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You are here		calogica.com Multi-level Modeling in RStan and brms (and the Mysteries of Log-Odds) \| Calogica.com
\|	\|	juliasilge.com Modeling salary and gender in the tech industry \| Julia Silge A data science blog	1.5 parsecs Travel
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\|	\|	statsandr.com Multiple linear regression made simple - Stats and R Learn how to run multiple and simple linear regression in R, how to interpret the results and how to verify the conditions of application	3.1 parsecs Travel
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\|	\|	twiecki.io whilemy_mcmc:gently(samples) - The Best Of Both Worlds: Hierarchical Linear Regression in PyMC [AI summary] This technical blog post explains the advantages of hierarchical Bayesian modeling over non-hierarchical approaches using a case study of predicting radon levels across different US counties with the PyMC3 library.	2.0 parsecs Travel
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\|	\|	stephenmalina.com Linear Algebra Done Right - Chapter 5 - Stephen Malina Selected Exercises # 5.A # 12. Define $ T \in \mathcal L(\mathcal P_4(\mathbf{R})) $ by $$ (Tp)(x) = xp'(x) $$ for all $ x \in \mathbf{R} $. Find all eigenvalues and eigenvectors of $ T $. Observe that, if $ p = a_0 + a_1 x + a_2 x^2 + a_3 x^3 + a_4 x^4 $, then $$ x p'(x) = a_1 x + 2 a_2 x^2 + 3 a_3 x^3 + 4 a_4 x^4.	26.8 parsecs Travel
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