Howard Seltman's Stat/Math Page

Statistics and Math:

Web Guide for Statisticians Stat & Graphics Resources: YorkU Stephen Few CMU Library Stat Guide StatSoft Elementary Statistics Textbook Statistics Glossary Paul Allison's Blog Donoho: 50 years of Data Science

CMU Statistics Department

On this page: Journals, Organizations and Searching Reference Tutorials and Reviews Calculation Teaching Bad Statistics/Science Causal Inference Bayesian Statistics History Humor Grants/Jobs Biostatistics Datasets and Code Classic Papers (in progress)

Journals, Organizations and Searching:

Reference:

Tutorials and Reviews:

Data Science
- Practical Data Science for Stats
Math Background and Computing
Basic Probability, Statistics and Modelling
Specific non-basic topics
- Machine Learning
  - Support vector machines: Hearst's Intro Burges' Intermediate Others
  - Machine Learning tutorials
  - Data Mining Links
- Longitudinal/Repeated/Mixed/Multilevel Modeling
- Multivariate Analysis
- Modern Non-parametrics
- Epidemiology/Biostatistics
  - Epidemiology Supercourse
  - Bayesian Methods in Health Technology Assessment (monogarph)
  - Cochran Collaborative on Combining Information
  - What is Meta-analysis?
  - Optimal multivariate matching before randomization (blocking) by Greevy, et al., Biostatistics (2004), 5(2),263-275
- Social Networks
- Structural Equation Modeling (SEM)
- Hidden Markov Models
- Sandwich Estimator
- Time Series
- Misc

Calculation:

Teaching:

Bad Statistics/Science:

Light intro to a serious topic: Fifty Ways to Fix Your Data poem
Dance of the p-values
Why Most Published Research Findings Are False by John P. A. Ioannidis (PLOS Medicine: 2(8), 0696-0701, 2005)
The Diederik Stapel affair: The Mind of a Con Man by Yudhijit Bhattacharjee (NY Times, 26 April 2013)
Publishers withdraw more than 120 gibberish papers by Richard Van Noorden (Nature News, 24 February 2014)
When Mice Mislead (pdf) by Jennifer Couzin-Frankel (Science: 342(6161), 922-925, 2013)
The Reformation: Can Social Scientists Save Themselves? by Jerry Adler (Pacific Standard, April 28, 2014)
Replicate This by Kayt Sukel (Pacific Standard, February 26, 2014)
False-Positive Psychology : Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant (pdf) by Joseph P. Simmons, Leif D. Nelson and Uri Simonsohn (Psychological Science: 22(11), 1359-1366 , 2011)
Why bad ideas refuse to die by Steven Poole (The Guardian, June 28, 2016)
Alex Reinhart's Statistics Done Wrong
The Rise of Big Data by Kenneth Neil Cukier and Viktor Mayer-Schoenberger (Foreign Affairs Magazine, May/June 2013)
Big Data: Are We Making a Big Mistake? by Tim Harford (Financial Times Magazine, March 28, 2014)
Retraction Watch
You Can't Biases With Big Numbers by Wheaton and Richey in scrip.insight
Big Data Pitfalls and Promise in Medicine: Big Data Meets Public Health by Muin Khoury and John Ioannidis (Science, 346(6213), p. 1054, 2014)
Big Data Pitfalls and Promise in Social Sciences: Social Media for Large Studies of Behavior by Derek Ruths and Jürgen Pfeffer (Science, 346(6213), p. 1063, 2014)
When Are Results Too Good to Be True? by Gary A. Churchill
A nutty, inconsistent overreaction: Basic and Applied Social Psychology has banned p-values and confidence intervals (though they say they do require effect sizes!!??)
The Trouble with Scientists by Phillip Ball (Nautilus, Issue 024, Chapter 2, May 14, 2015)
Self-correction in Science at Work by Alberts, et al. (Science, 348(6242): 1420-1422, 2015)
Reproducibility in Psychology: Original article data at Open Science Framework Critical response
Everything is Crumbling by Daniel Engber (Slate Magazine, March 2016)
The Use of Statistics in Medical Research by Strasak, et al. (American Statistician, 61: 47-55, 2007). Contains list of errors and flaws.

