Other resources about lm-glm-glmm-intro HOT 22 OPEN

Interactive Visualising mixed effect models (random intercepts, slopes) http://mfviz.com/hierarchical-models/

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• Great tutorial on mixed models by M. Clark: https://m-clark.github.io/mixed-models-with-R/

• Good interactive d3 tutorial on binary predictive models: http://mfviz.com/binary-predictions/

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How changing point values changes estimated model: https://github.com/Timag/DraggableRegressionPoints

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Elementary Statistical Modeling forApplied Biostatistics

https://www.middleprofessor.com/files/applied-biostatistics_bookdown/_book/Walker-elementary-statistical-modeling-draft.pdf
https://www.middleprofessor.com/files/applied-biostatistics_bookdown/_book/index.html

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Great advice for teaching statistics: https://github.com/mine-cetinkaya-rundel/preparing-to-teach

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O. Gimenez Master course https://github.com/oliviergimenez/statistics-for-ecologists-Master-courses

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Carsten Dormann's book: Environmental Data Analysis https://www.springer.com/gp/book/9783030550196

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Nice book (with R companion) from R. Poldrack https://statsthinking21.org/

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Fantastic book covering GLM & GLMM: https://bookdown.org/roback/bookdown-BeyondMLR/

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Another book: https://bookdown.org/egarpor/PM-UC3M/

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Intro to Modern Statistics book https://github.com/OpenIntroStat/ims

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These workshops are very good:
https://github.com/QCBSRworkshops/workshop04
https://github.com/QCBSRworkshops/workshop06
https://github.com/QCBSRworkshops/workshop07

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OpenIntro stats: https://www.openintro.org/

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shiny apps:
https://github.com/EducationShinyAppTeam/BOAST
https://github.com/gastonstat/shiny-introstats
https://github.com/rsquaredacademy/xplorerr
https://github.com/jodeleeuw/shiny-stats

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https://github.com/squid-group/squid

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David Warton's book: https://link.springer.com/book/10.1007/978-3-030-88443-7

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A guide to modeling outcomes that have lots of zeros with Bayesian hurdle lognormal and hurdle Gaussian regression models https://www.andrewheiss.com/blog/2022/05/09/hurdle-lognormal-gaussian-brms/

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A guide to figuring out what the heck marginal effects, marginal slopes, average marginal effects, marginal effects at the mean, and all these other marginal things are https://www.andrewheiss.com/blog/2022/05/20/marginalia/

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Interactive tutorials https://mlu-explain.github.io/

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Great GLM explanations here https://argoshare.is.ed.ac.uk/healthyr_book/

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Bert van der Veen's GLM workshop https://github.com/R-tutorials/GLM-workshop

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Models demystified book (Michael Clark): https://m-clark.github.io/book-of-models/

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