Philip Dixon, University Professor of Statistics

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


Use Google calendar to find out when I'm available for meetings

Details on my academic life: See my curriculum vitae

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


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


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:
Analysis of repeated experiments Ken Moore
Using telemetry to locate Monarch butterflies Steve Bradbury, Kelsey Fisher
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


    I am available to help ISU faculty and students with:
        design of experiments or observational studies
        choice of statistical methods
        statistical computing
        interpretation of output from statistical computing packages

    I have a broad background in statistical methods and a specific expertise in:
        analyses to demonstrate equivalence
        ecological statistics
            analysis of species composition data
            population size estimation
            analysis of spatial patterns
        environmental statistics
            analysis of censored data, especially below-detection limit values
            analysis of environmental trends
        computer-intensive analysis
            randomization tests

    I even like to get out into the field and see work in progress, when time permits.

    To make an appointment, please e-mail me at , or call me at 4-2142.

    Photos of the fun parts of my life