Add links to external material

[lindeloev]

Following the tweet, I have been made aware of many excellent ressources. This issue just serves to collect them before I add them somewhere.

https://www.middleprofessor.com/files/applied-biostatistics_bookdown/_book/ looks like a solid intro to linear modeling equivalent to the stats 101 models. Downsides: there is little visualization, and no mention of non-parametric (i think?), and a lot more sampling theory. Check if there are worked examples.

https://siminab.github.io/2018/01/10/everything-in-statistical-modeling-can-be-seen-as-a-regression/ contains the basics, but likely too superficial.

https://www.ncbi.nlm.nih.gov/pubmed/20063905 looks like an excellent academic discussion of rote learning vs. modeling.

[lindeloev]

Gelman: https://statmodeling.stat.columbia.edu/2019/03/28/understanding-how-anova-relates-to-regression/ and links to DBA and other posts.

[lindeloev]

Probabilistic Index Models as a general framework for "Non-parametric" tests: https://www.tandfonline.com/doi/abs/10.1080/01621459.2015.1016226

[lindeloev]

Nice one by Mattan Ben-Shachar: https://shouldbewriting.netlify.com/posts/2018-08-30-linear-regression-assumptions/

[vasili111]

I think this is nice link to add somewere: https://rpsystats.com/ . This book is free and combines common traditional statistics into the general linear model (GLM).