Introduction

Most of the hypothesis-testing procedures are based on the assumption that the random samples are selected from normal populations. Fortunately, most of these tests are still reliable when we experience slight departures from normality, particularly when the sample size is large. Traditionally, these testing procedures have been referred to as a parametric methods. Nonparametric or distribution-free methods, that often assume no knowledge whatsoever about the distributions of the underlying populations, except perhaps that they are continuous. There are many applications in science and engineering where the data are reported as values not on a continuum but rather on an ordinal scale such that it is quite natural to assign ranks to the data.

So this course concerns with the Non-Parametric approach instead of using the vast variety parametric statistics test due to violating the assumption of normality in the data. The course also enlightens the significance of different supplementary tests that have no concern with the distribution of data. Another direction of the course indicates the real-life applications, problem and their solutions in the applied field of statistics.

Pre-Requisite

STAT-206,305

​Learning Outcomes

The course gives an introduction to non-parametric statistics, starting with the difference between the mean and the median and the influence of having data with a skewed distribution. At the end of this course, students should be able to

• Distinguish between Parametric and non-parametric procedures
• Understand the problems associated with having skewed data
• Know when to use each type of non-parametric test
• Solve a hypothesis Using Non Parametric test
• Appreciate Practical problems related to Non- Parametric methods
• This course will make students able to choose the correct method of analysis in a wide range of situations.

Skills

       Perform non-parametric testing in software such as SPSS, and interpret the output

RECOMMENDED BOOK

1. Conover, W.J, (1999), Non-Parametric Statistics, 3rd edition, John Wiley and Sons, New York
2. NON PARAMETRIC STATISTICS  (2009)Step by Step Approach  by GREGORY W. CORDER DALE I. FOREMAN

Course plan:

SYSTEM OF EVALUATION:

• Assignments 10% 
• Class Attendance/Class Participation/Presentation  10%
• Mid Term Paper 30%
• Final Term 50 %

 CLASS TIME 

• Every Tuesday   from 9:00 AM to 10:00 AM
• Every Wednesday and Friday from 8:00 AM  to 9:00 AM