Nonparametric Statistics Course
Gain expertise in nonparametric statistics for handling skewed, zero-heavy, and censored clinical data. This course teaches robust estimators, rank-based tests, quantile regression, cluster-aware methods, practical effect sizes, EDA, sensitivity analysis, bootstrapping, permutation tests, and reproducible reporting using R/Python for real-world clinical challenges.
flexible workload of 4 to 360h
valid certificate in your country
What will I learn?
Master nonparametric methods for skewed, zero-heavy, censored clinical data through hands-on learning of robust estimators, rank tests, quantile/robust regression, cluster approaches, effect sizes, EDA, bootstrapping, permutation tests, and reproducible R/Python reporting for practical clinical applications.
Elevify advantages
Develop skills
- Apply robust nonparametric tests like Wilcoxon, Kruskal-Wallis, and Dunn.
- Design bootstrap and permutation analyses for skewed, zero-heavy clinical data.
- Compute robust effect sizes such as Hodges-Lehmann, Cliff’s delta, rank-biserial r.
- Build quantile and rank-based regression models with categorical covariates.
- Produce reproducible R/Python reports with robust tables, plots, and code.
Suggested summary
Before starting, you can change the chapters and the workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.What our students say
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