<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Math5806 on Aayush Bajaj's Augmenting Infrastructure</title><link>https://abaj.ai/tags/math5806/</link><description>Recent content in Math5806 on Aayush Bajaj's Augmenting Infrastructure</description><generator>Hugo</generator><language>en</language><copyright>© 2026 Aayush Bajaj</copyright><lastBuildDate>Mon, 31 Aug 2026 11:43:02 +1000</lastBuildDate><atom:link href="https://abaj.ai/tags/math5806/index.xml" rel="self" type="application/rss+xml"/><item><title>MATH5806 — Applied Regression Analysis at UNSW</title><link>https://abaj.ai/blog/pgrad-unsw/math5806/</link><pubDate>Mon, 31 Aug 2026 08:00:00 +1000</pubDate><guid>https://abaj.ai/blog/pgrad-unsw/math5806/</guid><description>&lt;p>I took MATH5806 in 2026 T2 as part of my &lt;a
 href="https://abaj.ai/blog/pgrad-unsw/"
 
 
>Masters of Statistics&lt;/a>. The name
undersells it: &amp;ldquo;applied regression analysis&amp;rdquo; here means &lt;em>generalised linear
models&lt;/em>, done properly &amp;mdash; exponential families, link functions, iteratively
reweighted least squares, deviance &amp;mdash; with ordinary linear regression as the
special case rather than the destination, and splines, Poisson and binomial
regression along the way. Everything runs in R. This page is the admin,
the textbook, and my notes, in one place.&lt;/p></description></item></channel></rss>