Non-Parametric Statistics Learning With Ease
Published 7/2025
Created by Aina Matthew
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 32 Lectures ( 1h 57m ) | Size: 1.1 GB
Non-Parametric Statistics When The Assumption of Normality Failed
What you'll learn
Student will learn the introduction to non-parametric Statistics
Student will learn diference between parametric and non-parametric
The advantages and Limitations of non-parametric will be learn
Various topics under non-parametric will be studied such as sign test,Run test and many more topics
Both Spearman Correlation and Kendall's tau correlation will be studied
Requirements
Basic knowledge of mathematics
Basic knowledge of statistics
Willingness and Zeal to learn
No programming is required
Description
Course Title: Non-Parametric StatisticsThis course provides an in-depth exploration of non-parametric statistical methods, offering powerful tools for analyzing data without relying on assumptions of normality or equal variances. It begins with the definition and fundamental principles of non-parametric tests, emphasizing their advantages in handling ordinal data, small samples, and data with outliers.Learners will study the Sign Test, a simple yet effective method for testing the median of a distribution. The course then introduces the Wilcoxon Signed-Rank Test, which serves as a non-parametric alternative to the paired t-test for comparing two related samples.The Mann-Whitney U Test is covered as a robust method for comparing two independent groups, while the Run Test is examined for testing the randomness of a data sequence. The course also includes the Kruskal-Wallis Test, which extends the Mann-Whitney test to more than two groups, making it a non-parametric alternative to one-way ANOVA.To address the analysis of relationships between ranked variables, the course explores two key measures of association: Spearman's Rank Correlation Coefficient and Kendall's Tau Correlation Coefficient, both of which assess the strength and direction of monotonic relationships between variables.Throughout the course, practical examples and real-world applications are emphasized to ensure learners can confidently apply non-parametric techniques to various research and professional scenarios.
Who this course is for
Students
Researchers
Educator
Scientist
Anyone interested in learning new things
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