During winter 2018 I am teaching the following courses at Carleton:

  • Math 215: Introduction to statistics
  • Math 285: Introduction to data science

Past courses that I have taught at Carleton:

  • Math 245: Applied regression
  • Math 265: Probability
  • Math 275: Introduction to statistical inference
  • Math 315: Bayesian statistics

Recent Publications

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  • Model Choice and Diagnostics for Linear Mixed-Effects Models Using Statistics on Street Corners

    Details PDF Code Journal

  • Variations of Q-Q Plots: The Power of Our Eyes!

    Details PDF Journal

  • Are You Normal? The Problem of Confounded Residual Structures in Hierarchical Linear Models

    Details Code Journal

  • Upper Midwest Climate Variations: Farmer Responses to Excess Water Risks

    Details PDF

  • Understanding Corn Belt farmer perspectives on climate change to inform engagement strategies for adaptation and mitigation

    Details PDF

Recent Talks

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Data Science for Statistics

Tutorials and case studies teaching data-scientific concepts in statistics courses.

lmeresampler: Bootstrapping Clustered Data in R

Providing an easy way to bootstrap nested linear-mixed effects models using either the parametric, residual, cases, CGR (semi-parametric), or random effects block (REB) bootstrap fit using either lme4 or nlme.

qqplotr: ggplot2 Compatible Quantile-Quantile plots in R

Extending ggplot2 to provide a complete implementation of Q-Q plots.

Recent Posts

In June I attended an ACM workshop focused on how the ACM can facilitate sharing elements of a data science curriculum across institutions. This is my recap.


The analyses that get me excited are not Google crunching a terabyte of web ad data in order to optimize revenue… [but rather] the biologists who are absolutely passionate about this one swampfly and now they can use R and they can understand it.


The Upshot takes a look at who’s in and who’s out of the first Republican debate taking into account sampling variability.



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