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Model Selection and Simplification

1 message · Oliver Hooker

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Oliver Hooker
# 
Master Model Selection & Simplification in R ? Live, Hands-On, 2 Days
https://www.prstats.org/course/model-selection-and-model-simplification-msms05/

*What you?ll gain:*

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   Clarity on *model fit metrics* (likelihood, deviance, residuals)
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   Confidence comparing *nested models* across linear, GLM, and mixed models
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   Tools to avoid overfitting using *cross-validation* and *information
   criteria (AIC, BIC, etc.)*
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   Practical know-how of *variable selection techniques* ? stepwise, ridge,
   Lasso, elastic net
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   Best practices on *model averaging vs simplification*

*Format & logistics:*

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   *Dates:* 3?4 November 2025
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   *Duration:* 2 days, 4 hours per day
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   *Format:* Live online (UK time) ? Sessions are recorded for later viewing
   -

   *Investment:* ?250 (early bird ?225 for first 5 spots)
   -

   *Prerequisites:* Basic familiarity with R & RStudio; some experience
   with regression (lm, glm) helpful
   -

   *Support included:*
   ? All code, slides, and datasets provided
   ? Bring your own data for hands-on work
   ? 30 days? email support + access to session recordings
   ? Certificate of attendance awarded

*Instructor:*
*Dr Niamh Mimnagh*, experienced statistician bridging ecology, epidemiology
and data science.

*Who should attend:*
Researchers, data analysts, postgraduate students ? anyone who routinely
fits statistical models and wants to move beyond ?black box? modeling to
principled model evaluation, selection, and simplification.
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*Limited early bird slots ? reserve your place now!*
Transform your modeling approach by combining theory, applied examples, and
hands-on coding in R.