Philip Dixon, University Professor of Statistics Emeritus

Where to find me.... 
   In person: 
     2121 Snedecor Hall

   Mailing address:
     Department of Statistics
     Snedecor Hall
     Iowa State University
     Ames, IA 50011-1210
     Tel: (515) 294-2142 
     Fax: (515) 294-4040

     email: pdixon@iastate.edu
     http://pdixon.stat.iastate.edu/

Details on my academic life: See my curriculum vitae

Quick index to this web page:
    Teaching
    Consulting
    Research
    Manuscripts and Preprints
    Software
    Lunchinators, non-GitHub information
    Photos of my life

Teaching

Statistics 301 Intermediate Statistical Methods
Statistics 401 Statistical Methods for Research Workers
Statistics 587 Statistical Methods for Research Workers (new number)
Statistics 471/571  Introduction to Experimental Design (the old 402)
Statistics 406  Statistical Methods for Spatial Data
Statistics 415  Advanced Statistical Methods for Research Workers
Statistics 493  Workshop in Statistics
Statistics 500  Statistical Methods
Statistics 505  Environmental Statistics
Statistics 510  Statistical Methods - II
Statistics 534  Ecological Statistics
EEB 698  Seminar on Ordination Methods

Research

My favorite research develops and evaluates statistical methods to answer interesting biological questions.  A lot of this work is collaborative.  The themes are using likelihood inference in non-standard situations and using computer-intensive methods.  Some of the current projects include:
 
 
Topic: With:
Predicting sex from fragmented bones Andrew Somerville
Estimating genetic gain Cintia Sciarresi and others
Equivalence of measured physical activity Greg Welk
Model-based visualization of community data various

Reprints and preprints

These are now archived in the Iowa State University Digital Repository. The easiest way to find all my available work is to click here .

Software and Data sets

Some useful SAS programs and R programs are available for public domain use. These archives include programs, macros, or functions for estimating Gini coefficients, bootstrapping, analysis of trends in species composition, analysis of censored data, and prediction from linear mixed effects models. Other SAS programs for analysis of experimental data, including variance component estimation, and simple mixed models are available on class web pages:

  • Statistical Methods: SAS, R
  • Design and Analysis of Experiments,
  • Random coefficient regression,
  • Environmental statistics, and
  • Ecological statistics.
  • Demo code for Reproducible Research using Rstudio, Rmarkdown, and knitr

    Some publicly available data sets

    Consulting


    I retired in May 2025 and am no longer supervising the ISU statistical consulting group. ISU faculty, staff and students can get help by visiting and requesting a meeting. Note: Your computer must be on the campus network to make a request. If off campus, use a VPN connection to campus

    Photos of the fun parts of my life