An introduction to data analysis and statistics, including exploratory data analysis, inference for numerical and categorical variables, and regression.
This course satisfies the G5Q requirement for graduation, and as such it is structured to meet the four broad goals of this requirement:
For assessment, certain exercises on tests will be structured to evaluate how well you are meeting these goals.
- Use appropriate statistical procedures to organize and analyze real-world 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.
Exploring the Practice of Statistics,
by Moore, McCabe, Craig,
"Almost all topics in twenty-first century statistics are now computer dependent ..."
Bradley Efron and Trevor Hastie, 2016, Computer Age Statistical Inference, Algorithms, Evidence, and Data Science
- Stat 204 MWF Spring 2017 (pdf)
Part 1: Exploratory Data Analysis
Part 2: Surveys, Observational Studies, and Experiments
- 3. Graphical and Numerical Methods
- 4. Bivariate Numerical Data
Part 3: Probability and Inference
- 2. Surveys, Observational Studies, and Experiments
Part 4: Categorical Data, Regression, and ANOVA
- 5. Probability, Random Variables, and Probability Distributions
- 6. Inference for Proportions
- 7. Inference for Means
- 8. Further Thoughts on CIs and HTs
- 9. Categorical Data
- 10. Simple Regression
- 11. Multiple Regression
- 12. Analysis of Variance
- Becoming an Independent Learner (pdf)
- Setting Up Your Computer for Stat 204 (pdf)
- Guidelines for Homework (pdf)
- R Programming for Statistics (pdf)
- R Functions for Statistics (pdf)
- OpenIntro Statistics, 3rd Edition is a very fine statistics textbook available for free as a pdf and for about $14 as a paperback and $20 as a hardcover 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, Fall 2016 (Abrahamsen),
Fall 2016 (Mukherjee),
(and supporting github resources)
by Prof. Mine Çetinkaya-Rundel and her colleagues at Duke University. Prof. Çetinkaya-Rundel is a co-author of the OpenIntro statistics text.
- Statistics with R Specialization, Coursera A sequence of four MOOCs, including
introduction to probability and data,
linear regression and modeling, and
by Prof. Mine Çetinkaya-Rundel, Duke University. Her online courses use the OpenIntro statistics text. Excellent videos and carefully stated learning objectives parallel our own course fairly closely. Coursera MOOCs may be audited for free. (First, click to sign up, and then look for the free option.) Highly recommended.
- Introduction to Biostatistics, S2015, and Biostatistical Methods, F2014 and F2016. Very fine lecture notes by Prof. Patrick Breheny, Department of Biostatistics, University of Iowa.
- Peck, Statistics, Learning from Data, 1st Edition, and book companion site
- 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
Office: Woods Laboratories 120
Mondays, 2:00 - 3:00 pm
Wednesdays, 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 (firstname.lastname@example.org), or by voice mail message (x1333).
Wednesdays, 7:00 - 9:00 pm
Thursdays, 7:00 - 9:00 pm
in the Mac Lab, G31,
in duPont Library
The Department of Mathematics and Computer Science sponsors statistics tutoring for students from all sections of Stat 204. Our first
(pioneer!) statistics tutor is Kasey Marshall. She is available to help you come to grips with the theoretical and computational aspects of statistics.
The hours are 7-9 pm on Wednesday and Thursday evenings, and the venue is the Mac Lab, G31, in the basement of the library. To go to the Mac Lab,
enter the library through the front door, turn left and go downstairs towards the ATC computer lab. At the foot of the stairs, turn right and go
nearly to the end of a long hallway. Room G31 is the last room on the right. Drop by to discuss statistics with the tutors and other students, or
just to work on your homework in a supportive environment, with nice computers, and easy access to knowledgeable statistics students.
Every assignment in this class has a specific due date. Normally, work submitted after its due date will not count for a grade.
Exceptions may be granted for officially sanctioned reasons, such as being out of town with a university athletic team, but you must
obtain permission beforehand. Otherwise, late work will not count for a grade and make-up quizzes and exams will not be given.