Non-parametric statistics tests : Beginner to Advanced level

Are you facing a problem in understanding Non-parametric hypothesis testing? Are you confused about How to perform a Non-parametric test and interpret its result? then don’t worry because this course will solve all your problems regarding Non-parametric hypothesis testing. If you are preparing for IASSC Lean Six Sigma green beltand Lean Six Sigma Black belt exam then this course will help you in preparing for the important topic of Analyze phase i.e. Non-parametric hypothesis tests in detail.

#Non-parametric hypothesis testing: Basics to advanced level

In this course you will learn :

• Basics of Non-parametric hypothesis testing that includes advantages, disadvantages, and applications.

• Comparison between Parametric and Non-parametric tests on the basis of 6 important criteria.

• All important types of Non-parametric testing by using 13 practical examples.

• 7 important types of Non-parametric hypothesis testing by using traditional method as well as P-value method on Minitab.

   1. 1-sample sign test

   2. 1-sample Wilcoxon test.

   3. Wilcoxon test for paired data.

   4. Mann-Whitney test.

   5. Mood’s median test.

   6. Kruskal Wallis test.

   7. Friedman test.

Why enroll?

1. Lean Six Sigma certification exams like IASSC Lean Six Sigma Green beltand IASSC Lean Six Sigma Black belt most of the time ask questions on Non-parametric hypothesis testing. In the ICGBexam, 8 to 9 questions are being asked, and similarly, in the ICBBexam, 13 to 14 questions are being asked on this topic so all those who are preparing for Lean Six Sigma certification exams like ICGB, ICBB can join this course because this course will help you in preparing for this difficult topic and then you can easily answer the questions based on Non-parametric hypothesis testing.

2. If you want to expand your knowledge about hypothesis testingor want to learn Non-parametric testing then definitely you should join this course because Hypothesis testing has a wide range of applications in Research work, Business decision making, AI & ML, Lean Six Sigma, etc. While selecting hypothesis testing for any application it is important that you need to have the knowledge of Parametric as well as Non-parametric testing so that you can select accurate tests to analyze your data.

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