Nonparametric Statistics Course
This course equips you with essential nonparametric statistics skills for analysing 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 dependable analyses and professional reports suited to rigorous real-world studies, ensuring reliable insights from challenging data.
from 4 to 360h flexible workload
valid certificate 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. You will explore robust estimators, rank tests, quantile regression, cluster methods, and effect measures, while applying exploratory data analysis, sensitivity analysis, bootstrapping, permutation testing, and reproducible reporting for real clinical challenges.
Elevify advantages
Develop skills
- Apply powerful nonparametric tests including Wilcoxon, Kruskal-Wallis, and Dunn procedures.
- Develop bootstrap and permutation strategies tailored to skewed and zero-heavy clinical datasets.
- Calculate reliable effect sizes such as Hodges-Lehmann, Cliff’s delta, and rank-biserial correlation.
- Construct quantile and rank-based regression models incorporating categorical variables.
- Generate reproducible reports in R or Python featuring robust tables, visualisations, and clean code.
Suggested summary
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