Syllabus for Stat 5660
Applied Nonparametric Statistics
Spring Term 2018

The web page for the course is:

Projects are due on Thur, thereafter a late penalty of 10 points per day. The absolute latest handin is on Sunday.

For R Class Scripts click here.

The text for the course is:

Nonparametric Statistical methods Using R, Kloke and McKean (2014), Boca Raton, FL: Chapman-Hall.

As the title suggests, the statistical software for this course is R. No previous knowledge of R is necessary. You will learn R as the course progresses. R is freeware and runs on all platforms. It is a powerful language (it has become the language of Statisticians) yet, as you will see, it is very simple to use.

To download R on your computer, just type CRAN in google or goto

i.e. Click here for CRAN

  1. Then Click on Download R for Windows, or for mac or for linux dependning on your computer.
  2. Then click on install R for the first time.
  3. Then click on Download R 3.4.3 for Windows .
Just take default and in less than a minute you will have installed R.

In Chapter 1 of the text we introduce R. Note that on the CRAN page there are free manuals for using R. Also feel free to download the following manual:
Free Manual on R

Also there is a free clickable R, R Studio, which you can download here.

Where we are: Reading and Problems for Class Discussion.

Data, rdas can be found here.

Other data can be found here.

R functions for the course. .


Please feel free to use the following Stat methods text. The author (Dr. Rasmussen allowed us to use it). In the Reading and Problems for Class Discussion page, I will refer to this book by Rasmussen along with the chapter, section, and page number.
Methods Book .

This is a course in applied nonparametric statistics. It assumes that the student has one undergraduate methods course in Statistics. One of the following Stat courses would satisfy this prerequisite: Stat 3640, Stat 2600, Stat 3660, Stat 2160, or Stat 3620.

Nonparametric statistical methods are inferential procedures which are valid under very mild assumptions. Hence, they are useful statistical procedures for many areas. These procedures can be used for the basic one and two sample location problems as well as higher order designs. They include techniques for the fitting of general linear models and testing general linear hypotheses. This course will cover these topics and also discuss appropriate statistical software for the computation of these procedures.

Topics Include:

  1. Introduction to R, (Chapter 1 and notes).
  2. Review of some simple statistical methods estimation, confidence intervals, tests of hypotheses, (Chapter 2 and notes).
  3. One-sample problems including diagnostic procedures (every topic includes diagnostic procedures), (Chapter 2 and notes).
  4. Two Sample Problems,(Chapter 3 and notes).
  5. Regression I, (Chapter 4 and notes).
  6. ANOVA (one-way and crossed), ANCOVA, (Chapter 5 and notes).
  7. From this point-on, it is pick and choose from the topics:
    1. Regression II, (Chapter 7 and notes).
    2. Cluster Correlated Data, (Chapter 8 and notes).
    3. Failure Time Data, (Chapter 6 and notes).

Office Hours: