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Computing in Statistical Education
Pang Du
Department of Statistics
Virginia Tech
My Limited Teaching Experience
• An introductory probability course for undergraduates
- small class, all from management majors
- no computing required
• A one-year introductory statistics course for nonstatistics major graduate students
- big class, wide varieties of majors
- computing is a critical part
Statistical Computing in Practice
• Statistical software only (80%)
• Statistical software + programming
languages (20%)
• Programming languages only (~0%)
Statistical Software
• SAS
- most powerful, standard in many disciplines and industries
- expensive, overwhelmingly detailed help file, not good at graphics, not
good at programming
• R
- free, powerful with contributed packages, convenient for programming
(similar to MatLab), fairly good graphics
- horrible help search
• SPSS
- standard in some fields, user friendly interface, interactively controllable
graphs
- cheaper than SAS, relatively simple statistical procedures only
Teaching Statistics with Computing
• Teach only statistical methods but not computing
- students free at choosing software
- high-level statistics graduate courses
• Teach statistical software from scratch
- basic syntax, basic functions/procedures, statistical procedures
- statistical computing courses
• Teach certain software but only specific statistical procedures
- templates of the procedures given
- courses focusing on classical statistical procedures
My Class
• Widely different computing experience
- “20+ years of programming”
- “freak out whenever programming is mentioned”
• SAS required
- “too expensive”, “hate SAS with a passion”
- Answer: “sorry, but it is the standard for some of you”
My Class
• Going over SAS examples in class
- “too easy, not worthy of class time”
- “could you go over the troubleshooting again?”
• Templates and detailed interpretations provided
for all the statistical procedures covered
- Unanimously “very helpful”
Summary of Challenges
• Increasing demands but limited resources
- expensive software limits number of licenses
- increasing number of audiences versus limited facilities and staff
• Diversities
- different levels of computing experience
- software with different features
• Mixing statistical theory and methods with statistical computing