Statistics with R

A 2-Day Seminar Taught by Michael Coen, Ph.D. 

The R environment occupies a unique place among data mining and statistical packages. It is the most widely used analytic tool in the world, supported by an enormous library of functions implementing state-of-the-art techniques. It offers both an interactive environment and a programming language for exploring and manipulating data, easily producing sophisticated publication quality graphics. It is the de facto standard throughout academia for data analysis in economics, statistics, biostatistics, sociology, and education. Its business uses are widespread, in fields ranging from pharmacology and geophysics to finance, risk management, and marketing. It is perhaps the single most useful addition to any analyst’s toolbox.

This two-day seminar will provide a comprehensive introduction to R, developing familiarity through practical problem solving. We will explore R’s data management, graphics, analytic, and statistical modeling capabilities. The seminar will be practical and hands-on, focusing on frequently used statistical tests and regression models that can be applied to everyday data.

Participants will learn how to use R from “beginning to end,” starting with sample datasets and performing continuous and categorical data comparisons, analysis of variance, and regression analysis, including multivariable linear and logistic regression. By the end of the seminar, attendees will be able to confidently use R to explore their own data, understand how to further explore its capabilities on their own, and incorporate its output directly into their own work and reports. 


R is free software. It also has a tremendous library of free, specialized analytic tools that can be dynamically added to it on demand. Seminar participants are expected to bring a laptop computer running Windows or Mac OS X with WiFi capability. We will install R and supporting software in real time at the beginning of the seminar. (Participants are also welcome to install R in advance from


This course will benefit both academic and business researchers. Current users of other systems (e.g., Matlab, SAS, Stata, and SPSS) will find this a highly useful addition to their repertoire of tools. The seminar generally will appeal to those who seek an accelerated and in-depth introduction to a powerful, widely popular, and flexible statistical software package.

The course will intently focus on developing mastery of software through practical problem solving. Thus, our focus will be on practice rather than its underlying theory. While basic familiarity with material covered in a first-year undergraduate statistics course will be assumed–such as common probability distributions, hypothesis testing, t-tests, ANOVA, and regression–this will all be re-examined in the context of R, making the course largely self-contained. Some sample problems will extend beyond the basic level to demonstrate the power of R’s framework in answering seemingly simple yet surprisingly complex questions. While helpful, no prior experience with statistical software will be assumed.


The course meets on Friday, October 26, and Saturday, October 27, at Temple University Center City, 1515 Market Street, Philadelphia, PA. The class runs from 9 a.m. to 5 p.m. with a one-hour break for lunch.

Participants receive a bound manual containing detailed lecture notes with equations and graphics, screen shots, examples of R output, and many other useful features. The book will greatly facilitate note-taking, comprehension, and retention of key concepts.

Registration and Lodging

The fee of $895 includes all course materials. 

Lodging Reservation Instructions
A block of guest rooms has been reserved at the Club Quarters Hotel, 1628 Chestnut Street, Philadelphia, PA at a special rate of $112 for a club room and $127 for a standard room per night. This location is a short walk to the seminar location. In order to make reservations, call 203-905-2100 during business hours and provide the group code STA125.
For guaranteed rate and availability, you must reserve your room no later than September 25, 2012. 

Course outline

1.Introduction to the R environment
2. Probability and distributions
3. R essentials — manipulating data and visualization
4. Descriptive statistics
5. One and two sample tests
6. Regression and correlation
7. Analysis of variance (ANOVA)
8. Power and sample size
9. Linear models and logistic regression
10. Generating publication quality images
11. Introduction to the Comprehensive R Archive Network