For Today’s Graduate, Just One Word: Statistics
nytimes.com
nytimes.com
http://ask.metafilter.com/105045/Best-way-to-relearn-statist...
He even mentioned just about every topic we covered in my stats/probability class. There's a book list at the end.
Applied Statistics for Engineers and Scientists , 2nd Edition, J. Devore and N. Farnum, Duxbury Press, Thomson Publishers[3]
Hope this helps.
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[1] http://www.maths.unsw.edu.au/students/current/homepages/math...
[2] Set text and course overview :: http://www.maths.unsw.edu.au/students/current/homepages/outl...
[3] http://www.amazon.com/Applied-Statistics-Engineers-Scientist...
http://www.amazon.com/Introductory-Statistics-Second-Sheldon...
That said, here's what has sort of worked for me:
- Cartoon Guide to Statistics by Larry Gonick
- Fundamentals of Applied Probability Theory by Al Drake: out of print, and mostly about probability. However, the stats intro at the end is the clearest one I've ever read. Originally recommended to me by Philip Greenspun.
- Introductory Statistics with R by Peter Dalgaard. I'm assuming if you are posting to Hacker News you probably are coming from a programming background. This book does exactly what the title says, shows you how to apply introductory statistics by programming. It's heavier on R than stats.
Many people working with statistics in their employment have never grappled with the issues of adequate descriptive statistics or reasonable inference. Besides the recommendations I just posted above,
http://statland.org/MyPapers/MAAFIXED.PDF
http://repositories.cdlib.org/cgi/viewcontent.cgi?article=10...
see
Statistics: A Guide to the Unknown 4th edition
http://www.amazon.com/Statistics-Guide-Roxy-Peck/dp/05343728...
for articles with examples of what statistics is really all about.
The Cartoon Guide to Statistics is an excellent way to go from zero to a good overview of the basics with a minimum of hard math. After that, if you're mostly interested in applying basic techniques to your own stuff, you want a good undergrad textbook. I don't have any good recommendations here, unfortunately. If you have a good math background (or are motivated to get it) and you want to keep going, Statistical Models; Theory and Practice by David A. Freedman (http://www.amazon.com/review/R2XUNM92KYU7BB) has the math, the philosophy, the hands on analysis of studies, and the exercises to put you in a better position to evaluate statistical research than some people who produce it.
This is usually the process one needs to follow, albeit with some intermediate steps switched out for others here and there.
If you want to learn statistics, it's probably better to start at the beginning (_Cartoon Guide to Statistics_, O'Reilly's new _Head First Statistics_, or Huff's _How to Lie With Statistics_) than to leap headlong into a huge, mostly intractable problem and pagefault in knowledge at each point you come across something you don't know how to do.
Don't tell Google that it's slogan ("Organize the world's information") is biting off more than it could chew.
Still, there is a place for an introductory book if you are truly coming from minimal knowledge of the subject.
This is true, but I think in the original poster's case it could be much more easily and reliably be accomplished by continually giving him problems that are within (or more ideally, just outside) his circle of competence, rather than a problem like "predict stock prices" which isn't in any human being's circle of competence. Moreover, with the latter problem, he'll get virtually no feedback as to whether his answer was correct, because a correct answer for "use statistics to predict stock prices" doesn't exist. Odds are that if he follows that path he'll quit before making any progress, or at the least he won't be able to close the feedback loop that is so vital to gaining expertise.
I think that if he's starting from ground zero and wanting to learn statistics, he'd be much better served by sitting down with The Cartoon Guide to Statistics and a deck of cards and set of dice first. He can work his way up to conquering the stock market :)
Don't tell Google that it's slogan ("Organize the world's information") is biting off more than it could chew.
I think that's apples and oranges - he's trying to learn statistics, not trying to convince potential clients or investors that he's already an expert. I don't think it would be harmful at all for him to have a lofty, far-out goal like "predict stock prices" to aim toward, but I do think that if he starts out trying to learn statistics by typing "statistical stock prediction methods" into Google, he will burn out rather quickly. Pagefaulting in knowledge when you need it is probably optimal for something where you just want to make sure your knowledge is passable, but if he wants to truly know his domain, he's gotta get out the marbles and urns. :)
They came up with that slogan well after they were already experts in search, and probably after Google had been written and they'd formed a company. To hear Larry tell it, Google started with the question of "What if we could download all of the web and just keep all the links?" And then Terry Winograd encouraged them, and they realized that the link structure of the web was a lot like the citation graph of academic publications. And then they realized they could build a kick-ass search engine by using that to rank page relevance. And then when nobody would license it, they formed a company.
The way to build massive world-changing systems isn't to start with massive world-changing ideas. It's to start with interesting, challenging, but tractable problems, and then see where they lead you.
The other problem with this approach is that while you may get depth, there is very little breadth. I find that when learning something for the first time, I want to go wide and shallow. Then once I have a basic understanding I can figure out where I want to learn deeper.
OTOH, maybe you just learn things differently from me!
I just gave my friend (a PhD in Operations Research) his basic Statistical Theory book back. I also wanted to get a better understanding of statistics, but that stuff was putting me to sleep too easily :-(
"Advice to Mathematics Teachers on Evaluating Introductory Statistics Textbooks"
http://statland.org/MyPapers/MAAFIXED.PDF
"The Introductory Statistics Course: A Ptolemaic Curriculum?"
http://repositories.cdlib.org/cgi/viewcontent.cgi?article=10...
Here's a list of the best beginning textbooks:
Step #1 is going to be getting the foundations so that you can quickly digest the meaning and purpose of things like machine learning. Fortunately, statistics can be largely summarized as "the science/math/art of explaining variance".
Study what variance is and means and you'll dive through probability, distributions, modeling, inference, prediction, parametrization, simulation, and all of those fun topics while keeping an understanding for why they exist.
Finally, I'd highly suggest taking a look at Tufte's work because once you understand variance, you've still got to explain it.
Here's a link to some of his talks on the subject: http://thenumerati.net/index.cfm?catID=4
There's an "aren't you shocked that they're gathering all of this data?!" tone that gets old as the book goes on, but it's generally a good read. He even describes some of the major algorithms (support vector machines and clustering) in layman's terms.
Robert Tibsharani provides the following comparative glossary for machine learning and statistics: http://anyall.org/blog/2008/12/statistics-vs-machine-learnin...
Glossary
Machine learning Statistics
network, graphs model
weights parameters
learning fitting
generalization test set performance
supervised learning regression/classification
unsupervised learning density estimation, clustering
large grant = $1,000,000 large grant= $50,000
nice place to have a meeting: nice place to have a meeting:
Snowbird, Utah, French Alps Las Vegas in AugustThese books have good indexes leading to statistics issues in particular fields of research or applications.
Of course, should add the obligatory remark: Lies, Damn Lies and Statistics.
"Statistics are like a bikini. What they reveal is suggestive, but what they conceal is vital."