Causal Inference:

Bayesian Statistics:

History:

Humor:

Grants/Jobs:

Biostatistics:

Datasets:

Multiple (by method and/or by topic)
Textbook Related
- Lots of great datasets from Paul Allison's books and seminars: Statistical Horizons
- R code for Ramsey and Schafer's "Statistical Sleuth: A Course in Methods of Data Analysis": Smith College
- R code for Moore, McCabe, and Craig's "Introduction to the Practice of Statistics": Smith College
- Data and SAS code for A Modern Approach to Regression with R (2009) by S.J. Sheather
- Data and SAS code for SAS System for Mixed Models (1996) by Littell, et. al
- Data and SAS/SPSS/R/Stata code for Linear Mixed Models: A Practical Guide Using Statistical Software (2006) by West, et. al
- Data for Hamilton's Regression with Graphics (linemerge.c) (or use: paste -s -d "\t\n" FILE.RAW to combine pairs of lines)
- Data from Gelman, et al.'s Bayesian Data Aanlysis
- Case Studies in Biometry
- Discovering Statistics (Andy Field): R and SPSS data files
- Statistical Science Web Data Set Links (many textbook data links)
Healthcare Related (see also Government Related)
Government Related
Classic Papers:
- Mixed Models
  - Estimation in Covariance Components Models by A.P. Dempster, D.B. Rubin, and R.K. Tsutakawa, JASA, Vol. 76, 1981, pp. 341-353
- Longitudinal Models
  - A Generalized Multivariate Analysis of Variance Useful Especially for Growth Curve Problems by R.R. Potthoff and S.N. Roy, Biometrika, Vol. 51, 1964, pp. 313-326
  - Analysis of Growth and Dose Response Curves by James E. Grizzle and David M. Allen, Biometrics, Vol. 25, No. 2, Jun., 1969, pp. 357-381
  - On Application of Growth Curve Techniques to Time Series Data by G.O. Zerbe and R. H. Jones, JASA, Vol 75, 1980, pp. 507-509
  - Random-Effects Models for Longitudinal Data by N.M. Laird and J.H. Ware, Biometrics, Vol 38, 1982, pp. 963-974
- Pseudo-likelihood
  - Pseudo Maximum Likelihood Methods: Theory by C. Gourieroux, A. Monfort and A. Trognon, Econometrica, Vol. 52, No. 3. (May, 1984), pp. 681-700.
  - Pseudo Maximum Likelihood Methods: Applications to Poisson Models by C. Gourieroux, A. Monfort and A. Trognon, Econometrica, Vol. 52, No. 3. (May, 1984), pp. 701-720.
- Sparse Data
  - Rule of Three (BMJ, 311(7005):619, 1995)
  - Much Ado About Nothing: A Comparison of the Performance of Meta-Analytical Methods with Rare Eventes by MJ Bradford, JJ Deeks, JA Berlin and AR Localio, Statistics in Medicine, Vol. 26, (2007), pp. 53-77
  - What to Add to Nothing? Use and Avoidance of Continuity Corrections in Meta-Analysis of Sparse Data by MJ Sweeting, AJ Sutton, and PC Lambert, Statistics in Medicine, Vol. 23, (2004), pp. 1351-1375.
- Causality
  - Rubin Causal Model
- Meta-analysis
  - Publication Bias
  - Heterogeneity
- Exact Inference
  - A Survey of Exact Inference for Contingency Tables by Alan Agresti, Statistical Science, Vol 7(1), (Feb 1992), pp. 131-153.
- Post Selection Inference
  - Valid Post-Selection Inference by Richard Berk, Lawrence Brown, Andreas Buja, Kai Zhang, and Linda Zhao, Annals of Statistics, 41(2),802-837.
All links active 2/5/2009. Please report missing links to