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, Restimates 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.
Notices
 Homework 1 deadline extended to 14 September, start of class.
 Office hours will be Mondays and Tuesdays 1011am in Hutcheson 403G, or by appointment
 Lectures will primarily chalkboard 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

Stats Review:
Supplementary examples: Nucleus study (data), central limit theorem, confidence intervals, and hypothesis tests. 
Tests Based on the Binomial Distribution:
Supplementary examples: binomial review, quantile tests, tolerance limits, sign tests, and variations on sign tests.
Homework Due at the start of lecture
 Homework 1: probability and statistics review, due 14 Sept 2017
Solutions  Homework 2: binomials, quantiles and tolerance limits, due 26 Sept 2017
Computing
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:
 A quick R tutorial and accompanying code file
 Some helpful video tutorials and step by step guides
 R Studio is an excelent multiplatform graphical
interface to
R
which you will likely prefer to the default Windows/OSX GUI(s).