The Royal Statistical Society's Examination Reading List

http://www.rss.org.uk/site/cms/contentviewarticle.asp?article=653

by elchief • 12 years ago

59 10 12 years ago

rss.org.uk

woopdy• 12 years ago

Someone say SOMETHING. This is big news.

elchief2• 12 years ago

I'm planning to take their Chartered Statistician designation tests. Looks like they are every May.

squidbot• 12 years ago

I'm busy preparing for my Royal Lies and Damn Lies Society exams. Will look at this next.

ronaldx• 12 years ago

Is it wrong that I now want to do these exams for fun?

tonetheman• 12 years ago

While this is an interesting list, I looked at a few of the books and the prices are out of this world high. They need this same list for people who do not have a crap ton of money. :)

elchief2• 12 years ago

You might be able to get a community-access library card at nearest university or college.

https://www.worldcat.org/ will show you nearest library where book is available.

phaus• 12 years ago

I was able to find every book on the first page of the ordinary certificate list for about 300 total. Most of them could be bought for a price between 1-30 dollars. Only a couple were expensive even when purchased used.

lotsofmangos• 12 years ago

Just take a simple random sample from the set.

ivan_ah• 12 years ago

In the super-low-price range, there is the OpenIntro Stats book[1] which costs < $10 in print. I haven't finished reading it, but so far it's been a good intro book for the material covered in a first-years stats---we used a different book in our class, but the material covered is the same.

[1] http://www.openintro.org/stat/

mturmon• 12 years ago

This is a pretty crusty and old-fashioned list. Within the sub-areas (e.g., time series), it is also duplicative. I don't think this is a good starting point for the HN readership.

I think that starting out with a machine learning (ML) text would be more helpful to someone who's new to the area of modeling and decision-making under uncertainty.

If you're already familiar with the ML perspective, and want a more conventional (but not stale) stats approach, "All of Statistics" (Wasserman), or Casella and Berger, would be better choices.

If you're familiar with the basic stats outlook, then there are better sources for many of the specific modules listed, in addition to (probably) a need for RSS to re-examine and diversify the modules offered. Old-fashioned-ness has been an issue for the discipline (or else there might not be a thing called Machine Learning, it would be part of Statistics proper).

The Royal Statistical Society's Examination Reading List

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