Robert B. Gramacy Professor of Statistics

Nonparametric Statistics

STAT 3504 is an undergruadate course focused on statistical methodology based on ranks, empirical distributions, and runs. One and two sample tests, ANOVA, correlation, goodness of fit, rank regression, R-estimates and confidence intervals. We will learn comparisons with classical parametric methods. There will be an emphasis on assumptions and interpretation. It is targeted towards students who have completed (and remember the concepts from) a course in introductory statistics. We will make extensive use of computational tools, such as the R language for statistical computing, both for illustration in class and in homework problems.


  • Homework 1 deadline extended to 14 September, start of class.
  • Office hours will be Mondays and Tuesdays 10-11am in Hutcheson 403G, or by appointment
  • Lectures will primarily chalk-board based, supplemented by computing demonstration in R. The code behind those demonstrations will be posted below. For notes you must come to class!

Lecture materials

Homework Due at the start of lecture

  • Homework 1: probability and statistics review, due 14 Sept 2017
  • Homework 2: binomials, quantiles and tolerance limits, due 26 Sept 2017


The recommended language for this course is R, which can be obtained from CRAN. Other languages such as MATLAB are allowed but are not recommended. Examples in lecture, and help in office hours, etc., will be exclusively in R. Below are some helpful R resources: