Nonparametric Statistics Course
Gain expertise in nonparametric statistics for challenging skewed and zero-inflated data commonly seen in health studies. This course teaches durable tests, effect measures, bootstrapping techniques, and reliable R/Python processes to produce trustworthy findings and straightforward reports for tough real-life research scenarios.

from 4 to 360h flexible workload
valid certificate in your country
What will I learn?
Build skills in current nonparametric techniques for uneven, zero-filled, and limited health data outcomes through this practical course. Cover sturdy estimators, ranking tests, quantile and tough regression, group-sensitive methods, and useful effect measures. Practice data exploration, checks for sensitivity, bootstrapping, swap tests, and steady reporting suited to everyday health data issues.
Elevify advantages
Develop skills
- Apply strong nonparametric tests like Wilcoxon, Kruskal-Wallis, and Dunn tests.
- Set up bootstrap and permutation methods for skewed and zero-heavy health data.
- Calculate reliable effect sizes such as Hodges-Lehmann, Cliff’s delta, and rank-biserial r.
- Develop quantile and rank-based regression with category variables.
- Create repeatable R/Python reports featuring solid tables, graphs, and scripts.
Suggested summary
Before starting, you can change the chapters and 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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