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This 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 session will introduce participants to the Analysis of Variance (ANOVA), a statistical method used for comparing the means of three or more groups. The concept of blocking will also be introduced to reduce noise and isolate sources of variation.

By the end of the session, participants should be able to use ANOVA for multi-treatment analysis, implement blocking in experimental design, calculate the F statistic for assessing significance, and appreciate how blocking can improve the efficiency of a study.

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This workshop will re-visit random and fixed effects with Hierachical GAMs (HGAMs) and expand the concepts of random slopes by introducing random smooths. The workshop will also cover smooth, nonlinear interaction terms via the ti() and te() functions.

By the end of this session, participants should be able to fit HGAMs with smooth interaction terms, plot and interpret the models.

This session will address the concepts of correlation, causation, and association in data. Participants will learn to differentiate between these concepts and to recognize and interpret various types of correlations.

By the end of the session, participants should be able to distinguish between correlation and causation, recognize the impact of confounding variables on associations, evaluate correlation reliability, and understand the significance of correlation results.

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Whether you're on the path to graduate school, working in academia, or venturing into the academic job market, our online workshop will help you compose a compelling cover letter.