STAT 161 - Introduction to Nonparametric Methods Learning Course

[STAT 161 - Introduction to Nonparametric Methods Learning Course] STAT 161 - Introduction to Nonparametric Methods STAT 161 introduces students to nonparametric statistical methods, which are distribution-free techniques used when assumptions about data distribution cannot be met or when data are ordinal or nominal. The course covers topics such as rank-based tests, kernel density estimation, bootstrap methods, and nonparametric regression. Students learn how to conduct hypothesis tests and construct confidence intervals without assuming specific distributions, using computational tools to analyze data robustly. Through theoretical foundations and practical applications, students gain skills in exploring relationships in data, detecting patterns, and making inferences in diverse fields such as biology, economics, and environmental sciences. STAT 161 prepares students for advanced coursework in statistical methodology and research applications where traditional parametric methods may not be appropriate.This Learning Course is a platform that helps students to study and learn. The platform provides all the required material for the students to study. The material includes Study guide, Exam Questions & Answers, Study Notes and other resources that are useful for the students. It also gives information about the upcoming exams so that students can prepare themselves accordingly.