I should start this with a disclaimer, *ie* that I’m not really claiming any “success” with this post. But I find it quite interesting that the estimations I produced with this very, very simple model turned out to be quite good.

The idea was to use the existing polls (that was a few days ago, even before the super-storm), which had been collated and presented in terms of an estimation of the proportion of voters for either party, together with some measure of uncertainty. Based on these, I constructed informative prior distributions, which I have then propagated to estimate the election results.

As it turns out, according to the projections of the final results, the prediction was accurate, as the following graph shows: the dots and lines indicate the average prediction and a 50% (darker) and 90% (lighter) credible intervals; the crosses are the observed proportions for Obama.

In all states, the prediction was “correct” (in the sense that the right “colour” was estimated). In some cases, the observed results were a bit more extreme than the observed ones, *eg* in Washington (WA) the actual proportion of votes for Obama is substantially larger than predicted $-$ but this has no real consequences on the final estimation of the election results as WA was already estimated to be a safe democratic state; and this is true for all other under/over estimated cases.

My final estimation was that, based on the model, I was expecting Obama to get 304 EVs. At the moment, the Guardian is reporting 303 $-$ so pretty good!

But, as I said, this is really not to brag, but rather to reflect on the point that while the race was certainly close, it probably wasn’t as close as the media made it. Famously, Nate Silver gave Obama a probability of winning the election exceeding 80%, a prediction which has given rise to some controversy $-$ but he was spot on.

Also, I think it’s interesting that, at least in this case, the polls were quite representative of the “true” population and what most people said they would do was in fact very similar to what most people actually did.

*Related*

To

**leave a comment** for the author, please follow the link and comment on their blog:

** Gianluca Baio's blog**.

R-bloggers.com offers

**daily e-mail updates** about

R news and

tutorials on topics such as:

Data science,

Big Data, R jobs, visualization (

ggplot2,

Boxplots,

maps,

animation), programming (

RStudio,

Sweave,

LaTeX,

SQL,

Eclipse,

git,

hadoop,

Web Scraping) statistics (

regression,

PCA,

time series,

trading) and more...

If you got this far, why not

__subscribe for updates__ from the site? Choose your flavor:

e-mail,

twitter,

RSS, or

facebook...