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.
Statistics, Learning from Data, 1st Edition,
by Roxy Peck,
CENGAGE Learning, 2015
book companion site
- Stat 204 MWF Spring 2016 (pdf)
Part 1: Exploratory Data Analysis
Part 2: Surveys, Observational Studies, and Experiments
- 2. Graphical Methods
- 3. Numerical Methods
- 4. Bivariate Numerical Data
Part 3: Probability, Random Variables, and Probability Distributions
- 1. Surveys, Observational Studies, and Experiments
Part 4: Inferential Statistics
- 5. Probability
- 6. Random Variables and Probability Distributions
Part 5: Experimental Data, Categorical Data, Regression, and ANOVA
- 7. Statistical Inference
- 8. Sampling Distributions
- 9. Estimating a Proportion
- 10. Hypothesis Tests for a Proportion
- 11. Hypothesis Tests for Two Proportions
- 12. Hypothesis Tests for a Mean
- 13. Hypothesis Tests for Two Means
- 14. Experimental Data
- 15. Categorical Data
- 16. Regression
- 17. 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 $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, Spring 2015, and Fall 2015 (and supporting github resources) by Prof. Mine Çetinkaya-Rundel, Duke University. Prof. Çetinkaya-Rundel is a co-author of the OpenIntro statistics text.
- Data Analysis and Statistical Inference, September 2015, on Coursera, by Prof. Mine Çetinkaya-Rundel, Duke University. Prof. Çetinkaya-Rundel brings her Duke University statistics course online via the Coursera platform. Her online course uses the OpenIntro statistics text. Highly recommended.
- Statistics Notes, British Medical Journal "Perhaps the finest series of short articles on the use of statistics is the occasional series of Statistics Notes started in 1994 by the British Medical Journal. It should be required reading in any introductory statistics course."
- Introduction to Biostatistics, Prof. Patrick Breheny, U of Iowa. Excellent lecture notes.
- JSE Interview with Roxy Peck
- 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
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 (firstname.lastname@example.org), or by voice mail message (x1333).
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.