During modern general election cycles, information to forecast the electoral outcome is plentiful. So-called fundamentals like economic growth provide information early in the cycle. Trial-heat polls become informative closer to Election Day. Our model builds on (Linzer, 2013) and is implemented in Stan (Team, 2020). We improve on the estimation…

Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the…

We consider three examples from our own teaching in which much was learned by critically examining examples from books. Even influential and well-regarded books can have examples where more can be learned with a small amount of additional effort. (Contains 3 figures.)

Presents several classroom demonstrations, based on well-known statistical ideas, that have sparked student involvement in introductory undergraduate courses in probability and statistics. Contains descriptions of 10 demonstrations. (SLD)