
Statistics 204
Elementary Statistics

Course Description
An introduction to data analysis and statistics, including exploratory data analysis, inference for numerical and categorical variables, and regression.
Assessment Goals
This course satisfies the G5Q requirement for graduation, and as such it is structured to meet the four broad goals of this requirement:
 Use appropriate statistical procedures to organize and analyze realworld data.
 Apply probabilistic reasoning to evidence presented in data.
 Demonstrate proficiency in interpreting statistical results in writing.
 Understand the limits and abuses of statistical practices.
For assessment, certain exercises on tests will be structured to evaluate how well you are meeting these goals.
Textbook
Statistics, The Art and Science of Learning from Data, 3nd Edition,
by Agresti and Franklin,
Pearson, 2013
Schedule
 Stat 204 MWF Spring 2015 (pdf)
Course Notes
Part 1: Gathering and Exploring Data
 1. Statistics: The Art and Science of Learning from Data
 2. Exploring Data with Graphs and Numerical Summaries
 3. Association: Contingency, Correlation, and Regression
 4. Gathering Data
Part 2: Probability, Probability Distributions, and Sampling Distributions
 5. Probability in Our Daily Lives
 6. Probability Distributions
 7. Sampling Distributions
Part 3: Inferential Statistics
 8. Statistical Inference: Confidence Intervals
 9. Statistical Inference: Significance Tests about Hypotheses
 10. Comparing Two Groups
Part 4: Analyzing Association and Extended Statistical Methods
 11. Analyzing the Association Between Categorical Variables
 12. Analyzing the Association Between Quantitative Variables: Regression Analysis
 13. Multiple Regression
 14. Comparing Groups: Analysis of Variance Methods
 15. Nonparametric Statistics
Additional Materials
 Setting Up Your Computer for Stat 204 (pdf)
 Introduction to Statistics (pdf)
 R Programming for Statistics (pdf)
Online Courses and Reference Materials
 OpenIntro Statistics, 2nd Edition is a very fine statistics textbook available for free as a pdf and for about $10 as a printed book from Amazon.com. Check out the associated labs, videos, lecture slides, and data files. Under the textbook tab, click on "Learning Objectives." Each of these eight documents on learning objectives presents a detailed analysis of the content of the respective textbook chapter, together with interspersed links to supporting materials, including numerous short statistics videos produced by OpenIntro collaborators and others. The OpenIntro Statistics textbook is used at Duke University for Statistics 101. See the following link. More recently, two more statistics textbooks have been made available by OpenIntro at the same website. Introductory Statistics with Randomization and Simulation is a close cousin of the original "OpenIntro Statistics" textbook, with a bit more emphasis on randomization and simulation, and a probability section that has been moved to the back of the book as an appendix. Both of these books are excellent supplements to the present course.
 Data Analysis and Statistical Inference, Spring 2013, Summer 2013, Fall 2013, Fall 2014, and Spring 2015 (and supporting github resources) by Prof. Mine ÇetinkayaRundel, Duke University. Prof. ÇetinkayaRundel is a coauthor of the OpenIntro statistics text.
 Data Analysis and Statistical Inference, February 2014, September 2014, March 2015, on Coursera, by Prof. Mine ÇetinkayaRundel, Duke University. Prof. ÇetinkayaRundel brings her Duke University statistics course online via the Coursera platform. Her online course uses the OpenIntro statistics text.
Highly recommended.
 Introduction to Biostatistics, Spring 2014, Prof. Patrick Breheny, College of Public Health, University of Iowa
This is a welldesigned, thoughtful, and engaging course on biostatistics featuring selfcontained lecture notes and supporting labs based on R and SAS. Students can run SAS University Edition for free on their own Linux, Mac, or Windows machines via virtualization software.
 Statistical Reasoning, Open Learning Initiative, CMU This course and the following one are free, online statistics courses hosted by CarnegieMellon University. One of these two courses includes a bit more probability than the other.
 Probability and Statistics, Open Learning Initiative, CMU
 Stat 2.1, Stat 2.2, Stat 2.3, by Prof. Ani Adhikari, edX, UCB. The courses are now closed, but the materials may still be online. These three edX courses, taken together, cover the material of Statistics 2 at the University of California, Berkeley, which is taken by about 1,000 students each year. Something like 50,000 students from all over the world signed up for the edX versions.
 SticiGui, an online statistics textbook by Prof Stark, UCB, which serves as the textbook for the previous edX courses. A series of 25 lecture videos from Prof. Stark's class in 2009 is available from YouTube via the menu button in the upper left corner of this page. Introductory quote by Prof. Stark : "Statistics means never having to say you're certain."
 Statistics One, by Prof. Conway, Coursera, Princeton. This online course begins on Sep 22, 2013, and continues for 12 weeks. Coursera courses are typically taken by tens of thousands of students from all over the world, and some of the edX and Coursera courses have had more than 100,000 students enrolled. The first time this course was offered, it had an enrollment of about 80,000 students. This time they are expecting an enrollment of about 100,000 students.
 GAISE Reports, Guidelines for Assessment and Instruction in Statistics Education, College Report. Very thoughtful essay on modern statistics education, meant particularly for teachers and students of statistics.
 Against All Odds: Inside Statistics, a televisionbased course in statistics with 26 halfhour long programs, produced in 1989 by Annenberg / Corporation for Public Broadcasting. The programs are available in videoondemand format for free from this website, and can be purchased in DVD format.
 Handbook of Biological Statistics, an online textbook by John McDonald, University of Delaware, for his course Biological Data Analysis, Fall 2014
 Statistics glossary, University of Glasgow
 Statistics glossary from Prof Stark, Statistics Department, University of California, Berkeley
 Tales of Statisticians
R Programming
 R programming environment. Useful libraries include car, ggplot2, lattice, MASS, openintro, RColorBrewer.
 RStudio programming environment. See this note if you are planning on using RStudio with the new Mac OS X 10.9, Mavericks.
 RTutorial Elementary Statistics with R
 QuickR A more comprehensive survey of statistics with R
 Try R, An R tutorial sponsored by O'Reilly and created by Code School
 R short reference card A fourpage summary of R commands
 R color chart R colors and color names, in color
 Introduction to the Practice of Statistics (6th Edition) in R by Nicholas Horton and Ben Baumer of Smith College. These notes cover R programming in support of an introductory statistics course based on a popular textbook by Moore and collaborators. This is an excellent supplement for our own course.
Further Reading
 Freedman, Pisani, Purves, Statistics, 4e
 Freedman, Statistical Models: Theory and Practice
 Whitlock, Schluter, The Analysis of Biological Data, Second Edition
, and supporting web site.
 Peck, et al., Statistics: A Guide to the Unknown
 Salsburg, The Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century
Professor
Chris Parrish
Office: Woods Laboratories 120
email: cparrish@sewanee.edu
Office Hours
Wednesdays, 2:00  3:00 pm
Fridays, 2:00  3:00 pm
If you would like to talk with me in addition to the interactions generated during those hours, please make an appointment to see me in my office (WL120), either when you see me in class or in the hallways or in my office, or by email (cparrish@sewanee.edu), or by voice mail message (x1333).
cparrish@sewanee.edu