Nonparametric Statistics Course
This course equips you with nonparametric statistics skills for complex, skewed, and zero-heavy datasets common in clinical research. Delve into robust testing methods, effect size calculations, bootstrapping procedures, and streamlined R/Python workflows to produce trustworthy analyses and professional reports suited to rigorous real-world applications.
from 4 to 360h flexible workload
certificate valid in your country
What will I learn?
Gain expertise in nonparametric techniques for handling skewed, zero-inflated, and censored data in clinical settings through this practical course. Explore robust estimators, rank tests, quantile regression, cluster methods, and effect measures. Apply exploratory data analysis, sensitivity analysis, bootstrapping, permutation testing, and reproducible reporting for real clinical challenges.
Elevify advantages
Develop skills
- Apply robust nonparametric tests including Wilcoxon, Kruskal-Wallis, and Dunn procedures.
- Design bootstrap and permutation tests tailored for skewed and zero-heavy clinical datasets.
- Calculate reliable effect sizes such as Hodges-Lehmann, Cliff’s delta, and rank-biserial correlation.
- Develop quantile and rank-based regression models incorporating categorical variables.
- Generate reproducible reports in R or Python featuring robust tables, visualisations, 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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