Last updated: 2019-04-29
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Knit directory: MSTPsummerstatistics/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 22ae3cd | Anthony Hung | 2019-04-29 | Add HMM file |
| html | e746cf5 | Anthony Hung | 2019-04-28 | Build site. |
| Rmd | 133df4a | Anthony Hung | 2019-04-28 | introR |
| html | 133df4a | Anthony Hung | 2019-04-28 | introR |
| html | 22b3720 | Anthony Hung | 2019-04-26 | Build site. |
| html | ddb3114 | Anthony Hung | 2019-04-26 | Build site. |
| html | 413d065 | Anthony Hung | 2019-04-26 | Build site. |
| html | 6b98d6c | Anthony Hung | 2019-04-26 | Build site. |
| html | 602e0f9 | Anthony Hung | 2019-04-25 | Build site. |
| Rmd | ecc06a5 | Anthony Hung | 2019-04-24 | Add syllabus: |
| html | ecc06a5 | Anthony Hung | 2019-04-24 | Add syllabus: |
| Rmd | 2459910 | Anthony Hung | 2018-09-28 | Update all webpages |
| html | 2459910 | Anthony Hung | 2018-09-28 | Update all webpages |
A thorough understanding of statistics is essential for both experimental design and data analysis. Too often, time and resources are wasted due to a poor understanding of sample size and power calculations, and the reliability of scientific reports has repeatedly been scrutinized in recent years due to questionable, if not fraudulent, application of statistical tests.
In an era of increasingly accessible computational tools, big data, and an emphasis on open-access databases, the need for rigorous training in statistical methods has become more important than ever. As a requirement for entry into Pritzker, all MSTP students must have taken a statistics or biomathematics course in college. At present, the biostatics course required for all MSTP trainees is very similar in content to what is covered in most introductory college statistics courses, making it unappealing and ineffective for most students. However, many of us agree that further training in statistics would be a valuable resource and would help us feel more confident about the work we are doing.
Foundations of theoretical and applied understanding of probability and statistics
Working knowledge of how to use R
Deeper understanding of statistical tests specific to research area
Connect students with resources to seek out statistical advising in the future
Data analysis project using real data
Week 1: Why statistics matters; Intro to R; Probability and distributions
Week 2: Review of basic statistical tests
Week 3: PCA, t-SNE, bootstrapping and machine learning
Week 4: Markov Chains; Hidden Markov Models; Linear Regression; Mutliple Testing
Weeks 5-9: Breakouts within specific programs
Work with PIs or senior grad students in same research area
Handle real data and work hands-on with analysis tools used by people in the field
Prepare a presentation with results of statistical analyses
Week 10 : Data presentations
Thank you very much to Frank Wen and Katie Lee, who started the course and gave helpful advice.