"id":"drshailesh88/integrated_content_OS/statsmodels"},{"id":"beita6969/ScienceClaw/statsmodels"},{"id":"HKUDS/Vibe-Trading/quant-statistics"},{"id":"JoelLewis/finance_skills/statistics-fundamentals"}]},"slug":{"owner":"wangyendt","repo":"wayne-skills","skill":"statistics"},"version":"9db48ebd"}