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Fitting Models to Data Not Data to Models Workshop Series: Interpreting and Predicting from Generalized Additive Models
March 20 at 2:00 pm - 3:00 pm
FreeThis workshop will show how to interpret GAMs and how to use GAMs to make publication-level figures.
By the end of this session, participants should be able to interpret GAMs and the output of the summary()
function, predict from GAMs, and make figures using GAMs.
This workshop qualifies for the Scholarly Research, Writing, and Publishing Credential offered through the College of Graduate Studies.
Workshop Leader: Stefano Mezzini, Centre for Scholarly Communication Data Consultant
Stefano specializes in statistical modeling, experimental and model design, data wrangling and visualization, and Bayesian statistics (including MCMC, power analyses, and likelihood ratio tests), with a focus on biological modeling. He provides support with R (both base R and tidyverse), RStudio, R Markdown (including bookdown), LaTeX, Git and GitHub.
Fitting Models to Data Not Data to Models Workshop Series
You might have heard someone say that “all models are wrong, but some are useful”. The best way to ensure models are useful is to choose a model that is appropriate to your data and research questions, rather than forcing your data to fit your model’s assumptions (e.g., normality, independence, constant variance).
This series introduces early-career researchers to statistical models that extend beyond linear models (i.e., ANOVAs) so that they may learn how to fit models to their data rather than fitting their data to models. All workshops will use R
and RStudio, so some experience with R
or other programming languages is encouraged but not required. See the R Fundamentals for Data Analysis for an introduction to R
and RStudio. Attendees who do not have experience with R
are encouraged to review this content or take the introductory workshop concurrently if it’s being offered.
You can either register for all workshops, or register for individual workshops that are most applicable to you.